Computers Math

With the addition of just a small number of autonomous vehicles (AVs) on the road, traffic flow can become faster, greener, and safer in the near future, a new study suggests.
The study, published in Journal of Physics A: Mathematical and Theoretical, focused on the anticipated hybrid traffic flow of the future, which will combine traditional, human-operated vehicles with a small fraction of AVs. This scenario raises several questions as to whether traffic flow would actually improve and, if so, how many AVs would be required to produce significant change.
It may seem that a large number of AVs is required for a significant impact on traffic flow, especially on multilane freeways, as human drivers can simply ignore and bypass AVs. But this isn’t necessarily so. In their research, Dr. Amir Goldental and Prof. Ido Kanter, from the Department of Physics at Bar-Ilan University, present a simple set of guidelines and regulations for achieving the self-organization of AVs into constellations that dynamically control the entire traffic flow.
The researchers suggest guidelines for efficient regulations, such that AVs can cooperate and significantly enhance traffic flow even when fewer than 5% of the vehicles on the road are autonomous, as seen in the accompanying video and image. In their article, the researchers describe how AVs should behave on a freeway in order to self-organize into groups that split the traffic flow into controllable clusters. It was observed that it takes less than two minutes to achieve self-organized high-speed, greener and safer traffic flow when starting from congested traffic.
“Without regulations on AVs, we face a classic example of game theory paradox, such as the prisoner’s dilemma, where each vehicle tries to optimize its driving speed but the overall traffic flow is not optimal. In our research we examine how, with proper regulations, a very small number of AVs can improve the overall traffic flow significantly, through cooperation,” says Dr. Goldental.
Quantitatively, the authors report a substantial increase of up to 40% in traffic flow speed with up to a 28% decrease in fuel consumption. Also, traffic safety is enhanced as traffic becomes more ordered and fewer lane transitions occur. The study shows that these improvements can be achieved without a central agent that governs AVs and without communication between AVs using current infrastructure.

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Materials provided by Bar-Ilan University. Note: Content may be edited for style and length. More

The emergence of artificial intelligence and machine learning techniques is changing the world dramatically with novel applications such as internet of things, autonomous vehicles, real-time imaging processing and big data analytics in healthcare. In 2020, the global data volume is estimated to reach 44 Zettabytes, and it will continue to grow beyond the current capacity of computing and storage devices. At the same time, the related electricity consumption will increase 15 times by 2030, swallowing 8% of the global energy demand. Therefore, reducing energy consumption and increasing speed of information storage technology is in urgent need.
Berkeley researchers led by HKU President Professor Xiang Zhang when he was in Berkeley, in collaboration with Professor Aaron Lindenberg’s team at Stanford University, invented a new data storage method: They make odd numbered layers slide relative to even-number layers in tungsten ditelluride, which is only 3nm thick. The arrangement of these atomic layers represents 0 and 1 for data storage. These researchers creatively make use of quantum geometry: Berry curvature, to read information out. Therefore, this material platform works ideally for memory, with independent ‘write’ and ‘read’ operation. The energy consumption using this novel data storage method can be over 100 times less than the traditional method.
This work is a conceptual innovation for non-volatile storage types and can potentially bring technological revolution. For the first time, the researchers prove that two-dimensional semi-metals, going beyond traditional silicon material, can be used for information storage and reading. This work was published in the latest issue of the journal Nature Physics [ref 1]. Compared with the existing non-volatile (NVW) memory, this new material platform is expected to increase storage speed by two orders and decrease energy cost by three orders, and it can greatly facilitate the realization of emerging in-memory computing and neural network computing.
This research was inspired by the research of Professor Zhang ‘s team on “Structural phase transition of single-layer MoTe2 driven by electrostatic doping” , published in Nature in 2017 ; and Lindenberg Lab’s research on “Use of light to control the switch of material properties in topological materials,” published in Nature in 2019.
Previously, researchers found that in the two-dimensional material-tungsten ditelluride, when the material is in a topological state, the special arrangement of atoms in these layers can produce so-called “Weyl nodes,” which will exhibit unique electronic properties, such as zero resistance conduction. These points are considered to have wormhole-like characteristics, where electrons tunnel between opposite surfaces of the material. In previous experiment, the researchers found that the material structure can be adjusted by terahertz radiation pulse, thereby quickly switching between the topological and non-topological states of the material, effectively turning the zero-resistance state off and then on again. Zhang’s team has proved that the atomic-level thickness of two-dimensional materials greatly reduces the screening effect of the electric field, and its structure is easily affected by the electron concentration or electric field. Therefore, topological materials at two-dimensional limit can allow the turning of optical manipulation into electrical control, paving towards electronic devices.
In this work, the researchers stacked three atomic layers of tungsten ditelluride metal layers, like nanoscale deck of cards. By injecting a small amount of carriers into the stack or applying a vertical electric field, they caused each odd-numbered layer to slide laterally relative to the even-numbered layers above and below it. Through the corresponding optical and electrical characterizations, they observed that this slip is permanent until another electrical excitation triggers layers to rearrange. Furthermore, in order to read the data and information stored between these moving atomic layers, the researchers used the extremely large “Berry curvature” in the semi-metallic material. This quantum characteristic is like a magnetic field, which can steer electrons’ propagation and result in nonlinear Hall effect. Through such effect, the arrangement of the atomic layer can be read without disturbing the stacking.
Using this quantum characteristic, different stacks and metal polarization states can be distinguished well. This discovery solves the long-term reading difficulty in ferroelectric metals due to their weak polarization. This makes ferroelectric metals not only interesting in basic physical exploration, but also proves that such materials may have applicational prospects comparable to conventional semiconductors and ferroelectric insulators. Changing the stacking orders only involves the breaking of the Van der Waals bond. Therefore, the energy consumption is theoretically two orders of magnitude lower than the energy consumed by breaking covalent bond in traditional phase change materials and provides a new platform for the development of more energy-efficient storage devices and helps us move towards a sustainable and smart future.

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Researchers have developed electronic artificial skin that reacts to pain just like real skin, opening the way to better prosthetics, smarter robotics and non-invasive alternatives to skin grafts.
The prototype device developed by a team at RMIT University in Melbourne, Australia, can electronically replicate the way human skin senses pain.
The device mimics the body’s near-instant feedback response and can react to painful sensations with the same lighting speed that nerve signals travel to the brain.
Lead researcher Professor Madhu Bhaskaran said the pain-sensing prototype was a significant advance towards next-generation biomedical technologies and intelligent robotics.
“Skin is our body’s largest sensory organ, with complex features designed to send rapid-fire warning signals when anything hurts,” Bhaskaran said.
“We’re sensing things all the time through the skin but our pain response only kicks in at a certain point, like when we touch something too hot or too sharp.

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“No electronic technologies have been able to realistically mimic that very human feeling of pain — until now.
“Our artificial skin reacts instantly when pressure, heat or cold reach a painful threshold.
“It’s a critical step forward in the future development of the sophisticated feedback systems that we need to deliver truly smart prosthetics and intelligent robotics.”
Functional sensing prototypes
As well as the pain-sensing prototype, the research team has also developed devices using stretchable electronics that can sense and respond to changes in temperature and pressure.

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Bhaskaran, co-leader of the Functional Materials and Microsystems group at RMIT, said the three functional prototypes were designed to deliver key features of the skin’s sensing capability in electronic form.
With further development, the stretchable artificial skin could also be a future option for non-invasive skin grafts, where the traditional approach is not viable or not working.
“We need further development to integrate this technology into biomedical applications but the fundamentals — biocompatibility, skin-like stretchability — are already there,” Bhaskaran said.
How to make electronic skin
The new research, published in Advanced Intelligent Systems and filed as a provisional patent, combines three technologies previously pioneered and patented by the team:
Stretchable electronics: combining oxide materials with biocompatible silicon to deliver transparent, unbreakable and wearable electronics as thin as a sticker.
Temperature-reactive coatings: self-modifying coatings 1,000 times thinner than a human hair based on a material that transforms in response to heat.
Brain-mimicking memory: electronic memory cells that imitate the way the brain uses long-term memory to recall and retain previous information.
The pressure sensor prototype combines stretchable electronics and long-term memory cells, the heat sensor brings together temperature-reactive coatings and memory, while the pain sensor integrates all three technologies.
PhD researcher Md Ataur Rahman said the memory cells in each prototype were responsible for triggering a response when the pressure, heat or pain reached a set threshold.
“We’ve essentially created the first electronic somatosensors — replicating the key features of the body’s complex system of neurons, neural pathways and receptors that drive our perception of sensory stimuli,” he said.
“While some existing technologies have used electrical signals to mimic different levels of pain, these new devices can react to real mechanical pressure, temperature and pain, and deliver the right electronic response.
“It means our artificial skin knows the difference between gently touching a pin with your finger or accidentally stabbing yourself with it — a critical distinction that has never been achieved before electronically.”
The research was supported by the Australian Research Council and undertaken at RMIT’s state-of-the-art Micro Nano Research Facility for micro/nano-fabrication and device prototyping.

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Materials provided by RMIT University. Original written by Gosia Kaszubska. Note: Content may be edited for style and length. More

Innovators from Purdue University hope their new technology can help transform paper sheets from a notebook into a music player interface and make food packaging interactive.
Purdue engineers developed a simple printing process that renders any paper or cardboard packaging into a keyboard, keypad or other easy-to-use human-machine interfaces. This technology is published in the Aug. 23 edition of Nano Energy.
“This is the first time a self-powered paper-based electronic device is demonstrated,” said Ramses Martinez, an assistant professor in Purdue’s School of Industrial Engineering and in the Weldon School of Biomedical Engineering in Purdue’s College of Engineering. “We developed a method to render paper repellent to water, oil and dust by coating it with highly fluorinated molecules. This omniphobic coating allows us to print multiple layers of circuits onto paper without getting the ink to smear from one layer to the next one.”
Martinez said this innovation facilitates the fabrication of vertical pressure sensors that do not require any external battery, since they harvest the energy from their contact with the user.
This technology is compatible with conventional large-scale printing processes and could easily be implemented to rapidly convert conventional cardboard packaging or paper into smart packaging or a smart human-machine interface.
“I envision this technology to facilitate the user interaction with food packaging, to verify if the food is safe to be consumed, or enabling users to sign the package that arrives at home by dragging their finger over the box to proper identify themselves as the owner of the package,” Martinez said. “Additionally, our group demonstrated that simple paper sheets from a notebook can be transformed into music player interfaces for users to choose songs, play them and change their volume.”
Videos showing this technology are available at https://youtu.be/TfA0d8IpjWU, https://youtu.be/J0iCxjicJIQ and https://youtu.be/c9E6vXYtIw0.

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Materials provided by Purdue University. Original written by Chris Adam. Note: Content may be edited for style and length. More

Among all the curious states of matter that can coexist in a quantum material, jostling for preeminence as temperature, electron density and other factors change, some scientists think a particularly weird juxtaposition exists at a single intersection of factors, called the quantum critical point or QCP.
“Quantum critical points are a very hot issue and interesting for many problems,” says Wei-Sheng Lee, a staff scientist at the Department of Energy’s SLAC National Accelerator Laboratory and investigator with the Stanford Institute for Materials and Energy Sciences (SIMES). “Some suggest that they’re even analogous to black holes in the sense that they are singularities — point-like intersections between different states of matter in a quantum material — where you can get all sorts of very strange electron behavior as you approach them.”
Lee and his collaborators reported in Nature Physics today that they have found strong evidence that QCPs and their associated fluctuations exist. They used a technique called resonant inelastic X-ray scattering (RIXS) to probe the electronic behavior of a copper oxide material, or cuprate, that conducts electricity with perfect efficiency at relatively high temperatures.
These so-called high-temperature superconductors are a bustling field of research because they could give rise to zero-waste transmission of energy, energy-efficient transportation systems and other futuristic technologies, although no one knows the underlying microscopic mechanism behind high-temperature superconductivity yet. Whether QCPs exist in cuprates is also a hotly debated issue.
In experiments at the UK’s Diamond Light Source, the team chilled the cuprate to temperatures below 90 kelvins (minus 183 degrees Celsius), where it became superconducting. They focused their attention on what’s known as charge order — alternating stripes in the material where electrons and their negative charges are denser or more sparse.
The scientists excited the cuprate with X-rays and measured the X-ray light that scattered into the RIXS detector. This allowed them to map out how the excitations propagated through the material in the form of subtle vibrations, or phonons, in the material’s atomic lattice, which are hard to measure and require very high-resolution tools.

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At the same time, the X-rays and the phonons can excite electrons in the charge order stripes, causing the stripes to fluctuate. Since the data obtained by RIXS reflects the coupling between the behavior of the charge stripes and the behavior of the phonons, observing the phonons allowed the researchers to measure the behavior of the charge order stripes, too.
What the scientists expected to see is that when the charge order stripes grew weaker, their excitations would also fade away. “But what we observed was very strange,” Lee said. “We saw that when charge order became weaker in the superconducting state, the charge order excitations became stronger. This is a paradox because they should go hand in hand, and that’s what people find in other charge order systems.”
He added, “To my knowledge this is the first experiment about charge order that has shown this behavior. Some have suggested that this is what happens when a system is near a quantum critical point, where quantum fluctuations become so strong that they melt the charge order, much like heating ice increases thermal vibrations in its rigid atomic lattice and melts it into water. The difference is that quantum melting, in principle, occurs at zero temperature.” In this case, Lee said, the unexpectedly strong charge order excitations seen with RIXS were manifestations of those quantum fluctuations.
Lee said the team is now studying these phenomena at a wider range of temperatures and at different levels of doping — where compounds are added to change the density of freely moving electrons in the material — to see if they can nail down exactly where the quantum critical point could be in this material.
Thomas Devereaux, a theorist at SIMES and senior author of the report, noted that many phases of matter can be intertwined in cuprates and other quantum materials.
“Superconducting and magnetic states, charge order stripes and so on are so entangled that you can be in all of them at the same time,” he said. “But we’re stuck in our classical way of thinking that they have to be either one way or another.”
Here, he said, “We have an effect, and Wei-Sheng is trying to measure it in detail, trying to see what’s going on.” More

In 2019, Google claimed it was the first to demonstrate a quantum computer performing a calculation beyond the abilities of today’s most powerful supercomputers.
But most of the time, creating a quantum algorithm that stands a chance at beating a classical computer is an accidental process, Purdue University scientists say. To bring more guidance to this process and make it less arbitrary, these scientists developed a new theory that may eventually lead to more systematic design of quantum algorithms.
The new theory, described in a paper published in the journal Advanced Quantum Technologies, is the first known attempt to determine which quantum states can be created and processed with an acceptable number of quantum gates to outperform a classical algorithm.
Physicists refer to this concept of having the right number of gates to control each state as “complexity.” Since the complexity of a quantum algorithm is closely related to the complexity of quantum states involved in the algorithm, the theory could therefore bring order to the search for quantum algorithms by characterizing which quantum states meet that complexity criteria.
An algorithm is a sequence of steps to perform a calculation. The algorithm is usually implemented on a circuit.
In classical computers, circuits have gates that switch bits to either a 0 or 1 state. A quantum computer instead relies on computational units called “qubits” that store 0 and 1 states simultaneously in superposition, allowing more information to be processed.

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What would make a quantum computer faster than a classical computer is simpler information processing, characterized by the enormous reduction in the number of quantum gates in a quantum circuit compared with a classical circuit.
In classical computers the number of gates in circuits increases exponentially with respect to the size of the problem of interest. This exponential model grows so astonishingly fast that it becomes physically impossible to handle for even a moderately sized problem of interest.
“For example, even a small protein molecule may contain hundreds of electrons. If each electron can only take two forms, then to simulate 300 electrons would require 2300 classical states, which is more than the number of all the atoms in the universe,” said Sabre Kais, a professor in Purdue’s Department of Chemistry and member of the Purdue Quantum Science and Engineering Institute.
For quantum computers, there is a way for quantum gates to scale up “polynomially” — rather than just exponentially like a classical computer — with the size of the problem (like the number of electrons in the last example). “Polynomial” means that there would be drastically fewer steps (gates) needed to process the same amount of information, making a quantum algorithm superior to a classical algorithm.
Researchers so far haven’t had a good way to identify which quantum states could satisfy this condition of polynomial complexity.
“There is a very large search space for finding the states and sequence of gates that match up in complexity to create a useful quantum algorithm capable of performing calculations faster than a classical algorithm,” said Kais, whose research group is developing quantum algorithms and quantum machine learning methods.
Kais and Zixuan Hu, a Purdue postdoctoral associate, used the new theory to identify a large group of quantum states with polynomial complexity. They also showed that these states may share a coefficient feature that could be used to better identify them when designing a quantum algorithm.
“Given any quantum state, we are now able to design an efficient coefficient sampling procedure to determine if it belongs to the class or not,” Hu said.
This work is supported by the U.S. Department of Energy (Office of Basic Energy Sciences) under Award No. DE-SC0019215. The Purdue Quantum Science and Engineering Institute is part of Purdue’s Discovery Park.

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Materials provided by Purdue University. Original written by Kayla Wiles. Note: Content may be edited for style and length. More

University of Texas at Dallas researchers and their international colleagues have developed a method to create micro LEDs that can be folded, twisted, cut and stuck to different surfaces.
The research, published online in June in the journal Science Advances, helps pave the way for the next generation of flexible, wearable technology.
Used in products ranging from brake lights to billboards, LEDs are ideal components for backlighting and displays in electronic devices because they are lightweight, thin, energy efficient and visible in different types of lighting. Micro LEDs, which can be as small as 2 micrometers and bundled to be any size, provide higher resolution than other LEDs. Their size makes them a good fit for small devices such as smart watches, but they can be bundled to work in flat-screen TVs and other larger displays. LEDs of all sizes, however, are brittle and typically can only be used on flat surfaces.
The researchers’ new micro LEDs aim to fill a demand for bendable, wearable electronics.
“The biggest benefit of this research is that we have created a detachable LED that can be attached to almost anything,” said Dr. Moon Kim, Louis Beecherl Jr. Distinguished Professor of materials science and engineering at UT Dallas and a corresponding author of the study. “You can transfer it onto your clothing or even rubber — that was the main idea. It can survive even if you wrinkle it. If you cut it, you can use half of the LED.”
Researchers in the Erik Jonsson School of Engineering and Computer Science and the School of Natural Sciences and Mathematics helped develop the flexible LED through a technique called remote epitaxy, which involves growing a thin layer of LED crystals on the surface of a sapphire crystal wafer, or substrate.

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Typically, the LED would remain on the wafer. To make it detachable, researchers added a nonstick layer to the substrate, which acts similarly to the way parchment paper protects a baking sheet and allows for the easy removal of cookies, for instance. The added layer, made of a one-atom-thick sheet of carbon called graphene, prevents the new layer of LED crystals from sticking to the wafer.
“The graphene does not form chemical bonds with the LED material, so it adds a layer that allows us to peel the LEDs from the wafer and stick them to any surface,” said Kim, who oversaw the physical analysis of the LEDs using an atomic resolution scanning/transmission electron microscope at UT Dallas’ Nano Characterization Facility.
Colleagues in South Korea carried out laboratory tests of LEDs by adhering them to curved surfaces, as well as to materials that were subsequently twisted, bent and crumpled. In another demonstration, they adhered an LED to the legs of a Lego minifigure with different leg positions.
Bending and cutting do not affect the quality or electronic properties of the LED, Kim said.
The bendy LEDs have a variety of possible uses, including flexible lighting, clothing and wearable biomedical devices. From a manufacturing perspective, the fabrication technique offers another advantage: Because the LED can be removed without breaking the underlying wafer substrate, the wafer can be used repeatedly.
“You can use one substrate many times, and it will have the same functionality,” Kim said.
In ongoing studies, the researchers also are applying the fabrication technique to other types of materials.
“It’s very exciting; this method is not limited to one type of material,” Kim said. “It’s open to all kinds of materials.”

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Materials provided by University of Texas at Dallas. Original written by Kim Horner. Note: Content may be edited for style and length. More

Imagine working on a jigsaw puzzle with so many pieces that even the edges seem indistinguishable from others at the puzzle’s centre. The solution seems nearly impossible. And, to make matters worse, this puzzle is in a futuristic setting where the pieces are not only numerous, but ever-changing. In fact, you not only must solve the puzzle, but “un-solve” it to parse out how each piece brings the picture wholly into focus.
That’s the challenge molecular and cellular biologists face in sorting through cells to study an organism’s structural origin and the way it develops, known as morphogenesis. If only there was a tool that could help. An eLife paper out this week shows there now is.
An EMBL research group led by Anna Kreshuk, a computer scientist and expert in machine learning, joined the DFG-funded FOR2581 consortium of plant biologists and computer scientists to develop a tool that could solve this cellular jigsaw puzzle. Starting with computer code and moving on to a more user-friendly graphical interface called PlantSeg, the team built a simple open-access method to provide the most accurate and versatile analysis of plant tissue development to date. The group included expertise from EMBL, Heidelberg University, the Technical University of Munich, and the Max Planck Institute for Plant Breeding Research in Cologne.
“Building something like PlantSeg that can take a 3D perspective of cells and actually separate them all is surprisingly hard to do, considering how easy it is for humans,” Kreshuk says. “Computers aren’t as good as humans when it comes to most vision-related tasks, as a rule. With all the recent development in deep learning and artificial intelligence at large, we are closer to solving this now, but it’s still not solved — not for all conditions. This paper is the presentation of our current approach, which took some years to build.”
If researchers want to look at morphogenesis of tissues at the cellular level, they need to image individual cells. Lots of cells means they also have to separate or “segment” them to see each cell individually and analyse the changes over time.
“In plants, you have cells that look extremely regular that in a cross-section looks like rectangles or cylinders,” Kreshuk says. “But you also have cells with so-called ‘high lobeness’ that have protrusions, making them look more like puzzle pieces. These are more difficult to segment because of their irregularity.”
Kreshuk’s team trained PlantSeg on 3D microscope images of reproductive organs and developing lateral roots of a common plant model, Arabidopsis thaliana, also known as thale cress. The algorithm needed to factor in the inconsistencies in cell size and shape. Sometimes cells were more regular, sometimes less. As Kreshuk points out, this is the nature of tissue.

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A beautiful side of this research came from the microscopy and images it provided to the algorithm. The results manifested themselves in colourful renderings that delineated the cellular structures, making it easier to truly “see” segmentation.
“We have giant puzzle boards with thousands of cells and then we’re essentially colouring each one of these puzzle pieces with a different colour,” Kreshuk says.
Plant biologists have long needed this kind of tool, as morphogenesis is at the crux of many developmental biology questions. This kind of algorithm allows for all kinds of shape-related analysis, for example, analysis of shape changes through development or under a change in environmental conditions, or between species. The paper gives some examples, such as characterising developmental changes in ovules, studying the first asymmetric cell division which initiates the formation of the lateral root, and comparing and contrasting the shape of leaf cells between two different plant species.
While this tool currently targets plants specifically, Kreshuk points out that it could be tweaked to be used for other living organisms as well.
Machine learning-based algorithms, like the ones used at the core of PlantSeg, are trained from correct segmentation examples. The group has trained PlantSeg on many plant tissue volumes, so that now it generalises quite well to unseen plant data. The underlying method is, however, applicable to any tissue with cell boundary staining and one could easily retrain it for animal tissue.
“If you have tissue where you have a boundary staining, like cell walls in plants or cell membranes in animals, this tool can be used,” Kreshuk says. “With this staining and at high enough resolution, plant cells look very similar to our cells, but they are not quite the same. The tool right now is really optimised for plants. For animals, we would probably have to retrain parts of it, but it would work.”
Currently, PlantSeg is an independent tool but one that Kreshuk’s team will eventually merge into another tool her lab is working on, ilastik Multicut workflow. More