Computers Math

Our modern understanding of the physical world is based on gauge theories: mathematical models from theoretical physics that describe the interactions between elementary particles (such as electrons or quarks) and explain quantum mechanically three of the fundamental forces of nature: the electromagnetic, weak, and strong forces. The fourth fundamental force, gravity, is described by Einstein’s theory of general relativity, which, while not yet understood in the quantum regime, is also a gauge theory. Gauge theories can also be used to explain the exotic quantum behavior of electrons in certain materials or the error correction codes that future quantum computers will need to work reliably, and are the workhorse of modern physics.
In order to better understand these theories, one possibility is to realize them using artificial and highly controllable quantum systems. This strategy is called quantum simulation and constitutes a special type of quantum computing. It was first proposed by the physicist Richard Feynman in the 80s, more than fifteen years after being awarded the Nobel prize in physics for his pioneering theoretical work on gauge theories. Quantum simulation can be seen as a quantum LEGO game where experimental physicists give reality to abstract theoretical models. They build them in the laboratory “quantum brick by quantum brick,” using very well controlled quantum systems such as ultracold atoms or ions. After assembling one quantum LEGO prototype for a specific model, the researchers can measure its properties very precisely in the lab, and use their results to understand better the theory that it mimics. During the last decade, quantum simulation has been intensively exploited to investigate quantum materials. However, playing the quantum LEGO game with gauge theories is fundamentally more challenging. Until now, only the electromagnetic force could be investigated in this way.
In a recent study published in Nature, ICFO experimental researchers Anika Frölian, Craig Chisholm, Ramón Ramos, Elettra Neri, and Cesar Cabrera, led by ICREA Prof. at ICFO Leticia Tarruell, in collaboration with Alessio Celi, a theoretical researcher from the Talent program at the Autonomous University of Barcelona, were able to simulate a gauge theory other than electromagnetism for the first time, using ultracold atoms.
A gauge theory for very heavy photons
The team set out to realize in the laboratory a gauge theory belonging to the class of topological gauge theories, different from the class of dynamical gauge theories to which electromagnetism belongs.
In the gauge theory language, the electromagnetic force between two electrons arises when they exchange a photon: a particle of light that can propagate even when matter is absent. However, in two-dimensional quantum materials subjected to very strong magnetic fields, the photons exchanged by the electrons behave as if they were extremely heavy and can only move as long as they are attached to matter. As a result, the electrons have very peculiar properties: they can only flow through the edges of the material, in a direction that is set by the orientation of the magnetic field, and their charge becomes apparently fractional. This behavior is known as the fractional quantum Hall effect, and is described by the Chern-Simons gauge theory (named after the mathematicians that developed one of its key elements). The behavior of the electrons restricted to a single edge of the material should also be described by a gauge theory, in this case called chiral BF, which was proposed in the 90s but not realized in a laboratory until the ICFO and UAB researchers pulled it out of the freezer. More

Researchers at NYU Tandon have reached a key milestone in their quest to develop wearable technology that manages to measure key brain mechanisms through the skin.
Rose Faghih, Associate Professor of Biomedical Engineering, has been working for the last seven years on a technology that can measure mental activity using electrodermal activity (EDA) — an electrical phenomenon of the skin that is influenced by brain activity related to emotional status. Internal stresses, whether caused by pain, exhaustion, or a particularly packed schedule, can cause changes in the EDA — changes that are directly correlated to mental states.
The overarching goal — a Multimodal Intelligent Noninvasive brain state Decoder for Wearable AdapTive Closed-loop arcHitectures, or MINDWATCH, as Faghih calls it — would act as a way to monitor a wearer’s mental state, and offer nudges that would help them revert back to a more neutral state of mind. For example, if a person was experiencing a particularly severe bout of work-related stress, the MINDWATCH could pick up on this and automatically play some relaxing music.
Now Faghih — along with Rafiul Amin, her former PhD student — has accomplished a crucial task required for monitoring this information. For the first time, they have developed a novel inference engine that can monitor brain activity through the skin in real time with high scalability and accuracy. The results are featured in a new paper, “Physiological Characterization of Electrodermal Activity Enables Scalable Near Real-Time Autonomic Nervous System Activation Inference,” published in PLOS Computational Biology.
“Inferring autonomic nervous system activation from wearable devices in real-time opens new opportunities for monitoring and improving mental health and cognitive engagement,” according to Faghih.
Previous methods measuring sympathetic nervous system activation through the skin took minutes, which is not practical for wearable devices. While her earlier work focused on inferring brain activity through sweat activation and other factors, the new study additionally models the sweat glands themselves. The model includes a 3D state-space representation of the direct secretion of sweat via pore opening, as well as diffusion followed by corresponding evaporation and reabsorption. This detailed model of the glands provides exceptional insight into inferring the brain activity. More

An international collaboration of scientists has created and observed an entirely new class of vortices — the whirling masses of fluid or air.
Led by researchers from Amherst College in the US and the University of East Anglia and Lancaster University in the UK, their new paper details the first laboratory studies of these ‘exotic’ whirlpools in an ultracold gas of atoms at temperatures as low as tens of billionths of a degree above absolute zero.
The discovery, announced this week in the journal Nature Communications, may have exciting future implications for implementations of quantum information and computing.
Vortices are familiar objects in nature, from the whirlpools of water down a bathtub drain to the airflow around a hurricane.
In quantum-mechanical systems, such as an atomic Bose-Einstein condensate, the vortices tend to be tiny and their circulation comes in discrete, quantized units. Such vortices have long been objects of fascination for physicists and have helped to illuminate the unusual properties of superfluidity and superconductivity.
The unusual nature of the observed whirlpools here, however, is due to symmetries in the quantum gas. One especially fascinating property of physical theories, from cosmology to elementary particles, is the appearance of asymmetric worlds despite perfect underlying symmetries. For example, when water freezes to ice, disordered molecules in a liquid arrange themselves into a periodic array. More

Wearable human-machine interface devices, HMIs, can be used to control machines, computers, music players, and other systems. A challenge for conventional HMIs is the presence of sweat on human skin.
In Applied Physics Reviews, by AIP Publishing, scientists at UCLA describe their development of a type of HMI that is stretchable, inexpensive, and waterproof. The device is based on a soft magnetoelastic sensor array that converts mechanical pressure from the press of a finger into an electrical signal.
The device involves two main components. The first component is a layer that translates mechanical movement to a magnetic response. It consists of a set of micromagnets in a porous silicone matrix that can convert the gentle fingertip pressure into a magnetic field variation.
The second component is a magnetic induction layer consisting of patterned liquid metal coils. These coils respond to the magnetic field changes and generate electricity through the phenomenon of electromagnetic induction.
“Owing to the material’s flexibility and durability, the magnetoelastic sensor array can generate stable power under deformations, such as rolling, folding, and stretching,” said author Jun Chen, from UCLA. “Due to these compelling features, the device can be adopted for human-body powered HMI by transforming human biomechanical activities into electrical signals.”
The power required to run the HMI comes from the wearer’s movements. This means no batteries or other external power components are required, rendering the HMI more environmentally friendly and sustainable.
The device was tested in a variety of real-world situations, including in the presence of a water spray, such as might exist in the shower, a rainstorm, or during vigorous athletic activity. The device worked well when wet, since the magnetic field was not greatly affected by the presence of water.
The investigators studied a range of fabrication and assembly techniques to optimize the biomechanical-to-electrical energy conversion of the device. They found they could achieve a balance between performance and flexibility by controlling the thickness of the flexible film and the concentration of the magnetic particles.
To test their system, the investigators carried out a series of experiments in which a subject applied finger taps to turn a lamp off and on and control a music player.
“Our magnetoelastic sensor array not only wirelessly functions as the on and off buttons of a lamp but also controls a music player’s command features, representing the actions of play, pause, next, and previous,” Chen said.
These tests promise new applications for versatile water-resistant HMIs that can be used to control many types of smart devices.
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Materials provided by American Institute of Physics. Note: Content may be edited for style and length. More

Cedars-Sinai investigators have created the most bio-realistic and complex computer models of individual brain cells — in unparalleled quantity. Their research, published today in the peer-reviewed journal Cell Reports, details how these models could one day answer questions about neurological disorders — and even human intellect — that aren’t possible to explore through biological experiments.
“These models capture the shape, timing and speed of the electrical signals that neurons fire in order to communicate with each other, which is considered the basis of brain function,” said Costas Anastassiou, PhD, a research scientist in the Department of Neurosurgery at Cedars-Sinai, and senior author of the study. “This lets us replicate brain activity at the single-cell level.”
The models are the first to combine data sets from different types of laboratory experiments to present a complete picture of the electrical, genetic and biological activity of single neurons. The models can be used to test theories that would require dozens of experiments to examine in the lab, Anastassiou said.
“Imagine that you wanted to investigate how 50 different genes affect a cell’s biological processes,” Anastassiou said. “You would need to create a separate experiment to ‘knock out’ each gene and see what happens. With our computational models, we will be able to change the recipes of these gene markers for as many genes as we like and predict what will happen.”
Another advantage of the models is that they allow researchers to completely control experimental conditions. This opens the possibility of establishing that one parameter, such as a protein expressed by a neuron, causes a change in the cell or a disease condition, such as epileptic seizures, Anastassiou said. In the lab, investigators can often show an association, but it is difficult to prove a cause.
“In laboratory experiments, the researcher doesn’t control everything,” Anastassiou said. “Biology controls a lot. But in a computational simulation, all the parameters are under the creator’s control. In a model, I can change one parameter and see how it affects another, something that is very hard to do in a biological experiment.”
To create their models, Anastassiou and his team from the Anastassiou Lab — members of the Departments of Neurology and Neurosurgery, the Board of Governors Regenerative Medicine Institute and the Center for Neural Science and Medicine at Cedars-Sinai, used two different sets of data on the mouse primary visual cortex, the area of the brain that processes information coming from the eyes. More

In late May of 2021, 172 runners set out to tackle a 100-kilometer (62-mile) ultramarathon in northwestern China. By midday, as the runners made their way through a rugged, high-elevation part of the course, temperatures plunged, strong winds whipped around the hillslopes and freezing rain and hail pummeled the runners. By the next day, the death toll from the sudden storm had risen to 21.
A new study revisits the deadly event with the goal of testing how hyper-local modeling can improve forecast accuracy for mountain events. The runners ran into trouble because hourly weather forecasts for the race underestimated the storm. The steep mountain slopes had highly localized effects on wind, precipitation and temperature at too small a scale for the weather forecasts for the event, according to the new study, which is published in the AGU journal JGR Atmospheres.
Hourly forecasts for the 2021 race were based on relatively large-scale atmospheric processes, with models running at a resolution of three kilometers — sufficient for most regional predictions, but too coarse to capture the “hyper-local” weather like the storm that struck the course, says Haile Xue, a climate scientist at China’s CMA Earth System Modeling and Prediction Centre and lead author of the new study. Even though a wind and cold temperature advisory had been issued the night before, it lacked the resolution required to pinpoint the danger zones on the course.
“An apparent temperature forecast based on a high-resolution simulation may be helpful” in addition to general regional forecasts, Xue says. Conditions like the 2021 storm are common in mountains with extremely high elevations, such as Mount Everest and Denali, the paper states. While less frequent at lower elevations, when such storms do occur, they can strike suddenly and lead to injuries and loss of life.
The new study uses topographic data from the course, at tens of meters of resolution rather than kilometers, to model the hyper-local weather conditions created by the mountains. With a resolution two orders of magnitude finer than the original forecasts for that weekend, as well as detailed considerations of mountainous topography, the model accurately recreated the storm conditions from the race and even offered greater insight into what may have happened that day.
The original forecast included a large-scale cold front, which would have led to temperature drops and stronger — but not extreme — winds, with only a low-level wind advisory issued. The new study found the apparent temperature could have dropped as low as -10 degrees Celsius (14 degrees Fahrenheit), about 3 degrees Celsius cooler than what the original models predicted.
The model also generated an “impact forecast,” including apparent temperature, which could have dropped even lower as it considers humidity and would ideally include the effect of wet clothes or skin on body temperature. Including these in forecasts, Xue says, could help mitigate the risk of hypothermia.
Along with the weather, planning for the race and gear requirements for the runners were discussed following the event. Many endurance events require ample layers for warmth and rain protection; these were suggested but not required, which could have contributed to the loss of life. Both accurate weather forecasts and gear requirements are essential for an event to be safe.
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Materials provided by American Geophysical Union. Note: Content may be edited for style and length. More

With its irregularities and anatomical complexities, the root canal system is one of the most clinically challenging spaces in the oral cavity. As a result, biofilm not fully cleared from the nooks and crannies of the canals remains a leading cause of treatment failure and persistent endodontic infections, and there are limited means to diagnose or assess the efficacy of disinfection. One day, clinicians may have a new tool to overcome these challenges in the form of microrobots.
In a proof-of-concept study, researchers from Penn Dental Medicine and its Center for Innovation & Precision Dentistry (CiPD), have shown that microrobots can access the difficult to reach surfaces of the root canal with controlled precision, treating and disrupting biofilms and even retrieving samples for diagnostics, enabling a more personalized treatment plan. The Penn team shared their findings on the use of two different microrobotic platforms for endodontic therapy in the August issue of the Journal of Dental Research ; the work was selected for the issue’s cover.
“The technology could enable multimodal functionalities to achieve controlled, precision targeting of biofilms in hard-to-reach spaces, obtain microbiological samples, and perform targeted drug delivery, ” says Dr. Alaa Babeer, lead author of the study and a Penn Dental Medicine Doctor of Science in Dentistry (DScD) and endodontics graduate, who is now within the lab of Dr. Michel Koo, co-director of the CiPD .
In both platforms, the building blocks for the microrobots are iron oxide nanoparticles (NPs) that have both catalytic and magnetic activity and have been FDA approved for other uses. In the first platform, a magnetic field is used to concentrate the NPs in aggregated microswarms and magnetically control them to the apical area of the tooth to disrupt and retrieve biofilms through a catalytic reaction. The second platform uses 3D printing to create miniaturized helix-shaped robots embedded with iron oxide NPs. These helicoids are guided by magnetic fields to move within the root canal, transporting bioactives or drugs that can be released on site.
“This technology offers the potential to advance clinical care on a variety of levels,” says Dr. Koo, co-corresponding author of the study with Dr. Edward Steager, a senior research investigator in Penn’s School of Engineering and Applied Science.
“One important aspect is the ability to have diagnostic as well as therapeutic applications. In the microswarm platform, we can not only remove the biofilm, but also retrieve it, enabling us identify what microorganisms caused the infection. In addition, the ability to conform to the narrow and difficult-to-reach spaces within the root canal allows for a more effective disinfection in comparison to the files and instrumentation techniques presently used.”
A Collaborative System More

Scientists have developed a small robot to understand how ants teach one another.
The team built the robot to mimic the behaviour of rock ants that use one-to-one tuition, in which an ant that has discovered a much better new nest can teach the route there to another individual.
The findings, published in the Journal of Experimental Biology today, confirm that most of the important elements of teaching in these ants are now understood because the teaching ant can be replaced by a machine.
Key to this process of teaching is tandem running where one ant literally leads another ant quite slowly along a route to the new nest. The pupil ant learns the route sufficiently well that it can find its own way back home and then lead a tandem-run with another ant to the new nest, and so on.
Prof Nigel Franks of Bristol’s School of Biological Sciences said: “Teaching is so important in our own lives that we spend a great deal of time either instructing others or being taught ourselves. This should cause us to wonder whether teaching actually occurs among non-human animals. And, in fact, the first case in which teaching was demonstrated rigorously in any other animal was in an ant.” The team wanted to determine what was necessary and sufficient in such teaching. If they could build a robot that successfully replaced the teacher, this should show that they largely understood all the essential elements in this process.
The researchers built a large arena so there was an appreciable distance between the ants’ old nest, which was deliberately made to be of low quality, and a new much better one that ants could be led to by a robot. A gantry was placed atop the arena to move back and forth with a small sliding robot attached to it, so that the scientists could direct the robot to move along either straight or wavy routes. Attractive scent glands, from a worker ant, were attached to the robot to give it the pheromones of an ant teacher. More