Aurelia Butler

A special artificial intelligence (AI)-based computer algorithm created by Mount Sinai researchers was able to learn how to identify subtle changes in electrocardiograms (also known as ECGs or EKGs) to predict whether a patient was experiencing heart failure.
“We showed that deep-learning algorithms can recognize blood pumping problems on both sides of the heart from ECG waveform data,” said Benjamin S. Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences, a member of the Hasso Plattner Institute for Digital Health at Mount Sinai, and a senior author of the study published in the Journal of the American College of Cardiology: Cardiovascular Imaging. “Ordinarily, diagnosing these type of heart conditions requires expensive and time-consuming procedures. We hope that this algorithm will enable quicker diagnosis of heart failure.”
The study was led by Akhil Vaid, MD, a postdoctoral scholar who works in both the Glicksberg lab and one led by Girish N. Nadkarni, MD, MPH, CPH, Associate Professor of Medicine at the Icahn School of Medicine at Mount Sinai, Chief of the Division of Data-Driven and Digital Medicine (D3M), and a senior author of the study.
Affecting about 6.2 million Americans, heart failure, or congestive heart failure, occurs when the heart pumps less blood than the body normally needs. For years doctors have relied heavily on an imaging technique called an echocardiogram to assess whether a patient may be experiencing heart failure. While helpful, echocardiograms can be labor-intensive procedures that are only offered at select hospitals.
However, recent breakthroughs in artificial intelligence suggest that electrocardiograms — a widely used electrical recording device — could be a fast and readily available alternative in these cases. For instance, many studies have shown how a “deep-learning” algorithm can detect weakness in the heart’s left ventricle, which pushes freshly oxygenated blood out to the rest of the body. In this study, the researchers described the development of an algorithm that not only assessed the strength of the left ventricle but also the right ventricle, which takes deoxygenated blood streaming in from the body and pumps it to the lungs.
“Although appealing, traditionally it has been challenging for physicians to use ECGs to diagnose heart failure. This is partly because there is no established diagnostic criteria for these assessments and because some changes in ECG readouts are simply too subtle for the human eye to detect,” said Dr. Nadkarni. “This study represents an exciting step forward in finding information hidden within the ECG data which can lead to better screening and treatment paradigms using a relatively simple and widely available test.”
Typically, an electrocardiogram involves a two-step process. Wire leads are taped to different parts of a patient’s chest and within minutes a specially designed, portable machine prints out a series of squiggly lines, or waveforms, representing the heart’s electrical activity. These machines can be found in most hospitals and ambulances throughout the United States and require minimal training to operate. More

Convolutional neural networks running on quantum computers have generated significant buzz for their potential to analyze quantum data better than classical computers can. While a fundamental solvability problem known as “barren plateaus” has limited the application of these neural networks for large data sets, new research overcomes that Achilles heel with a rigorous proof that guarantees scalability.
“The way you construct a quantum neural network can lead to a barren plateau — or not,” said Marco Cerezo, coauthor of the paper titled “Absence of Barren Plateaus in Quantum Convolutional Neural Networks,” published today by a Los Alamos National Laboratory team in Physical Review X. Cerezo is a physicist specializing in quantum computing, quantum machine learning, and quantum information at Los Alamos. “We proved the absence of barren plateaus for a special type of quantum neural network. Our work provides trainability guarantees for this architecture, meaning that one can generically train its parameters.”
As an artificial intelligence (AI) methodology, quantum convolutional neural networks are inspired by the visual cortex. As such, they involve a series of convolutional layers, or filters, interleaved with pooling layers that reduce the dimension of the data while keeping important features of a data set.
These neural networks can be used to solve a range of problems, from image recognition to materials discovery. Overcoming barren plateaus is key to extracting the full potential of quantum computers in AI applications and demonstrating their superiority over classical computers.
Until now, Cerezo said, researchers in quantum machine learning analyzed how to mitigate the effects of barren plateaus, but they lacked a theoretical basis for avoiding it altogether. The Los Alamos work shows how some quantum neural networks are, in fact, immune to barren plateaus.
“With this guarantee in hand, researchers will now be able to sift through quantum-computer data about quantum systems and use that information for studying material properties or discovering new materials, among other applications,” said Patrick Coles, a quantum physicist at Los Alamos and a coauthor of the paper. More

As a robotics engineer, Yasemin Ozkan-Aydin, assistant professor of electrical engineering at the University of Notre Dame, gets her inspiration from biological systems. The collective behaviors of ants, honeybees and birds to solve problems and overcome obstacles is something researchers have developed in aerial and underwater robotics. Developing small-scale swarm robots with the capability to traverse complex terrain, however, comes with a unique set of challenges.
In research published in Science Robotics, Ozkan-Aydin presents how she was able to build multi-legged robots capable of maneuvering in challenging environments and accomplishing difficult tasks collectively, mimicking their natural-world counterparts.
“Legged robots can navigate challenging environments such as rough terrain and tight spaces, and the use of limbs offers effective body support, enables rapid maneuverability and facilitates obstacle crossing,” Ozkan-Aydin said. “However, legged robots face unique mobility challenges in terrestrial environments, which results in reduced locomotor performance.”
For the study, Ozkan-Aydin said, she hypothesized that a physical connection between individual robots could enhance the mobility of a terrestrial legged collective system. Individual robots performed simple or small tasks such as moving over a smooth surface or carrying a light object, but if the task was beyond the capability of the single unit, the robots physically connected to each other to form a larger multi-legged system and collectively overcome issues.
“When ants collect or transport objects, if one comes upon an obstacle, the group works collectively to overcome that obstacle. If there’s a gap in the path, for example, they will form a bridge so the other ants can travel across — and that is the inspiration for this study,” she said. “Through robotics we’re able to gain a better understanding of the dynamics and collective behaviors of these biological systems and explore how we might be able to use this kind of technology in the future.”
Using a 3D printer, Ozkan-Aydin built four-legged robots measuring 15 to 20 centimeters, or roughly 6 to 8 inches, in length. Each was equipped with a lithium polymer battery, microcontroller and three sensors — a light sensor at the front and two magnetic touch sensors at the front and back, allowing the robots to connect to one another. Four flexible legs reduced the need for additional sensors and parts and gave the robots a level of mechanical intelligence, which helped when interacting with rough or uneven terrain. More

The central principle of superconductivity is that electrons form pairs. But can they also condense into foursomes? Recent findings have suggested they can, and a physicist at KTH Royal Institute of Technology today published the first experimental evidence of this quadrupling effect and the mechanism by which this state of matter occurs.
Reporting today in Nature Physics, Professor Egor Babaev and collaborators presented evidence of fermion quadrupling in a series of experimental measurements on the iron-based material, Ba1?xKxFe2As2. The results follow nearly 20 years after Babaev first predicted this kind of phenomenon, and eight years after he published a paper predicting that it could occur in the material.
The pairing of electrons enables the quantum state of superconductivity, a zero-resistance state of conductivity which is used in MRI scanners and quantum computing. It occurs within a material as a result of two electrons bonding rather than repelling each other, as they would in a vacuum. The phenomenon was first described in a theory by, Leon Cooper, John Bardeen and John Schrieffer, whose work was awarded the Nobel Prize in 1972.
So-called Cooper pairs are basically “opposites that attract.” Normally two electrons, which are negatively-charged subatomic particles, would strongly repel each other. But at low temperatures in a crystal they become loosely bound in pairs, giving rise to a robust long-range order. Currents of electron pairs no longer scatter from defects and obstacles and a conductor can lose all electrical resistance, becoming a new state of matter: a superconductor.
Only in recent years has the theoretical idea of four-fermion condensates become broadly accepted.
For a fermion quadrupling state to occur there has to be something that prevents condensation of pairs and prevents their flow without resistance, while allowing condensation of four-electron composites, Babaev says. More

Researchers led by the Institute of Scientific and Industrial Research (SANKEN) at Osaka University have trained a deep neural network to correctly determine the output state of quantum bits, despite environmental noise. The team’s novel approach may allow quantum computers to become much more widely used.
Modern computers are based on binary logic, in which each bit is constrained to be either a 1 or a 0. But thanks to the weird rules of quantum mechanics, new experimental systems can achieve increased computing power by allowing quantum bits, also called qubits, to be in “superpositions” of 1 and 0. For example, the spins of electrons confined to tiny islands called quantum dots can be oriented both up and down simultaneously. However, when the final state of a bit is read out, it reverts to the classical behavior of being one orientation or the other. To make quantum computing reliable enough for consumer use, new systems will need to be created that can accurately record the output of each qubit even if there is a lot of noise in the signal.
Now, a team of scientists led by SANKEN used a machine learning method called a deep neural network to discern the signal created by the spin orientation of electrons on quantum dots. “We developed a classifier based on deep neural network to precisely measure a qubit state even with noisy signals,” co-author Takafumi Fujita explains.
In the experimental system, only electrons with a particular spin orientation can leave a quantum dot. When this happens, a temporary “blip” of increased voltage is created. The team trained the machine learning algorithm to pick out these signals from among the noise. The deep neural network they used had a convolutional neural network to identify the important signal features, combined with a recurrent neural network to monitor the time-series data.
“Our approach simplified the learning process for adapting to strong interference that could vary based on the situation,” senior author Akira Oiwa says. The team first tested the robustness of the classifier by adding simulated noise and drift. Then, they trained the algorithm to work with actual data from an array of quantum dots, and achieved accuracy rates over 95%. The results of this research may allow for the high-fidelity measurement of large-scale arrays of qubits in future quantum computers.
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If the properties of materials can be reliably predicted, then the process of developing new products for a huge range of industries can be streamlined and accelerated. In a study published XXX in Advanced Intelligent Systems, researchers from The University of Tokyo Institute of Industrial Science used core-loss spectroscopy to determine the properties of organic molecules using machine learning.
The spectroscopy techniques energy loss near-edge structure (ELNES) and X-ray near-edge structure (XANES) are used to determine information about the electrons, and through that the atoms, in materials. They have high sensitivity and high resolution and have been used to investigate a range of materials from electronic devices to drug delivery systems.
However, connecting spectral data to the properties of a material — things like optical properties, electron conductivity, density, and stability — remains ambiguous. Machine learning (ML) approaches have been used to extract information for large complex sets of data. Such approaches use artificial neural networks, which are based on how our brains work, to constantly learn to solve problems. Although the group previously used ELNES/XANES spectra and ML to find out information about materials, what they found did not relate to the properties of the material itself. Therefore, the information could not be easily translated into developments.
Now the team has used ML to reveal information hidden in the simulated ELNES/XANES spectra of 22,155 organic molecules. “The ELNES/XANES spectra of the molecules, or their “descriptors” in this scenario, were then input into the system,” explains lead author Kakeru Kikumasa. “This descriptor is something that can be directly measured in experiments and can therefore be determined with high sensitivity and resolution. This method is highly beneficial for materials development because it has the potential to reveal where, when, and how certain material properties arise.”
A model created from the spectra alone was able to successfully predict what are known as intensive properties. However, it was unable to predict extensive properties, which are dependent on the molecular size. Therefore, to improve the prediction, the new model was constructed by including the ratios of three elements in relation to carbon (which is present in all organic molecules) as extra parameters to allow extensive properties such as the molecular weight to be correctly predicted.
“Our ML learning treatment of core-loss spectra provides accurate prediction of extensive material properties, such as internal energy and molecular weight. The link between core-loss spectra and extensive properties has previously never been made; however, artificial intelligence was able to unveil the hidden connections. Our approach might also be applied to predict the properties of new materials and functions” says senior author Teruyasu Mizoguchi. “We believe that our model will be a very useful tool for the high-throughput development of materials in a wide range of industries.”
The study, “Quantification of the Properties of Organic Molecules Using Core-Loss Spectra as Neural Network Descriptors,” was published in Advanced Intelligent Systems.
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Scientists have worked out how to best get DNA to communicate with membranes in our body, paving the way for the creation of ‘mini biological computers’ in droplets that have potential uses in biosensing and mRNA vaccines.
UNSW’s Dr Matthew Baker and the University of Sydney’s Dr Shelley Wickham co-led the study, published recently in Nucleic Acids Research.
It discovered the best way to design and build DNA ‘nanostructures’ to effectively manipulate synthetic liposomes — tiny bubbles which have traditionally been used to deliver drugs for cancer and other diseases.
But by modifying the shape, porosity and reactivity of liposomes, there are far greater applications, such as building small molecular systems that sense their environment and respond to a signal to release a cargo, such as a drug molecule when it nears its target.
Lead author Dr Matt Baker from UNSW’s School of Biotechnology and Biomolecular Sciences says the study discovered how to build “little blocks” out of DNA and worked out how best to label these blocks with cholesterol to get them to stick to lipids, the main constituents of plant and animal cells.
“One major application of our study is biosensing: you could stick some droplets in a person or patient, as it moves through the body it records local environment, processes this and delivers a result so you can ‘read out’, the local environment,” Dr Baker says. More

Assistant Professor Kang Hao Cheong and his research team from the Singapore University of Technology and Design (SUTD) have set out to apply concepts from quantum Parrondo’s paradox in search of a working protocol for semiclassical encryption. In a recent Physical Review Research letter, the team published the paper ‘Chaotic switching for quantum coin Parrondo’s games with application to encryption’ and discovered that chaotic switching for quantum coin Parrondo’s games has similar underlying ideas and working dynamics to encryption.
Parrondo’s paradox is a phenomenon where the switching of two losing games results in a winning outcome. In the two-sided quantum coin tossing game introduced by the authors, they showed in a previous work that random and certain periodic tossing of two quantum coins can turn a quantum walker’s expected position from a losing position into a fair and winning position, respectively. In such a game, the quantum walker is given a set of instructions on how to move depending on the outcome of the quantum coin toss.
Inspired by the underlying principles of this quantum game, Joel Lai, the lead author of the study from SUTD explained, “Suppose I present to you the outcome of the quantum walker at the end of 100 coin tosses, knowing the initial position, can you tell me the sequence of tosses that lead to this final outcome?” As it turns out, this task can either be very difficult or very easy. Lai added: “In the case of random switching, it is almost impossible to determine the sequence of tosses that lead to the end result. However, for periodic tossing, we could get the sequence of tosses rather easily, because a periodic sequence has structure and is deterministic.”
Random sequences have too much uncertainty, on the other hand, periodic sequences are deterministic. This led to the idea of incorporating chaotic sequences as a means to perform the switching. The authors discovered that using chaotic switching through a pre-generated chaotic sequence significantly enhances the work. For an observer who does not know parts of the information required to generate the chaotic sequence, deciphering the sequence of tosses is analogous to determining a random sequence. However, for an agent with information on how to generate the chaotic sequence, this is analogous to a periodic sequence. According to the authors, this information on generating the chaotic sequence is likened to the keys in encryption. Knowing just the keys and the final outcome (i.e. the encrypted message), this outcome can be inverted to obtain the original state of the quantum walker (i.e. the original message).
Assistant Professor Cheong, the senior author of the study remarked, “The introduction of chaotic switching, when combined with Parrondo’s paradox, extends the application of Parrondo’s paradox from simply a mathematical tool used in quantum information for classification or identification of the initial state and final outcome to one that has real-world engineering applications. Cheong added, “The development of a fully implementable quantum chaotic Parrondo’s game may also improve on our semiclassical framework and provide advances to bridge some of the problems still faced in quantum encryption.”
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