Computers Math

Researchers have leveraged deep learning techniques to enhance the image quality of a metalens camera. The new approach uses artificial intelligence to turn low-quality images into high-quality ones, which could make these cameras viable for a multitude of imaging tasks including intricate microscopy applications and mobile devices.
Metalenses are ultrathin optical devices — often just a fraction of a millimeter thick — that use nanostructures to manipulate light. Although their small size could potentially enable extremely compact and lightweight cameras without traditional optical lenses, it has been difficult to achieve the necessary image quality with these optical components.
“Our technology allows our metalens-based devices to overcome the limitations of image quality,” said research team leader Ji Chen from Southeast University in China. “This advance will play an important role in the future development of highly portable consumer imaging electronics and can also be used in specialized imaging applications such as microscopy.”
In Optica Publishing Group journal Optics Letters, the researchers describe how they used a type of machine learning known as a multi-scale convolutional neural network to improve resolution, contrast and distortion in images from a small camera — about 3 cm × 3 cm × 0.5 cm — they created by directly integrating a metalens onto a CMOS imaging chip.
“Metalens-integrated cameras can be directly incorporated into the imaging modules of smartphones, where they could replace the traditional refractive bulk lenses,” said Chen. “They could also be used in devices such as drones, where the small size and lightweight camera would ensure imaging quality without compromising the drone’s mobility.”
Enhancing image quality
The camera used in the new work was previously developed by the researchers and uses a metalens with 1000-nm tall cylindrical silicon nitride nano-posts. The metalens focuses light directly onto a CMOS imaging sensor without requiring any other optical elements. Although this design created a very small camera the compact architecture limited the image quality. Thus, the researchers decided to see if machine learning could be used to improve the images.

Deep learning is a type of machine learning that uses artificial neural networks with multiple layers to automatically learn features from data and make complex decisions or predictions. The researchers applied this approach by using a convolution imaging model to generate a large number of high- and low-quality image pairs. These image pairs were used to train a multi-scale convolutional neural network so that it could recognize the characteristics of each type of image and use that to turn low-quality images into high-quality images.
“A key part of this work was developing a way to generate the large amount of training data needed for the neural network learning process,” said Chen. “Once trained, a low-quality image can be sent from the device to into the neural network for processing, and high-quality imaging results are obtained immediately.”
Applying the neural network
To validate the new deep learning technique, the researchers used it on 100 test images. They analyzed two commonly used image processing metrics: the peak signal-to-noise ratio and the structural similarity index. They found that the images processed by the neural network exhibited a significant improvement in both metrics. They also showed that the approach could rapidly generate high-quality imaging data that closely resembled what was captured directly through experimentation.
The researchers are now designing metalenses with complex functionalities — such as color or wide-angle imaging — and developing neural network methods for enhancing the imaging quality of these advanced metalenses. To make this technology practical for commercial application would require new assembly techniques for integrating metalenses into smartphone imaging modules and image quality enhancement software designed specifically for mobile phones.
“Ultra-lightweight and ultra-thin metalenses represent a revolutionary technology for future imaging and detection,” said Chen. “Leveraging deep learning techniques to optimize metalens performance marks a pivotal developmental trajectory. We foresee machine learning as a vital trend in advancing photonics research.” More

It’s one thing to dream up a quantum internet that could send hacker-proof information around the world via photons superimposed in different quantum states. It’s quite another to physically show it’s possible.
That’s exactly what Harvard physicists have done, using existing Boston-area telecommunication fiber, in a demonstration of the world’s longest fiber distance between two quantum memory nodes to date. Think of it as a simple, closed internet between point A and B, carrying a signal encoded not by classical bits like the existing internet, but by perfectly secure, individual particles of light.
The groundbreaking work, published in Nature, was led by Mikhail Lukin, the Joshua and Beth Friedman University Professor in the Department of Physics, in collaboration with Harvard professors Marko Lončar and Hongkun Park, who are all members of the Harvard Quantum Initiative, alongside researchers at Amazon Web Services.
The Harvard team established the practical makings of the first quantum internet by entangling two quantum memory nodes separated by optical fiber link deployed over a roughly 22-mile loop through Cambridge, Somerville, Watertown, and Boston. The two nodes were located a floor apart in Harvard’s Laboratory for Integrated Science and Engineering.
Quantum memory, analogous to classical computer memory, is an important component of an interconnected quantum computing future because it allows for complex network operations and information storage and retrieval. While other quantum networks have been created in the past, the Harvard team’s is the longest fiber network between devices that can store, process and move information.
Each node is a very small quantum computer, made out of a sliver of diamond that has a defect in its atomic structure called a silicon-vacancy center. Inside the diamond, carved structures smaller than a hundredth the width of a human hair enhance the interaction between the silicon-vacancy center and light.
The silicon-vacancy center contains two qubits, or bits of quantum information: one in the form of an electron spin used for communication, and the other in a longer-lived nuclear spin used as a memory qubit to store entanglement (the quantum-mechanical property that allows information to be perfectly correlated across any distance). Both spins are fully controllable with microwave pulses. These diamond devices — just a few millimeters square — are housed inside dilution refrigeration units that reach temperatures of -459 Fahrenheit.

Using silicon-vacancy centers as quantum memory devices for single photons has been a multi-year research program at Harvard. The technology solves a major problem in the theorized quantum internet: signal loss that can’t be boosted in traditional ways. A quantum network cannot use standard optical-fiber signal repeaters because copying of arbitrary quantum information is impossible — making the information secure, but also very hard to transport over long distances.
Silicon vacancy center-based network nodes can catch, store and entangle bits of quantum information while correcting for signal loss. After cooling the nodes to close to absolute zero, light is sent through the first node and, by nature of the silicon vacancy center’s atomic structure, becomes entangled with it.
“Since the light is already entangled with the first node, it can transfer this entanglement to the second node,” explained first author Can Knaut, a Kenneth C. Griffin Graduate School of Arts and Sciences student in Lukin’s lab. “We call this photon-mediated entanglement.”
Over the last several years, the researchers have leased optical fiber from a company in Boston to run their experiments, fitting their demonstration network on top of the existing fiber to indicate that creating a quantum internet with similar network lines would be possible.
“Showing that quantum network nodes can be entangled in the real-world environment of a very busy urban area, is an important step towards practical networking between quantum computers,” Lukin said.
A two-node quantum network is only the beginning. The researchers are working diligently to extend the performance of their network by adding nodes and experimenting with more networking protocols. More

Semiconductors are the foundation of all modern electronics. Now, researchers at Linköping University, Sweden, have developed a new method where organic semiconductors can become more conductive with the help of air as a dopant. The study, published in the journal Nature, is a significant step towards future cheap and sustainable organic semiconductors.
“We believe this method could significantly influence the way we dope organic semiconductors. All components are affordable, easily accessible, and potentially environmentally friendly, which is a prerequisite for future sustainable electronics,” says Simone Fabiano, associate professor at Linköping University.
Semiconductors based on conductive plastics instead of silicon have many potential applications. Among other things, organic semiconductors can be used in digital displays, solar cells, LEDs, sensors, implants, and for energy storage.
To enhance conductivity and modify semiconductor properties, so-called dopants are typically introduced. These additives facilitate the movement of electrical charges within the semiconductor material and can be tailored to induce positive (p-doping) or negative (n-doping) charges. The most common dopants used today are often either very reactive (unstable), expensive, challenging to manufacture, or all three.
Now, researchers at Linköping University have developed a doping method that can be performed at room temperature, where inefficient dopants such as oxygen are the primary dopant, and light activates the doping process.
“Our approach was inspired by nature, as it shares many analogies with photosynthesis, for example. In our method, light activates a photocatalyst, which then facilitates electron transfer from a typically inefficient dopant to the organic semiconductor material,” says Simone Fabiano.
The new method involves dipping the conductive plastic into a special salt solution — a photocatalyst — and then illuminating it with light for a short time. The duration of illumination determines the degree to which the material is doped. Afterwards, the solution is recovered for future use, leaving behind a p-doped conductive plastic in which the only consumed substance is oxygen in the air.

This is possible because the photocatalyst acts as an “electron shuttle,” taking electrons or donating them to material in the presence of sacrificial weak oxidants or reductants. This is common in chemistry but has not been used in organic electronics before.
“It’s also possible to combine p-doping and n-doping in the same reaction, which is quite unique. This simplifies the production of electronic devices, particularly those where both p-doped and n-doped semiconductors are required, such as thermoelectric generators. All parts can be manufactured at once and doped simultaneously instead of one by one, making the process more scalable,” says Simone Fabiano.
The doped organic semiconductor has better conductivity than traditional semiconductors, and the process can be scaled up. Simone Fabiano and his research group at the Laboratory of Organic Electronics showed earlier in 2024 how conductive plastics could be processed from environmentally friendly solvents like water; this is their next step.
“We are at the beginning of trying to fully understand the mechanism behind it and what other potential application areas exist. But it’s a very promising approach showing that photocatalytic doping is a new cornerstone in organic electronics,” says Simone Fabiano, a Wallenberg Academy Fellow. More

A new Northwestern Medicine study shows that technology alone can’t replace the human touch to produce meaningful weight loss in obesity treatment.
“Giving people technology alone for the initial phase of obesity treatment produces unacceptably worse weight loss than giving them treatment that combines technology with a human coach,” said corresponding study author Bonnie Spring, director of the Center for Behavior and Health and professor of preventive medicine at Northwestern University Feinberg School of Medicine.
The need for low cost but effective obesity treatments delivered by technology has become urgent as the ongoing obesity epidemic exacerbates burgeoning health care costs.
But current technology is not advanced enough to replace human coaches, Spring said.
In the new SMART study, people who initially only received technology without coach support were less likely to have a meaningful weight loss, considered to be at least 5% of body weight, compared to those who had a human coach at the start.
Investigators intensified treatment quickly (by adding resources after just two weeks) if a person showed less than optimal weight loss, but the weight loss disadvantage for those who began their weight loss effort without coach support persisted for six months, the study showed.
The study will be published May 14 in JAMA.

Eventually more advanced technology may be able to supplant human coaches, Spring said.
“At this stage, the average person still needs a human coach to achieve clinically meaningful weight loss goals because the tech isn’t sufficiently developed yet,” Spring said. “We may not be so far away from having an AI chat bot that can sub for a human, but we are not quite there yet. It’s within reach. The tech is developing really fast.”
Previous research showed that mobile health tools for tracking diet, exercise and weight increase engagement in behavioral obesity treatment. Before this new study, it wasn’t clear whether they produced clinically acceptable weight loss in the absence of support from a human coach.
Scientists are now trying to parse what human coaches do that makes them successful, and how AI can better imitate a human, not just in terms of content but in emotional tone and context awareness, Spring said.
Surprising results
“We predicted that starting treatment with technology alone would save money and reduce burden without undermining clinically beneficial weight loss, because treatment augmentation occurred so quickly once poor weight loss was detected,” Spring said. “That hypothesis was disproven, however.”
Drug and surgical interventions also are available for obesity but have some drawbacks. “They’re very expensive, convey medical risks and side effects and aren’t equitably accessible,” Spring said. Most people who begin taking a GLP-1 agonist stop taking the drug within a year against medical advice, she noted.

Many people can achieve clinically meaningful weight loss without antiobesity medications, bariatric surgery or even behavioral treatment, Spring said. In the SMART study, 25% of people who began treatment with technology alone achieved 5% weight loss after six months without anytreatment augmentation. (In fact, the team had to take back the study technologies after three months to recycle to new participants.)
An unsolved problem in obesity treatment is matching treatment type and intensity to individuals’ needs and preferences. “If we could just tell ahead of time who needs which treatment at what intensity, we might start to manage the obesity epidemic,” Spring said.
How the study worked
The SMART Weight Loss Management study was a randomized controlled trial that compared two different stepped care treatment approaches for adult obesity. Stepped care offers a way to spread treatment resources across more of the population in need. The treatment that uses the least resources but that will benefit some people is delivered first; then treatment is intensified only for those who show insufficient response. Half of participants in the SMART study began their weight loss treatment with technology alone. The other half began with gold standard treatment involving both technology and a human coach.
The technology used in the SMART trial was a Wireless Feedback System (an integrated app, Wi-Fi scale and Fitbit) that participants used to track and receive feedback about their diet, activity and weight.
Four-hundred adults between ages 18-60 with obesity were randomly assigned to begin three months of stepped care behavioral obesity treatment beginning with either the Wireless Feedback System (WFS) alone or the WFS plus telehealth coaching. Weight loss was measured after two, four and eight weeks of treatment, and treatment was intensified at the first sign of suboptimal weight loss (less than .5 pounds per week).
Treatment for both groups began with the same WFS tracking technology, but standard-of-care treatment also transmitted the participant’s digital data to a coach, who used it to provide behavioral coaching by telehealth. Those showing suboptimal weight loss in either group were re-randomized once to either of two levels of treatment intensification: modest (adding an inexpensive technology component — supportive messaging) or vigorous (adding both messaging plus a more costly traditional weight loss treatment component — coaching for those who hadn’t received it, meal replacement for those who’d already received coaching). More

Researchers from the University of Illinois Urbana-Champaign have recast diffusion in multicomponent alloys as a sum of individual contributions, called “kinosons.” Using machine learning to compute the statistical distribution of the individual contributions, they were able to model the alloy and calculate its diffusivity orders of magnitude more efficiently than computing whole trajectories. This work was recently published in the journal Physical Review Letters.
“We found a much more efficient way to calculate diffusion in solids, and at the same time, we learned more about the fundamental processes of diffusion in that same system,” says materials science & engineering professor Dallas Trinkle, who led this work, along with graduate student Soham Chattopadhyay.
Diffusion in solids is the process by which atoms move throughout a material. The production of steel, ions moving through a battery and the doping of semiconductor devices are all things that are controlled by diffusion.
Here, the team modeled diffusion in multicomponent alloys, which are metals composed of five different elements — manganese, cobalt, chromium, iron and nickel in this research — in equal amounts. These types of alloys are interesting because one way to make strong materials is to add different elements together like adding carbon and iron to make steel. Multicomponent alloys have unique properties, such as good mechanical behavior and stability at high temperatures, so it is important to understand how atoms diffuse in these materials.
To get a good look at diffusion, long timescales are needed since atoms randomly move around and, over time, their displacement from the starting point will grow. “If somebody tries to simulate the diffusion, it’s a pain because you have to run the simulation for a very long time to get the full picture,” Trinkle says. “That really limits a lot of the ways that we can study diffusion. More accurate methods for calculating transition rates often can’t be used because you wouldn’t be able to do enough steps of a simulation to get the longtime trajectory and get a reasonable value of diffusion.”
An atom might jump to the left but then it might jump back to the right. In that case, the atom hasn’t moved anywhere. Now, say it jumps left, then 1000 other things happen, then it jumps back to the right. That’s the same effect. Trinkle says, “We call that correlation because at one point the atom made one jump and then later it undid that jump. That’s what makes diffusion complicated. When we look at how machine learning is solving the problem, what it’s really doing is it’s changing the problem into one where there aren’t any of these correlated jumps.”
Therefore, any jump that an atom makes contributes to diffusion and the problem becomes a lot easier to solve. “We call those jumps kinosons, for little moves,” Trinkle says. “We’ve shown that you can extract the distribution of those, the probability of seeing a kinoson of a certain magnitude, and add them all up to get the true diffusivity. On top of that you can tell how different elements are diffusing in a solid.”
Another advantage of modeling diffusion using kinosons and machine learning is that it is significantly faster than calculating long-timescale, whole trajectories. Trinkle says that with this method, simulations can be done 100 times faster than it would take with the normal methods.
“I think this method is really going to change the way we think about diffusion,” he says. “It’s a different way to look at the problem and I hope that in the next 10 years, this will be the standard way of looking at diffusion. To me, one of the exciting things is not just that it works faster, but you also learn more about what’s happening in the system.” More

Cinematography techniques can significantly increase user engagement with virtual environments and, in particular, the aesthetic appeal of what users see in virtual reality.
This was the result of a recent study conducted by computer scientists at the University of Helsinki. The results were published in May at the ACM Conference on Human Factors in Computing Systems (CHI).
The team aimed to investigate how principles of composition and continuity, commonly used in filmmaking, could be utilized to enhance navigation around virtual environments.
Composition refers to how the elements in a scene are oriented with respect to the viewer, whereas continuity is about how camera positions between shots can help viewers to understand spatial relationships between elements in the scene.
“Using these ideas, we developed a new teleportation method for exploring virtual environments that subtly repositions and reorientates the user’s viewpoint after teleportation to better frame the contents of the scene,” says Alan Medlar, University Researcher in computer science at the University of Helsinki.
The images show how this differs from regular teleportation used in modern VR games: from the same starting point (top images), regular teleportation moves the user forward while retaining the same orientation (middle images), whereas cinematic techniques can increase the visual appeal of the environment (bottom images).
Sense of space preserved without motion sickness
The results also address the issue of motion sickness — a common problem for VR users. Usually, to prevent nausea, designers use teleportation as a method for moving through digital spaces. The researchers’ approach is also based on teleportation, but it aims to fix the problems associated with this technique.

“In virtual environments, teleportation can lead to reduced spatial awareness, forcing users to reorient themselves after teleporting and can cause them to miss important elements in their surroundings,” says Medlar.
“The cinematography techniques we used give the designers of virtual environments a way to influence users’ attention as they move around the space to affect how they perceive their surroundings,” he continues.
Implications for gaming, museums, and movies
The research carries substantial implications for a range of VR applications, especially as the affordability of VR headsets keeps improving. Video games, virtual museums, galleries, and VR movies could all benefit from these findings, utilizing the proposed methods to craft more engaging and coherent experiences for their users.
Medlar believes the results will be of practical use to virtual reality designers.
“The potential impact of improving navigation in VR and giving designers more tools to affect user experience is huge.” More

Using interference between two lasers, a research group led by scientists from RIKEN and NTT Research have created an ‘optical conveyor belt’ that can move polaritons — a type of light-matter hybrid particle — in semiconductor-based microcavities. This work could lead to the development of new devices with applications in areas such as quantum metrology and quantum information.
For the current study, published in Nature Photonics, the scientists used the interference between two lasers to create a dynamic potential energy landscape — imagine a landscape of valleys and hills, in constant repeating motion — for a coherent, laser-like state of polaritons known as a polariton condensate. They achieved this by introducing a new optical tool — an optical conveyor belt — to enable the control of the energy landscape, concretely, the lattice depths and the interactions between neighboring particles. By further tuning the frequency difference between the two lasers, the conveyer belt moves at speeds of the order of 0.1 percent of the speed of light, driving the polaritons into a new state.
Non-reciprocity — a phenomenon where system dynamics are different in opposing directions — is a crucial ingredient for creating what is known as an artificial topological phase of matter. Topology is the mathematical classification of objects by counting the number of number of ‘holes’, e.g. a donut or a knot may have a finite number of holes, while a ball has none. Quantum materials can also be engineered with a non-zero topology, which in this case is more abstractly embedded into the band structure. Such materials can exhibit behavior such as dissipationless transport, meaning that they can move without energy loss, and other exotic quantum phenomena. It is extremely challenging to introduce non-reciprocity into engineered optical platforms, and this simple, extendible experimental demonstration opens new opportunities for emerging quantum technologies incorporate functional topology.
The research group, including first author Yago del Valle Inclan Redondo, and led by Senior Research Scientist Michael Fraser, both from RIKEN CEMS and NTT Research, together with collaborators from Germany, Singapore and Australia, have conducted a study in this direction. Fraser says, “We have created a topological state of light in a semiconductor structure by a mechanism involving rapid modulation of the energy landscape, resulting in the introduction of a synthetic dimension.” A synthetic dimension is a method of mapping a non-spatial dimension, in this case time, into a space-like dimension, such that the system dynamics can evolve in a higher number of dimensions and become better suited to realizing topological matter. This work extends upon a technique developed by the group, published last year (see “Optically Driven Rotation of Exciton-Polariton Condensates”), which similarly used temporally modulated lasers to drive the rapid rotation of polariton condensates.
Using this simple experimental scheme involving the interference between two lasers, the scientists were able to organize polaritons in precisely the right dimensions to create an artificial band structure, meaning that the particles organized into energy bands like electrons in a material. By tuning the dimensions, depth and speed of the polariton optical lattice, control over the band structure is achieved. Thanks to this rapid motion, the polaritons see a different potential energy landscape depending on whether they are propagating with or against the flow of the lattice, an effect which is analogous to the Doppler shift for sound. This asymmetric response of the confined polaritons breaks time-reversal symmetry, driving non-reciprocity and the formation of a topological band structure.
“Photonic states with topological properties can be used in advanced opto-electronic devices where topology might greatly improve the performance of optical devices, circuits, and networks, such as by reducing noise and lasing threshold powers, and dissipationless optical waveguiding. Further, the simplicity and robustness of our technique opens new opportunities for the development of topological photonic devices with applications in quantum metrology and quantum information,” concludes Fraser. More

The declining population in Osaka is related to an aging society that is driving up health expenditures. Dr. Haruka Kato, a junior associate professor at Osaka Metropolitan University, teamed up with the Future Co-creation Laboratory at Japan System Techniques Co., Ltd. to conduct natural experiments on how a new train station might impact healthcare expenditures.
JR-Sojiji Station opened in March 2018 in a suburban city on the West Japan Railway line connecting Osaka and Kyoto. The researchers used a causal impact algorithm to analyze the medical expenditure data gathered from the time series medical dataset REZULT provided by Japan System Techniques.
Their results indicate that opening this mass transit station was significantly associated with a decrease in average healthcare expenditures per capita by approximately 99,257.31 Japanese yen (USD 929.99) over four years, with US dollar figures based on March 2018 exchange rates. In addition, the 95% confidence interval indicated the four-year decreasing expenditure of JPY 136,194.37 ($1276.06) to JPY 62,119.02 ($582.02). This study’s findings are consistent with previous studies suggesting that increased access to transit might increase physical activity among transit users. The results provided evidence for the effectiveness of opening a mass transit station from the viewpoint of health expenditures.
“From the perspective of evidence-based policymaking, there is a need to assess the social impact of urban designs,” said Dr. Kato. “Our findings are an important achievement because they enable us to assess this impact from the perspective of health care expenditures, as in the case of JR-Sojiji Station.” More