Aurelia Butler

The past may be a fixed and immutable point, but with the help of machine learning, the future can at times be more easily divined.
Using a new type of machine learning method called next generation reservoir computing, researchers at The Ohio State University have recently found a new way to predict the behavior of spatiotemporal chaotic systems — such as changes in Earth’s weather — that are particularly complex for scientists to forecast.
The study, published today in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science, utilizes a new and highly efficient algorithm that, when combined with next generation reservoir computing, can learn spatiotemporal chaotic systems in a fraction of the time of other machine learning algorithms.
Researchers tested their algorithm on a complex problem that has been studied many times in the past — forecasting the behavior of an atmospheric weather model. In comparison to traditional machine learning algorithms that can solve the same tasks, the Ohio State team’s algorithm is more accurate, and uses 400 to 1,250 times less training data to make better predictions than its counterpart. Their method is also less computationally expensive; while solving complex computing problems previously required a supercomputer, they used a laptop running Windows 10 to make predictions in about a fraction of a second — about 240,000 times faster than traditional machine learning algorithms.
“This is very exciting, as we believe it’s a substantial advance in terms of data processing efficiency and prediction accuracy in the field of machine learning,” said Wendson De Sa Barbosa, lead author and a postdoctoral researcher in physics at Ohio State. He said that learning to predict these extremely chaotic systems is a “physics grand challenge,” and understanding them could pave the way to new scientific discoveries and breakthroughs.
“Modern machine learning algorithms are especially well-suited for predicting dynamical systems by learning their underlying physical rules using historical data,” said De Sa Barbosa. “Once you have enough data and computational power, you can make predictions with machine learning models about any real-world complex system.” Such systems can include any physical process, from the bob of a clock’s pendulum to disruptions in power grids.
Even heart cells display chaotic spatial patterns when they oscillate at an abnormally higher frequency than a normal heartbeat, said De Sa Barbosa. That means this research could one day be used to provide better insight to controlling and interpreting heart disease, as well as a bevy of other “real-world” problems.
“If one knows the equations that accurately describe how these unique processes for a system will evolve, then its behavior could be reproduced and predicted,” he said. Simple movements, like the swing position of a clock, can be predicted easily using only its current position and velocity. Yet more complex systems, like Earth’s weather, are far more difficult to foresee due to how many variables actively dictate its chaotic behavior.
To make precise predictions of the entire system, scientists would have to have accurate information about every single one of these variables, and the model equations that describe how these many variables are related, which is altogether impossible, said De Sa Barbosa. But with their machine learning algorithm, the almost 500,000 historical training data points used in previous works for the atmospheric weather example used in this study could be reduced to only 400, while still achieving the same or better accuracy.
Going forward, De Sa Barbosa aims to further his research by using their algorithm to possibly speed up spatiotemporal simulations, he said.
“We live in a world that we still know so little about, so it’s important to recognize these high-dynamical systems and learn how to more efficiently predict them.”
The co-author of the study was Daniel J. Gauthier, a professor of physics at Ohio State. Their work was supported by the Air Force Office of Scientific Research.
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Materials provided by Ohio State University. Original written by Tatyana Woodall. Note: Content may be edited for style and length. More

Researchers at QuTech — a collaboration between the Delft University of Technology and TNO — have engineered a record number of six, silicon-based, spin qubits in a fully interoperable array. Importantly, the qubits can be operated with a low error-rate that is achieved with a new chip design, an automated calibration procedure, and new methods for qubit initialization and readout. These advances will contribute to a scalable quantum computer based on silicon. The results are published in Nature today.
Different materials can be used to produce qubits, the quantum analogue to the bit of the classical computer, but no one knows which material will turn out to be best to build a large-scale quantum computer. To-date there have only been smaller demonstrations of silicon quantum chips with high quality qubit operations. Now, researchers from QuTech, led by Prof. Lieven Vandersypen, have produced a six qubit chip in silicon that operates with low error-rates. This is a major step towards a fault-tolerant quantum computer using silicon.
To make the qubits, individual electrons are placed in a linear array of six ‘quantum dots’ spaced 90 nanometers apart. The array of quantum dots is made in a silicon chip with structures that closely resemble the transistor — a common component in every computer chip. A quantum mechanical property called spin is used to define a qubit with its orientation defining the 0 or 1 logical state. The team used finely-tuned microwave radiation, magnetic fields, and electric potentials to control and measure the spin of individual electrons and make them interact with each other.
“The quantum computing challenge today consists of two parts,” explained first author Mr. Stephan Philips. “Developing qubits that are of good enough quality, and developing an architecture that allows one to build large systems of qubits. Our work fits into both categories. And since the overall goal of building a quantum computer is an enormous effort, I think it is fair to say we have made a contribution in the right direction.”
The electron’s spin is a delicate property. Tiny changes in the electromagnetic environment cause the direction of spin to fluctuate, and this increases the error rate. The QuTech team built upon their previous experience engineering quantum dots with new methods for preparing, controlling, and reading the spin states of electrons. Using this new arrangement of qubits they could create logic gates and entangle systems of two or three electrons, on demand.
Quantum arrays with over 50 qubits have been produced using superconducting qubits. It is the global availability of silicon engineering infrastructure however, which gives silicon quantum devices the promise of easier migration from research to industry. Silicon brings certain engineering challenges, and until this work from the QuTech team only arrays of up to three qubits could be engineered in silicon without sacrificing quality.
“This paper shows that with careful engineering, it is possible to increase the silicon spin qubit count while keeping the same precision as for single qubits. The key building block developed in this research could be used to add even more qubits in the next iterations of study,” said co-author Dr. Mateusz Madzik.
“In this research we push the envelope of the number of qubits in silicon, and achieve high initialization fidelities, high readout fidelities, high single-qubit gate fidelities, and high two-qubit state fidelities,” said Prof. Vandersypen. “What really stands out though is that we demonstrate all these characteristics together in one single experiment on a record number of qubits.”
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A team of researchers from CERN, Massachusetts Institute of Technology, and Staffordshire University have implemented a ground-breaking algorithm for reconstructing particles at the Large Hadron Collider.
The Large Hadron Collider (LHC) is the most powerful particle accelerator ever built which sits in a tunnel 100 metres underground at CERN, the European Organisation for Nuclear Research, near Geneva in Switzerland. It is the site of long-running experiments which enable physicists worldwide to learn more about the nature of the Universe.
The project is part of the Compact Muon Solenoid (CMS) experiment — one of seven installed experiments which uses detectors to analyse the particles produced by collisions in the accelerator.
The subject of a new academic paper End-to-end multiple-particle reconstruction in high occupancy imaging calorimeters with graph neural networks published in European Physical Journal C, the project has been carried out ahead of the high luminosity upgrade of the Large Hadron Collider. The High Luminosity Large Hadron Collider (HL-LHC) project aims to crank up the performance of the LHC in order to increase the potential for discoveries after 2029. The HL-LHC will increase the number of proton-proton interactions in an event from 40 to 200.
Professor Raheel Nawaz, Pro Vice-Chancellor for Digital Transformation, at Staffordshire University, has supervised the research. He explained: “Limiting the increase of computing resource consumption at large pileups is a necessary step for the success of the HL-LHC physics programme and we are advocating the use of modern machine learning techniques to perform particle reconstruction as a possible solution to this problem.”
He added: “This project has been both a joy and a privilege to work on and is likely to dictate the future direction of research on particle reconstruction by using a more advanced AI-based solution.”
Dr Jan Kieseler from the Experimental Physics Department at CERN added: “This is the first single-shot reconstruction of about 1000 particles from and in an unprecedentedly challenging environment with 200 simultaneous interactions each proton-proton collision. Showing that this novel approach, combining dedicated graph neural network layers (GravNet) and training methods (Object Condensation), can be extended to such challenging tasks while staying within resource constraints represents an important milestone towards future particle reconstruction.”
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When self-propelling objects interact with each other, interesting phenomena can occur. Birds align with each other when they flock together. People at a concert spontaneously create vortices when they nudge and bump into each other. Fire ants work together to create rafts that float on the water’s surface.
While many of these interactions happen through direct contact, like the concert-goers’ nudging, some interactions can transmit through the material the objects are on or in — these are known as indirect interactions. For example, a bridge with pedestrians on it can transmit vibrations, like in the famous Millennium Bridge “wobbly bridge” instance.
While the results of direct interactions (like nudging) are of increasing interest and study, and the results of indirect interactions through mechanisms like vision are well-studied, researchers are still learning about indirect mechanical interactions (for example, how two rolling balls might influence each other’s movement on a trampoline by indenting the trampoline’s surface with their weight, thus exerting mechanical forces without touching).
Physicists are using small wheeled robots to better understand these indirect mechanical interactions, how they play a role in active matter, and how we can control them. Their findings, “Field-mediated locomotor dynamics on highly deformable surfaces” are recently published in the The Proceedings of the National Academy of Sciences (PNAS).
In the paper, led by Shengkai Li, former Ph.D. student in the School of Physics at Georgia Tech, now a Center for the Physics of Biological Function (CPBF) fellow at Princeton University, researchers illustrated that active matter on deformable surfaces can interact with others through non-contact force — then created a model to allow control of the collective behavior of moving objects on deformable surfaces through simple changes in the engineering of the robots.
Co-authors include Georgia Tech School of Physics co-authors Daniel Goldman, Dunn Family Professor; Gongjie Li, assistant professor; and graduate student Hussain Gynai — along with Pablo Laguna and Gabriella Small (University of Texas at Austin), Yasemin Ozkan-Aydin (University of Notre Dame), Jennifer Rieser (Emory University), Charles Xiao (University of California, Santa Barbara). More

One reason that it’s so difficult to deliver large protein drugs orally is that these drugs can’t pass through the mucus barrier that lines the digestive tract. This means that insulin and most other “biologic drugs” — drugs consisting of proteins or nucleic acids — have to be injected or administered in a hospital.
A new drug capsule developed at MIT may one day be able to replace those injections. The capsule has a robotic cap that spins and tunnels through the mucus barrier when it reaches the small intestine, allowing drugs carried by the capsule to pass into cells lining the intestine.
“By displacing the mucus, we can maximize the dispersion of the drug within a local area and enhance the absorption of both small molecules and macromolecules,” says Giovanni Traverso, the Karl van Tassel Career Development Assistant Professor of Mechanical Engineering at MIT and a gastroenterologist at Brigham and Women’s Hospital.
In a study appearing today in Science Robotics, the researchers demonstrated that they could use this approach to deliver insulin as well as vancomycin, an antibiotic peptide that currently has to be injected.
Shriya Srinivasan, a research affiliate at MIT’s Koch Institute for Integrative Cancer Research and a junior fellow at the Society of Fellows at Harvard University, is the lead author of the study.
Tunneling through
For several years, Traverso’s lab has been developing strategies to deliver protein drugs such as insulin orally. This is a difficult task because protein drugs tend to be broken down in acidic environment of the digestive tract, and they also have difficulty penetrating the mucus barrier that lines the tract. More

In a study published online this week, robotics researchers, engineers and materials scientists from Rice University and Harvard University showed it is possible to make programmable, nonelectronic circuits that control the actions of soft robots by processing information encoded in bursts of compressed air.
“Part of the beauty of this system is that we’re really able to reduce computation down to its base components,” said Rice undergraduate Colter Decker, lead author of the study in the Proceedings of the National Academy of Sciences. He said electronic control systems have been honed and refined for decades, and recreating computer circuitry “with analogs to pressure and flow rate instead of voltage and current” made it easier to incorporate pneumatic computation.
Decker, a senior majoring in mechanical engineering, constructed his soft robotic control system primarily from everyday materials like plastic drinking straws and rubber bands. Despite its simplicity, experiments showed the system’s air-driven logic gates could be configured to perform operations called Boolean functions that are the meat and potatoes of modern computing.
“The goal was never to entirely replace electronic computers,” Decker said. He said there are many cases where soft robots or wearables need only be programmed for a few simple movements, and it’s possible the technology demonstrated in the paper “would be much cheaper and safer for use and much more durable” than traditional electronic controls.
As a freshman, Decker began working in the lab of Daniel Preston, an assistant professor of mechanical engineering at Rice. Decker studied fluidic control systems and became interested in creating one when he won a competitive summer research fellowship that would allow him to spend a few months working in the lab of Harvard chemist and materials scientist George Whitesides. More

New research led by the National Institute for Health & Care Research (NIHR) Mental Health Policy Research Unit (MHPRU) at King’s College London and University College London (UCL), has shown that certain groups of people benefit from the freedom of choice that telemental health provides, but this is not true for all.
The research, published today in the Interactive Journal of Medical Research, investigates which telemental health approaches work (or do not work) for whom, in which contexts, and through which mechanisms. Telemental health was found to be effective overall, but researchers highlight that there is no ‘one size fits all’.
Telemental health (or telemedicine) is mental health care — patient care, administrative activities and health education — delivered via ‘telecommunications technologies’ e.g. video calls, telephone calls or SMS text messages. It has become increasingly widespread, as it can be useful in providing care to service users in remote communities, or during an emergency restricting face-to-face contact, such as the COVID-19 pandemic.
The study found telemental health can be effective in reducing treatment gaps and barriers, by improving access to mental health care across different service user groups (e.g. adult, child and adolescent, older adults, and ethnic minority groups) and across personal contexts (e.g. difficulty accessing services, caring responsibilities or condition). However, it is crucial that providers consider that there are a set of key factors which lead to variations in peoples’ response to telemental health; for example, variations in access to a private and confidential space, ability to develop therapeutic relationships, individual preferences and circumstances as well as the internet connection quality.
King’s researcher Dr Katherine Saunders, from NIHR MHPRU and joint lead author said, “We live in an increasingly digital world, and the COVID-19 pandemic accelerated the role of technology in mental health care. Our study found that, while certain groups do benefit from the opportunities telemental health can provide, it is not a one size fits all solution. Receiving telemental health requires access to a device, an internet connection and an understanding of technology. If real world barriers to telemental health are ignored in favour of wider implementation, we risk further embedding inequalities into our healthcare system.”
Important limitations have been reported that implementing telemental health could risk the reinforcement of pre-existing inequalities in service provision. Those who benefit less are people without access to internet or phone, those experiencing social and economic disadvantages, cognitive difficulties, auditory or visual impairments, or severe mental health problems (such as psychosis).
Professor Sonia Johnson from UCL and Director, NIHR MHPRU and senior author adds “Our research findings emphasise the importance of personal choice, privacy and safety, and therapeutic relationships in telemental health care. The review also identified particular service users likely to be disadvantaged by telemental health implementation. For those people, we recommend a need to ensure that face-to-face care of equivalent timeliness remains available”
The authors suggest the findings have implications across the board of clinical practice, service planning, policy and research. If telemental health is to be widely incorporated into routine care, a clear understanding is needed of when and for whom it is an acceptable and effective approach and when face-to-face care is needed.
Professor Alan Simpson, from King’s and Co-Director, NIHR MHPRU concludes “As well as reviewing a huge amount of research literature, in this study we also involved and consulted with many clinicians and users of mental health services. This included young people, those that worked in or used inpatient and crisis services, and those who had personal lived experience of telemental throughout the pandemic. This gives this research a relevance that will be of interest to policy makers, service providers and those working in and using our services.”
Merle Schlief, joint lead author from NIHR MHPRU at UCL said “Working entirely online to conduct this study gave us access to experts and stakeholders who we simply would not have been able to include if we had been working in-person, including people living and working internationally, and those who would have been unable to travel. This highlights one of the key strengths of technology.”
The authors recommend that guidelines and strategies are co-produced with service users and frontline staff are needed to optimize telemental health implementation in real-world settings.
The MHPRU is a joint enterprise between researchers at UCL and King’s College London with a national network of collaborators. We conduct research commissioned by the NIHR Policy Research Programme to help the Department of Health and Social Care and others involved in making nationwide plans for mental health services to make decisions based on good evidence. The MHPRU contributed research evidence to the national review of the Mental Health Act and is currently undertaking a number of studies. More

In a newly-published study, a team of researchers in Oxford University’s Department of Materials led by Harish Bhaskaran, Professor of Applied Nanomaterials, describe a breakthrough approach to pick up single nanowires from the growth substrate and place them on virtually any platform with sub-micron accuracy.
The innovative method uses novel tools, including ultra-thin filaments of polyethylene terephthalate (PET) with tapered nanoscale tips that are used to pick up individual nanowires. At this fine scale, adhesive van der Waals (tiny forces of attraction that occur between atoms and molecules) cause the nanowires to ‘jump’ into contact with the tips. The nanowires are then transferred to a transparent dome-shaped elastic stamp mounted on a glass slide. This stamp is then turned upside down and aligned with the device chip, with the nanowire then printed gently onto the surface.
Deposited nanowires showed strong adhesive qualities, remaining in place even when the device was immersed in liquid. The research team were also able to place nanowires on fragile substrates, such as ultra-thin 50 nanometre membranes, demonstrating the delicacy and versatility of the stamping technique.
In addition, the researchers used the method to build an optomechanical sensor (an instrument that uses laser light to measure vibrations) that was 20 times more sensitive than existing nanowire-based devices.
Nanowires, materials with diameters 1000 times smaller than a human hair and fascinating physical properties, could enable major advancements in many different fields, from energy harvesters and sensors, to information and quantum technologies. In particular, their minuscule size could allow the development of smaller transistors and miniaturised computer chips. A major obstacle, however, to realising the full potential of nanowires has been the inability to precisely position them within devices.
Most electronic device manufacturing techniques cannot tolerate the conditions needed to produce nanowires. Consequently, nanowires are usually grown on a separate substrate and then mechanically or chemically transferred to the device. In all existing nanowire transfer techniques, however, the nanowires are placed randomly onto the chip surface, which limits their application in commercial devices.
DPhil student Utku Emre Ali (Department of Materials), who developed the technique, said: ‘This new pick-and-place assembly process has enabled us to create first-of-its-kind devices in the nanowire realm. We believe that it will inexpensively advance nanowire research by allowing users to incorporate nanowires with existing on-chip platforms, be it electronic or photonic, unlocking physical properties that have not been attainable so far. Furthermore, this technique could be fully automated, making full-scale fabrication of high quality nanowire-integrated chips a real possibility.’
Professor Harish Bhaskaran (Department of Materials) added: ‘This technique is readily scalable to larger areas, and brings the promise of nanowires to devices made on any substrate and using any process. This is what makes this technique so powerful.’
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