Computers Math

Wearable devices can identify COVID-19 cases earlier than traditional diagnostic methods and can help track and improve management of the disease, Mount Sinai researchers report in one of the first studies on the topic. The findings were published in the Journal of Medical Internet Research on January 29.
The Warrior Watch Study found that subtle changes in a participant’s heart rate variability (HRV) measured by an Apple Watch were able to signal the onset of COVID-19 up to seven days before the individual was diagnosed with the infection via nasal swab, and also to identify those who have symptoms.
“This study highlights the future of digital health,” says the study’s corresponding author Robert P. Hirten, MD, Assistant Professor of Medicine (Gastroenterology) at the Icahn School of Medicine at Mount Sinai, and member of the Hasso Plattner Institute for Digital Health at Mount Sinai and the Mount Sinai Clinical Intelligence Center (MSCIC). “It shows that we can use these technologies to better address evolving health needs, which will hopefully help us improve the management of disease. Our goal is to operationalize these platforms to improve the health of our patients and this study is a significant step in that direction. Developing a way to identify people who might be sick even before they know they are infected would be a breakthrough in the management of COVID-19.”
The researchers enrolled several hundred health care workers throughout the Mount Sinai Health System in an ongoing digital study between April and September 2020. The participants wore Apple Watches and answered daily questions through a customized app. Changes in their HRV — a measure of nervous system function detected by the wearable device — were used to identify and predict whether the workers were infected with COVID-19 or had symptoms. Other daily symptoms that were collected included fever or chills, tiredness or weakness, body aches, dry cough, sneezing, runny nose, diarrhea, sore throat, headache, shortness of breath, loss of smell or taste, and itchy eyes.
Additionally, the researchers found that 7 to 14 days after diagnosis with COVID-19, the HRV pattern began to normalize and was no longer statistically different from the patterns of those who were not infected.
“This technology allows us not only to track and predict health outcomes, but also to intervene in a timely and remote manner, which is essential during a pandemic that requires people to stay apart,” says the study’s co-author Zahi Fayad, PhD, Director of the BioMedical Engineering and Imaging Institute, Co-Founder of the MSCIC, and the Lucy G. Moses Professor of Medical Imaging and Bioengineering at the Icahn School of Medicine at Mount Sinai.
The Warrior Watch Study draws on the collaborative effort of the Hasso Plattner Institute for Digital Health and the MSCIC, which represents a diverse group of data scientists, engineers, clinical physicians, and researchers across the Mount Sinai Health System who joined together in the spring of 2020 to combat COVID-19. The study will next take a closer look at biometrics including HRV, sleep disruption, and physical activity to better understand which health care workers are at risk of the psychological effects of the pandemic. More

Soft robots may not be in touch with human feelings, but they are getting better at feeling human touch.
Cornell University researchers have created a low-cost method for soft, deformable robots to detect a range of physical interactions, from pats to punches to hugs, without relying on touch at all. Instead, a USB camera located inside the robot captures the shadow movements of hand gestures on the robot’s skin and classifies them with machine-learning software.
The group’s paper, “ShadowSense: Detecting Human Touch in a Social Robot Using Shadow Image Classification,” published in the Proceedings of the Association for Computing Machinery on Interactive, Mobile, Wearable and Ubiquitous Technologies. The paper’s lead author is doctoral student, Yuhan Hu.
The new ShadowSense technology is the latest project from the Human-Robot Collaboration and Companionship Lab, led by the paper’s senior author, Guy Hoffman, associate professor in the Sibley School of Mechanical and Aerospace Engineering.
The technology originated as part of an effort to develop inflatable robots that could guide people to safety during emergency evacuations. Such a robot would need to be able to communicate with humans in extreme conditions and environments. Imagine a robot physically leading someone down a noisy, smoke-filled corridor by detecting the pressure of the person’s hand.
Rather than installing a large number of contact sensors — which would add weight and complex wiring to the robot, and would be difficult to embed in a deforming skin — the team took a counterintuitive approach. In order to gauge touch, they looked to sight.

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“By placing a camera inside the robot, we can infer how the person is touching it and what the person’s intent is just by looking at the shadow images,” Hu said. “We think there is interesting potential there, because there are lots of social robots that are not able to detect touch gestures.”
The prototype robot consists of a soft inflatable bladder of nylon skin stretched around a cylindrical skeleton, roughly four feet in height, that is mounted on a mobile base. Under the robot’s skin is a USB camera, which connects to a laptop. The researchers developed a neural-network-based algorithm that uses previously recorded training data to distinguish between six touch gestures — touching with a palm, punching, touching with two hands, hugging, pointing and not touching at all — with an accuracy of 87.5 to 96%, depending on the lighting.
The robot can be programmed to respond to certain touches and gestures, such as rolling away or issuing a message through a loudspeaker. And the robot’s skin has the potential to be turned into an interactive screen.
By collecting enough data, a robot could be trained to recognize an even wider vocabulary of interactions, custom-tailored to fit the robot’s task, Hu said.
The robot doesn’t even have to be a robot. ShadowSense technology can be incorporated into other materials, such as balloons, turning them into touch-sensitive devices.
In addition to providing a simple solution to a complicated technical challenge, and making robots more user-friendly to boot, ShadowSense offers a comfort that is increasingly rare in these high-tech times: privacy.
“If the robot can only see you in the form of your shadow, it can detect what you’re doing without taking high fidelity images of your appearance,” Hu said. “That gives you a physical filter and protection, and provides psychological comfort.”
The research was supported by the National Science Foundation’s National Robotic Initiative.

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Materials provided by Cornell University. Original written by David Nutt. Note: Content may be edited for style and length. More

Systems designed to detect deepfakes — videos that manipulate real-life footage via artificial intelligence — can be deceived, computer scientists showed for the first time at the WACV 2021 conference which took place online Jan. 5 to 9, 2021.
Researchers showed detectors can be defeated by inserting inputs called adversarial examples into every video frame. The adversarial examples are slightly manipulated inputs which cause artificial intelligence systems such as machine learning models to make a mistake. In addition, the team showed that the attack still works after videos are compressed.
“Our work shows that attacks on deepfake detectors could be a real-world threat,” said Shehzeen Hussain, a UC San Diego computer engineering Ph.D. student and first co-author on the WACV paper. “More alarmingly, we demonstrate that it’s possible to craft robust adversarial deepfakes in even when an adversary may not be aware of the inner workings of the machine learning model used by the detector.”
In deepfakes, a subject’s face is modified in order to create convincingly realistic footage of events that never actually happened. As a result, typical deepfake detectors focus on the face in videos: first tracking it and then passing on the cropped face data to a neural network that determines whether it is real or fake. For example, eye blinking is not reproduced well in deepfakes, so detectors focus on eye movements as one way to make that determination. State-of-the-art Deepfake detectors rely on machine learning models for identifying fake videos.
The extensive spread of fake videos through social media platforms has raised significant concerns worldwide, particularly hampering the credibility of digital media, the researchers point out. “”If the attackers have some knowledge of the detection system, they can design inputs to target the blind spots of the detector and bypass it,” ” said Paarth Neekhara, the paper’s other first coauthor and a UC San Diego computer science student.
Researchers created an adversarial example for every face in a video frame. But while standard operations such as compressing and resizing video usually remove adversarial examples from an image, these examples are built to withstand these processes. The attack algorithm does this by estimating over a set of input transformations how the model ranks images as real or fake. From there, it uses this estimation to transform images in such a way that the adversarial image remains effective even after compression and decompression.??

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The modified version of the face is then inserted in all the video frames. The process is then repeated for all frames in the video to create a deepfake video. The attack can also be applied on detectors that operate on entire video frames as opposed to just face crops.
The team declined to release their code so it wouldn’t be used by hostile parties.
High success rate
Researchers tested their attacks in two scenarios: one where the attackers have complete access to the detector model, including the face extraction pipeline and the architecture and parameters of the classification model; and one where attackers can only query the machine 
 learning model to figure out the probabilities of a frame being classified as real or fake. In the first scenario, the attack’s success rate is above 99 percent for uncompressed videos. For compressed videos, it was 84.96 percent. In the second scenario, the success rate was 86.43 percent for uncompressed and 78.33 percent for compressed videos. This is the first work which demonstrates successful attacks on state-of-the-art deepfake detectors.
“To use these deepfake detectors in practice, we argue that it is essential to evaluate them against an adaptive adversary who is aware of these defenses and is intentionally trying to foil these defenses,”? the researchers write. “We show that the current state of the art methods for deepfake detection can be easily bypassed if the adversary has complete or even partial knowledge of the detector.”
To improve detectors, researchers recommend an approach similar to what is known as adversarial training: during training, an adaptive adversary continues to generate new deepfakes that can bypass the current state of the art detector; and the detector continues improving in order to detect the new deepfakes.
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples
*Shehzeen Hussain, Malhar Jere, Farinaz Koushanfar, Department of Electrical and Computer Engineering, UC San Diego Paarth Neekhara, Julian McAuley, Department of Computer Science and Engineering, UC San Diego More

Floating sea macro-litter is a threat to the conservation of marine ecosystems worldwide. The largest density of floating litter is in the great ocean gyres — systems of circular currents that spin and catch litter — but the polluting waste is abundant in coastal waters and semi closed seas such as the Mediterranean.
MARLIT, an open access web app based on an algorithm designed with deep learning techniques, will enable the detection and quantification of floating plastics in the sea with a reliability over 80%, according to a study published in the journal Environmental Pollution and carried out by experts of the Faculty of Biology and the Biodiversity Research Institute of the University of Barcelona (IRBio).
This methodology results from the analysis through artificial intelligence techniques of more than 3,800 aerial images of the Mediterranean coast in Catalonia, and it will allow researchers to make progress in the assessment of the presence, density and distribution of the plastic pollutants in the seas and oceans worldwide. Among the participants in the study, published in the journal Environmental Pollution, are the experts of the Consolidated Research Group on Large Marine Vertebrates of the UB and IRBio, and the Research Group on Biostatistics and Bioinformatics (GRBIO) of the UB, integrated in the Bioinformatics Barcelona platform (BIB).
Litter that floats and pollutes the ocean
Historically, direct observations (boats, planes, etc.) are the base for the common methodology to assess the impact of floating marine macro-litter (FMML). However, the great ocean area and the volume of data make it hard for the researchers to advance with the monitoring studies.
“Automatic aerial photography techniques combined with analytical algorithms are more efficient protocols for the control and study of this kind of pollutants,” notes Odei Garcia-Garin, first author of the article and member of the CRG on Large Marine Mammals, led by Professor Àlex Aguilar.

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“However,” he continues, “automated remote sensing of these materials is at an early stage. There are several factors in the ocean (waves, wind, clouds, etc.) that harden the detection of floating litter automatically with the aerial images of the marine surface. This is why there are only a few studies that made the effort to work on algorithms to apply to this new research context.”
The experts designed a new algorithm to automate the quantification of floating plastics in the sea through aerial photographs by applying the deep learning techniques, automatic learning methodology with artificial neuronal networks able to learn and take the learning to higher levels.
“The great amount of images of the marine surface obtained by drones and planes in monitoring campaigns on marine litter -also in experimental studies with known floating objects- enabled us to develop and test a new algorithm that reaches a 80% of precision in the remote sensing of floating marine macro-litter,” notes Garcia-Garin, member of the Department of Evolutionary Biology, Ecology and Environmental Sciences of the UB and IRBio.
Preservation of the oceans with deep learning techniques
The new algorithm has been implemented to MARLIT, an open access web app described in the article and which is available to all managers and professionals in the study of the detection and quantification of floating marine macro-litter with aerial images. In particular, this is a proof of concept based on an R Shiny package, a methodological innovation with great interest to speed up the monitoring procedures of floating marine macro-litter.
MARLIT enables the analysis of images individually, as well as to divide them into several segments, according to the user’s guidelines, identify the presence of floating litter in each certain area and estimate their density with the image metadata (height, resolution). In the future, it is expected to adapt the app to a remote sensor (for instance, a drone) to automate the remote sensing process.
At a European level, the EU Marine Strategy Framework Directive indicates the application of FMML monitoring techniques to fulfill the continuous assessment of the environmental state of the marine environment. “Therefore, the automatization of monitoring processes and the use of apps such as MARLIT would ease the member states’ fulfilment of the directive,” conclude the authors of the study. More

A new machine-learning framework uses reported test results and death rates to calculate estimates of the actual number of current COVID-19 infections within all 50 U.S. states and 50 countries. Jungsik Noh and Gaudenz Danuser of the University of Texas Southwestern Medical Center present these findings in the open-access journal PLOS ONE on February 8, 2021.
During the ongoing pandemic, U.S. states and many countries have reported daily counts of COVID-19 infections and deaths confirmed by testing. However, many infections have gone undetected, resulting in under-counting of the total number of people currently infected at any given point in time — an important metric to guide public health efforts.
Now, Noh and Danuser have developed a computational model that uses machine-learning strategies to estimate the actual daily number of current infections for all 50 U.S. states and the 50 most-infected countries. To make the calculations, the model draws on previously published pandemic parameters and publicly available daily data on confirmed cases and deaths. Visualizations of these daily estimates are freely available online.
The model’s estimates indicate severe undercounting of cases across the U.S. and worldwide. The cumulative number of actual cases in 9 out of 50 countries is estimated to be at least five times higher than confirmed cases. Within the U.S., estimates of the cumulative number of actual cases within states were in line with the results of an antibody testing study conducted in 46 states.
For some countries, such as the U.S., Belgium, and the U.K., estimates indicate that more than 20 percent of the total population has experienced infection. As of January 31, 2021, some U.S. states — including Pennsylvania, Arizona, and Florida — have currently active cases totaling more than 5 percent of the state’s entire population. In Washington, the active cases were estimated to be one percent of the population that day.
Looking ahead, the model has been estimating current COVID-19 case counts within communities, which could help inform contact-tracing and other public health efforts.
The authors add: “Given that the confirmed cases only capture the tip of the iceberg in the middle of the pandemic, the estimated sizes of current infections in this study provide crucial information to determine the regional severity of COVID-19 that can be misguided by the confirmed cases.”

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Materials provided by PLOS. Note: Content may be edited for style and length. More

Cold Spring Harbor Laboratory (CSHL) Assistant Professor Peter Koo and collaborator Matt Ploenzke reported a way to train machines to predict the function of DNA sequences. They used “neural nets,” a type of artificial intelligence (AI) typically used to classify images. Teaching the neural net to predict the function of short stretches of DNA allowed it to work up to deciphering larger patterns. The researchers hope to analyze more complex DNA sequences that regulate gene activity critical to development and disease.
Machine-learning researchers can train a brain-like “neural net” computer to recognize objects, such as cats or airplanes, by showing it many images of each. Testing the success of training requires showing the machine a new picture of a cat or an airplane and seeing if it classifies it correctly. But, when researchers apply this technology to analyzing DNA patterns, they have a problem. Humans can’t recognize the patterns, so they may not be able to tell if the computer identifies the right thing. Neural nets learn and make decisions independently of their human programmers. Researchers refer to this hidden process as a “black box.” It is hard to trust the machine’s outputs if we don’t know what is happening in the box.
Koo and his team fed DNA (genomic) sequences into a specific kind of neural network called a convolutional neural network (CNN), which resembles how animal brains process images. Koo says:
“It can be quite easy to interpret these neural networks because they’ll just point to, let’s say, whiskers of a cat. And so that’s why it’s a cat versus an airplane. In genomics, it’s not so straightforward because genomic sequences aren’t in a form where humans really understand any of the patterns that these neural networks point to.”
Koo’s research, reported in the journal Nature Machine Intelligence, introduced a new method to teach important DNA patterns to one layer of his CNN. This allowed his neural network to build on the data to identify more complex patterns. Koo’s discovery makes it possible to peek inside the black box and identify some key features that lead to the computer’s decision-making process.
But Koo has a larger purpose in mind for the field of artificial intelligence. There are two ways to improve a neural net: interpretability and robustness. Interpretability refers to the ability of humans to decipher why machines give a certain prediction. The ability to produce an answer even with mistakes in the data is called robustness. Usually, researchers focus on one or the other. Koo says:
“What my research is trying to do is bridge these two together because I don’t think they’re separate entities. I think that we get better interpretability if our models are more robust.”
Koo hopes that if a machine can find robust and interpretable DNA patterns related to gene regulation, it will help geneticists understand how mutations affect cancer and other diseases.

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Materials provided by Cold Spring Harbor Laboratory. Original written by Jasmine Lee. Note: Content may be edited for style and length. More

Researchers have identified a new form of magnetism in so-called magnetic graphene, which could point the way toward understanding superconductivity in this unusual type of material.
The researchers, led by the University of Cambridge, were able to control the conductivity and magnetism of iron thiophosphate (FePS3), a two-dimensional material which undergoes a transition from an insulator to a metal when compressed. This class of magnetic materials offers new routes to understanding the physics of new magnetic states and superconductivity.
Using new high-pressure techniques, the researchers have shown what happens to magnetic graphene during the transition from insulator to conductor and into its unconventional metallic state, realised only under ultra-high pressure conditions. When the material becomes metallic, it remains magnetic, which is contrary to previous results and provides clues as to how the electrical conduction in the metallic phase works. The newly discovered high-pressure magnetic phase likely forms a precursor to superconductivity so understanding its mechanisms is vital.
Their results, published in the journal Physical Review X, also suggest a way that new materials could be engineered to have combined conduction and magnetic properties, which could be useful in the development of new technologies such as spintronics, which could transform the way in which computers process information.
Properties of matter can alter dramatically with changing dimensionality. For example, graphene, carbon nanotubes, graphite and diamond are all made of carbon atoms, but have very different properties due to their different structure and dimensionality.
“But imagine if you were also able to change all of these properties by adding magnetism,” said first author Dr Matthew Coak, who is jointly based at Cambridge’s Cavendish Laboratory and the University of Warwick. “A material which could be mechanically flexible and form a new kind of circuit to store information and perform computation. This is why these materials are so interesting, and because they drastically change their properties when put under pressure so we can control their behaviour.”
In a previous study by Sebastian Haines of Cambridge’s Cavendish Laboratory and the Department of Earth Sciences, researchers established that the material becomes a metal at high pressure, and outlined how the crystal structure and arrangement of atoms in the layers of this 2D material change through the transition.

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“The missing piece has remained however, the magnetism,” said Coak. “With no experimental techniques able to probe the signatures of magnetism in this material at pressures this high, our international team had to develop and test our own new techniques to make it possible.”
The researchers used new techniques to measure the magnetic structure up to record-breaking high pressures, using specially designed diamond anvils and neutrons to act as the probe of magnetism. They were then able to follow the evolution of the magnetism into the metallic state.
“To our surprise, we found that the magnetism survives and is in some ways strengthened,” co-author Dr Siddharth Saxena, group leader at the Cavendish Laboratory. “This is unexpected, as the newly-freely-roaming electrons in a newly conducting material can no longer be locked to their parent iron atoms, generating magnetic moments there — unless the conduction is coming from an unexpected source.”
In their previous paper, the researchers showed these electrons were ‘frozen’ in a sense. But when they made them flow or move, they started interacting more and more. The magnetism survives, but gets modified into new forms, giving rise to new quantum properties in a new type of magnetic metal.
How a material behaves, whether conductor or insulator, is mostly based on how the electrons, or charge, move around. However, the ‘spin’ of the electrons has been shown to be the source of magnetism. Spin makes electrons behave a bit like tiny bar magnets and point a certain way. Magnetism from the arrangement of electron spins is used in most memory devices: harnessing and controlling it is important for developing new technologies such as spintronics, which could transform the way in which computers process information.

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“The combination of the two, the charge and the spin, is key to how this material behaves,” said co-author Dr David Jarvis from the Institut Laue-Langevin, France, who carried out this work as the basis of his PhD studies at the Cavendish Laboratory. “Finding this sort of quantum multi-functionality is another leap forward in the study of these materials.”
“We don’t know exactly what’s happening at the quantum level, but at the same time, we can manipulate it,” said Saxena. “It’s like those famous ‘unknown unknowns’: we’ve opened up a new door to properties of quantum information, but we don’t yet know what those properties might be.”
There are more potential chemical compounds to synthesise than could ever be fully explored and characterised. But by carefully selecting and tuning materials with special properties, it is possible to show the way towards the creation of compounds and systems, but without having to apply huge amounts of pressure.
Additionally, gaining fundamental understanding of phenomena such as low-dimensional magnetism and superconductivity allows researchers to make the next leaps in materials science and engineering, with particular potential in energy efficiency, generation and storage.
As for the case of magnetic graphene, the researchers next plan to continue the search for superconductivity within this unique material. “Now that we have some idea what happens to this material at high pressure, we can make some predictions about what might happen if we try to tune its properties through adding free electrons by compressing it further,” said Coak.
“The thing we’re chasing is superconductivity,” said Saxena. “If we can find a type of superconductivity that’s related to magnetism in a two-dimensional material, it could give us a shot at solving a problem that’s gone back decades.” More

An important class of challenging computational problems, with applications in graph theory, neural networks, artificial intelligence and error-correcting codes can be solved by multiplying light signals, according to researchers from the University of Cambridge and Skolkovo Institute of Science and Technology in Russia.
In a paper published in the journal Physical Review Letters, they propose a new type of computation that could revolutionise analogue computing by dramatically reducing the number of light signals needed while simplifying the search for the best mathematical solutions, allowing for ultra-fast optical computers.
Optical or photonic computing uses photons produced by lasers or diodes for computation, as opposed to classical computers which use electrons. Since photons are essentially without mass and can travel faster than electrons, an optical computer would be superfast, energy-efficient and able to process information simultaneously through multiple temporal or spatial optical channels.
The computing element in an optical computer — an alternative to the ones and zeroes of a digital computer — is represented by the continuous phase of the light signal, and the computation is normally achieved by adding two light waves coming from two different sources and then projecting the result onto ‘0’ or ‘1’ states.
However, real life presents highly nonlinear problems, where multiple unknowns simultaneously change the values of other unknowns while interacting multiplicatively. In this case, the traditional approach to optical computing that combines light waves in a linear manner fails.
Now, Professor Natalia Berloff from Cambridge’s Department of Applied Mathematics and Theoretical Physics and PhD student Nikita Stroev from Skolkovo Institute of Science and Technology have found that optical systems can combine light by multiplying the wave functions describing the light waves instead of adding them and may represent a different type of connections between the light waves.

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They illustrated this phenomenon with quasi-particles called polaritons — which are half-light and half-matter — while extending the idea to a larger class of optical systems such as light pulses in a fibre. Tiny pulses or blobs of coherent, superfast-moving polaritons can be created in space and overlap with one another in a nonlinear way, due to the matter component of polaritons.
“We found the key ingredient is how you couple the pulses with each other,” said Stroev. “If you get the coupling and light intensity right, the light multiplies, affecting the phases of the individual pulses, giving away the answer to the problem. This makes it possible to use light to solve nonlinear problems.”
The multiplication of the wave functions to determine the phase of the light signal in each element of these optical systems comes from the nonlinearity that occurs naturally or is externally introduced into the system.
“What came as a surprise is that there is no need to project the continuous light phases onto ‘0’ and ‘1’ states necessary for solving problems in binary variables,” said Stroev. “Instead, the system tends to bring about these states at the end of its search for the minimum energy configuration. This is the property that comes from multiplying the light signals. On the contrary, previous optical machines require resonant excitation that fixes the phases to binary values externally.”
The authors have also suggested and implemented a way to guide the system trajectories towards the solution by temporarily changing the coupling strengths of the signals.
“We should start identifying different classes of problems that can be solved directly by a dedicated physical processor,” said Berloff. “Higher-order binary optimisation problems are one such class, and optical systems can be made very efficient in solving them.”
There are still many challenges to be met before optical computing can demonstrate its superiority in solving hard problems in comparison with modern electronic computers: noise reduction, error correction, improved scalability, guiding the system to the true best solution are among them.
“Changing our framework to directly address different types of problems may bring optical computing machines closer to solving real-world problems that cannot be solved by classical computers,” said Berloff. More