Computers Math

Researchers in UC Santa Barbara professor Yasamin Mostofi’s lab have proposed a new foundation that can enable high-quality imaging of still objects with only WiFi signals. Their method uses the Geometrical Theory of Diffraction and the corresponding Keller cones to trace edges of the objects. The technique has also enabled, for the first time, imaging, or reading, the English alphabet through walls with WiFi, a task deemed too difficult for WiFi due to the complex details of the letters.
“Imaging still scenery with WiFi is considerably challenging due to the lack of motion,” said Mostofi, a professor of electrical and computer engineering. “We have then taken a completely different approach to tackle this challenging problem by focusing on tracing the edges of the objects instead.” The proposed methodology and experimental results appeared in the Proceedings of the 2023 IEEE National Conference on Radar (RadarConf) on June 21, 2023.
This innovation builds on previous work in the Mostofi Lab, which since 2009 has pioneered sensing with everyday radio frequency signals such as WiFi for several different applications, including crowd analytics, person identification, smart health and smart spaces.
“When a given wave is incident on an edge point, a cone of outgoing rays emerges according to the Keller’s Geometrical Theory of Diffraction (GTD), referred to as a Keller cone,” Mostofi explained. The researchers note that this interaction is not limited to visibly sharp edges but applies to a broader set of surfaces with a small enough curvature.
“Depending on the edge orientation, the cone then leaves different footprints (i.e., conic sections) on a given receiver grid. We then develop a mathematical framework that uses these conic footprints as signatures to infer the orientation of the edges, thus creating an edge map of the scene,” Mostofi continued.
More specifically, the team proposed a Keller cone-based imaging projection kernel. This kernel is implicitly a function of the edge orientations, a relationship that is then exploited to infer the existence/orientation of the edges via hypothesis testing over a small set of possible edge orientations. In other words, if existence of an edge is determined, the edge orientation that best matches the resulting Keller cone-based signature is chosen for a given point that they are interested in imaging.
“Edges of real-life objects have local dependencies,” said Anurag Pallaprolu, the lead Ph.D. student on the project. “Thus, once we find the high-confidence edge points via the proposed imaging kernel, we then propagate their information to the rest of the points using Bayesian information propagation. This step can further help improve the image, since some of the edges may be in a blind region, or can be overpowered by other edges that are closer to the transmitters.” Finally, once an image is formed, the researchers can further improve the image by using image completion tools from the area of vision. More

Using laser light, researchers have developed the most robust method currently known to control individual qubits made of the chemical element barium. The ability to reliably control a qubit is an important achievement for realizing future functional quantum computers.
This new method, developed at the University of Waterloo’s Institute for Quantum Computing (IQC), uses a small glass waveguide to separate laser beams and focus them four microns apart, about four-hundredths of the width of a single human hair. The precision and extent to which each focused laser beam on its target qubit can be controlled in parallel is unmatched by previous research.
“Our design limits the amount of crosstalk-the amount of light falling on neighbouring ions-to the very small relative intensity of 0.01 per cent, which is among the best in the quantum community,” said Dr. K. Rajibul Islam, a professor at IQC and Waterloo’s Department of Physics and Astronomy. “Unlike previous methods to create agile controls over individual ions, the fibre-based modulators do not affect each other.
“This means we can talk to any ion without affecting its neighbours while also retaining the capability to control each individual ion to the maximum possible extent. This is the most flexible ion qubit control system with this high precision that we know of anywhere, in both academia and industry.”
The researchers targeted barium ions, which are becoming increasingly popular in the field of trapped ion quantum computation. Barium ions have convenient energy states that can be used as the zero and one levels of a qubit and be manipulated with visible green light, unlike the higher energy ultraviolet light needed for other atom types for the same manipulation. This allows the researchers to use commercially available optical technologies that are not available for ultraviolet wavelengths.
The researchers created a waveguide chip that divides a single laser beam into 16 different channels of light. Each channel is then directed into individual optical fibre-based modulators which independently provide agile control over each laser beam’s intensity, frequency, and phase. The laser beams are then focused down to their small spacing using a series of optical lenses similar to a telescope. The researchers confirmed each laser beam’s focus and control by measuring them with precise camera sensors.
“This work is part of our effort at the University of Waterloo to build barium ion quantum processors using atomic systems,” said Dr. Crystal Senko, Islam’s co-principal investigator and a faculty member at IQC and Waterloo’s Department of Physics and Astronomy. “We use ions because they are identical, nature-made qubits, so we don’t need to fabricate them. Our task is to find ways to control them.”
The new waveguide method demonstrates a simple and precise method of control, showing promise for manipulating ions to encode and process quantum data and for implementation in quantum simulation and computing. More

Researchers of Johannes Gutenberg University Mainz and the University of Konstanz in Germany as well as of Tohoku University in Japan have been able to increase the diffusion of magnetic whirls, so called skyrmions, by a factor of ten.
In today’s world, our lives are unimaginable without computers. Up until now, these devices process information using primarily electrons as charge carriers, with the components themselves heating up significantly in the process. Active cooling is thus necessary, which comes with high energy costs. Spintronics aims to solve this problem: Instead of utilizing the electron flow for information processing, it relies on their spin or their intrinsic angular momentum. This approach is expected to have a positive impact on the size, speed, and sustainability of computers or specific components.
Magnetic whirls store and process information
Science often does not simply consider the spin of an individual electron, but rather magnetic whirls composed of numerous spins. These whirls called skyrmions emerge in magnetic metallic thin layers and can be considered as two-dimensional quasi-particles. On the one hand, the whirls can be deliberately moved by applying a small electric current to the thin layers; on the other hand, they move randomly and extremely efficiently due to diffusion. The feasibility of creating a functional computer based on skyrmions was demonstrated by a team of researchers from Johannes Gutenberg University Mainz (JGU), led by Professor Dr. Mathias Kläui, using an initial prototype. This prototype consisted of thin, stacked metallic layers, some only a few atomic layers thick.
Energy efficiency: Tenfold increase in whirl diffusion
In collaboration with the University of Konstanz and Tohoku University in Japan, researchers of Mainz University have now achieved another step towards spin-based, unconventional computing: They were able to increase the diffusion of skyrmions by a factor of about ten using synthetic antiferromagnets, which drastically reduces the energy consumption and increases the speed of such a potential computer. “The reduction of energy usage in electronic devices is one of the biggest challenges in fundamental research,” emphasized Professor Dr. Ulrich Nowak, who led the theoretical part of the project in Konstanz.
But what is an antiferromagnet and what is it used for? Normal ferromagnets consist of many small spins, all coupled together to point in the same direction, thereby creating a large magnetic moment. In antiferromagnets, the spins are aligned alternatingly antiparallel, i.e., a spin and its direct neighbors point in the opposite direction. As a result, there is no net magnetic moment, even though the spins remain antiferromagnetically well-ordered. Antiferromagnets have significant advantages, such as three magnitudes of faster dynamics for switching, better stability, and the potential for higher storage densities. These properties are intensively studied in multiple research projects. More

Using artificial intelligence applications to help craft a message to a friend is not a good idea — at least if your friend finds out about the use of AI, a new study suggests.
Researchers found that people in the study perceived that a fictional friend who used AI assistance to write them a message didn’t put forth as much effort as a friend who wrote a message themselves.
That perception may be understandable, but the effect goes beyond the message itself, said Bingjie Liu, lead author of the study and assistant professor of communication at The Ohio State University.
“After they get an AI-assisted message, people feel less satisfied with their relationship with their friend and feel more uncertain about where they stand,” Liu said.
But to be fair to AI, it wasn’t just the use of technology that turned people off. The study also found negative effects when people learned their friend got help from another person to write a message.
“People want their partners or friends to put forth the effort to come up with their own message without help — from AI or other people,” Liu said.
The study was published online recently in the Journal of Social and Personal Relationships. More

Researchers who created a soft robot that could navigate simple mazes without human or computer direction have now built on that work, creating a “brainless” soft robot that can navigate more complex and dynamic environments.
“In our earlier work, we demonstrated that our soft robot was able to twist and turn its way through a very simple obstacle course,” says Jie Yin, co-corresponding author of a paper on the work and an associate professor of mechanical and aerospace engineering at North Carolina State University. “However, it was unable to turn unless it encountered an obstacle. In practical terms this meant that the robot could sometimes get stuck, bouncing back and forth between parallel obstacles.
“We’ve developed a new soft robot that is capable of turning on its own, allowing it to make its way through twisty mazes, even negotiating its way around moving obstacles. And it’s all done using physical intelligence, rather than being guided by a computer.”
Physical intelligence refers to dynamic objects — like soft robots — whose behavior is governed by their structural design and the materials they are made of, rather than being directed by a computer or human intervention.
As with the earlier version, the new soft robots are made of ribbon-like liquid crystal elastomers. When the robots are placed on a surface that is at least 55 degrees Celsius (131 degrees Fahrenheit), which is hotter than the ambient air, the portion of the ribbon touching the surface contracts, while the portion of the ribbon exposed to the air does not. This induces a rolling motion; the warmer the surface, the faster the robot rolls.
However, while the previous version of the soft robot had a symmetrical design, the new robot has two distinct halves. One half of the robot is shaped like a twisted ribbon that extends in a straight line, while the other half is shaped like a more tightly twisted ribbon that also twists around itself like a spiral staircase.
This asymmetrical design means that one end of the robot exerts more force on the ground than the other end. Think of a plastic cup that has a mouth wider than its base. If you roll it across the table, it doesn’t roll in a straight line — it makes an arc as it travels across the table. That’s due to its asymmetrical shape. More

Computer scientists and mathematicians working in complex systems at the University of Sydney and the Max Planck Institute for Mathematics in the Sciences in Germany have developed new methods to describe what many of us take for granted — how easy, or hard, it can be to fall in and out of sync.
Synchronised phenomena are all around us, whether it is human clapping and dancing, or the way fireflies flash, or how our neurons and heart cells interact. However, it is something not fully understood in engineering and science.
Associate Professor Joseph Lizier, expert in complex systems at the University of Sydney, said: “We know the feeling of dancing in step to the ‘Nutbush’ in a crowd — or the awkward feeling when people lose time clapping to music. Similar processes occur in nature, and it is vital that we better understand how falling in and out of sync actually works.
“Being in sync in a system can be very good; you want your heart cells to all beat together rather than fibrillate. But being in sync can also be very bad; you don’t want your brain cells to all fire together in an epileptic seizure.”
Associate Professor Lizier and colleagues at the Max Planck Institute in Leipzig, Germany have published new research on synchronisation in the Proceedings of the National Academy of Sciences (PNAS).
The paper sets out the mathematics of how the network structure connecting a set of individual elements controls how well they can synchronise their activity. It is a critical insight into how these systems operate, because in most real-world systems, no one individual element controls all the others. And nor can any individual directly see and react to all the others: they are only connected through a network.
Associate Professor Lizier, from the Centre of Complex Systems and the School of Computer Science in the Faculty of Engineering, said: “Our results open new opportunities for designing network structures or interventions in networks. This could be super useful in stabilising electricity in power grids, vital for the transition to renewables, or to avoid neural synchronisation in the brain, which can trigger epilepsy.”
To understand how these systems work, the researchers studied what are known as “walks” through a network in a complex system. Walks are sequences of connected hops between individual elements or nodes in the network. More

Researchers at the Center for Functional Nanomaterials (CFN), a U.S. Department of Energy (DOE) Office of Science User Facility at DOE’s Brookhaven National Laboratory, and Northrop Grumman, a multinational aerospace and defense technology company, have found a way to maintain valley polarization at room temperature using novel materials and techniques. This discovery could lead to devices that store and process information in novel ways using this technology without the need to keep them at ultra-low temperatures. Their research was recently published in Nature Communications.
One of the paths being explored to achieve these devices is a relatively new field called “valleytronics.” A material’s electronic band structure — the range of energy levels in each atom’s electron configurations — can dip up or down. These peaks and troughs are known as “valleys.” Some materials have multiple valleys with the same energy. An electron in a system like this can occupy any one of these valleys, presenting a unique way to store and process information based on which valley the electron occupies. One challenge, however, has been the effort and expense of maintaining the low temperatures needed to keep valley polarization stable. Without this stability, devices would begin to lose information. In order to make a technology like this feasible for practical, affordable applications, experts would need to find a way to around this constraint.
Exploring 2D Landscapes for the Perfect Valleys
Transition metal dichalcogenides (TMDs) are interesting, layered materials that can be, at their thinnest, only few atoms thick. Each layer in the material consists of a two-dimensional (2D) sheet of transition metal atoms sandwiched between chalcogen atoms. While the metal and the chalcogen are strongly bound by covalent bonds in a layer, adjacent layers are only weakly bound by van der Waal’s interactions. The weak bonds that hold these layers together enable TMDs to be exfoliated down to a monolayer that’s only one “molecule” thick. These are often referred to as 2D materials.
The team at CFN synthesized single crystals of chiral lead halide perovskites (R/S-NEAPbI3). Chirality describes a set of objects, like molecules, that are a mirror image of each other but can’t be superimposed. It is derived from the Greek word for “hands,” a perfect example of chirality. The two shapes are identical, but if you put one hand on top of the other, they will not align. This asymmetry is important for controlling valley polarization.
Flakes of this material, roughly 500 nanometers thick or five-thousandths the thickness of a human hair, were layered onto a monolayer of molybdenum disulfide (MoS2) TMD to create what is known as a heterostructure. By combining different 2D materials with properties that affect the charge transfer at the interface between the two materials, these heterostructures open up a world of possibility.
After creating and characterizing this heterostructure, the team was eager to see how it behaved. More

An artificial intelligence tool developed by researchers at the University of Rochester can help people with Parkinson’s disease remotely assess the severity of their symptoms within minutes. A study in npj Digital Medicine describes the new tool, which has users tap their fingers 10 times in front of a webcam to assess motor performance on a scale of 0-4.
Doctors often have patients perform simple motor tasks to assess movement disorders and rate the severity using guidelines such as the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). The AI model provides a rapid assessment using the MDS-UPDRS guidelines, automatically generating computational metrics such as speed, amplitude, frequency, and period that are interpretable, standardized, repeatable, and consistent with medical guidebooks. It uses those attributes to classify the severity of tremors.
The finger-tapping task was performed by 250 global participants with Parkinson’s disease and the AI system’s ratings were compared with those by three neurologists and three primary care physicians. While expert neurologists performed slightly better than the AI model, the AI model outperformed the primary care physicians with UPDRS certification.
The AI-based Parkinson’s disease severity test generates computational metrics such as speed, amplitude, frequency, and period, and uses those attributes to classify the severity of tremors. 
“These findings could have huge implications for patients who have difficulty gaining access to neurologists, getting appointments, and traveling to the hospital,” says Ehsan Hoque, an associate professor in Rochester’s Department of Computer Science and co-director of the Rochester Human-Computer Interaction Laboratory. “It’s an example of how AI is being gradually introduced into health care to serve people outside of the clinic and improve health equity and access.”
The study was led by Md. Saiful Islam, a Google PhD fellow and a graduate student in computer science advised by Hoque. The team of computer scientists collaborated with several members of the Medical Center’s Department of Neurology, including associate professor Jamie Adams; Ray Dorsey, the David M. Levy Professor of Neurology; and associate professor Ruth Schneider.
The researchers say their method can be applied to other motor tasks, which opens the door to evaluating other types of movement disorders such as ataxia and Huntington’s disease. The new Parkinson’s disease assessment is available online, though the researchers caution that it reflects an emerging technology and at this early stage should not be considered, on its own and without a physician’s input, as a definitive measure of the presence or severity of the disease. More