Computers Math

A new artificial-intelligence technology for heart imaging can potentially improve care for patients, allowing doctors to examine their hearts for scar tissue while eliminating the need for contrast injections required for traditional cardiovascular magnetic resonance imaging (CMR).
A team of researchers who developed the technology, including doctors at UVA Health, reports the success of the approach in a new article in the scientific journal Circulation. The team compared its AI approach, known as Virtual Native Enhancement (VNE), with contrast-enhanced CMR scans now used to monitor hypertrophic cardiomyopathy, the most common genetic heart condition. The researchers found that VNE produced higher-quality images and better captured evidence of scar in the heart, all without the need for injecting the standard contrast agent required for CMR.
“This is a potentially important advance, especially if it can be expanded to other patient groups,” said researcher Christopher Kramer, MD, the chief of the Division of Cardiovascular Medicine at UVA Health, Virginia’s only designated Center of Excellence by the Hypertrophic Cardiomyopathy Association. “Being able to identify scar in the heart, an important contributor to progression to heart failure and sudden cardiac death, without contrast, would be highly significant. CMR scans would be done without contrast, saving cost and any risk, albeit low, from the contrast agent.”
Imaging Hypertrophic Cardiomyopathy
Hypertrophic cardiomyopathy is the most common inheritable heart disease, and the most common cause of sudden cardiac death in young athletes. It causes the heart muscle to thicken and stiffen, reducing its ability to pump blood and requiring close monitoring by doctors.
The new VNE technology will allow doctors to image the heart more often and more quickly, the researchers say. It also may help doctors detect subtle changes in the heart earlier, though more testing is needed to confirm that.
The technology also would benefit patients who are allergic to the contrast agent injected for CMR, as well as patients with severely failing kidneys, a group that avoids the use of the agent.
The new approach works by using artificial intelligence to enhance “T1-maps” of the heart tissue created by magnetic resonance imaging (MRI). These maps are combined with enhanced MRI “cines,” which are like movies of moving tissue — in this case, the beating heart. Overlaying the two types of images creates the artificial VNE image
Based on these inputs, the technology can produce something virtually identical to the traditional contrast-enhanced CMR heart scans doctors are accustomed to reading — only better, the researchers conclude. “Avoiding the use of contrast and improving image quality in CMR would only help both patients and physicians down the line,” Kramer said.
While the new research examined VNE’s potential in patients with hypertrophic cardiomyopathy, the technology’s creators envision it being used for many other heart conditions as well.
“While currently validated in the HCM population, there is a clear pathway to extend the technology to a wider range of myocardial pathologies,” they write. “VNE has enormous potential to significantly improve clinical practice, reduce scan time and costs, and expand the reach of CMR in the near future.” More

Large-scale quantum networks have been proposed, but so far, they do not exist. Some components of what would make up such networks are being studied, but the control mechanism for such a large-scale network has not been developed. In AVS Quantum Science, by AIP Publishing, investigators outline how a time-sensitive network control plane could be a key component of a workable quantum network.
Quantum networks are similar to classical networks. Information travels through them, providing a means of communication between devices and over distances. Quantum networks move quantum bits of information, called qubits, through the network.
These qubits are usually photons. Through the quantum phenomena of superposition and entanglement, they can transmit much more information than classical bits, which are limited to logical states of 0 and 1, are able to. Successful long-distance transmission of a qubit requires precise control and timing.
In addition to the well-understood requirements of transmission distance and data rate, for quantum networks to be useful in a real-world setting there are at least two other requirements of industry that need to be considered.
One is real-time network control, specifically time-sensitive networking. This control method, which takes network traffic into account, has been used successfully in other types of networks, such as Ethernet, to ensure messages are transmitted and received at precise times. This is precisely what is required to control quantum networks.
The second requirement is cost. Large-scale adoption of an industrial quantum network will only happen if costs can be significantly reduced. One way to accomplish cost reduction is with photonic integrated circuits. More

Researchers in the life sciences who use machine learning for their studies should adopt standards that allow other researchers to reproduce their results, according to a comment article published today in the journal Nature Methods.
The authors explain that the standards are key to advancing scientific breakthroughs, making advances in knowledge, and ensuring research findings are reproducible from one group of scientists to the next. The standards would allow other groups of scientists to focus on the next breakthrough rather than spending time recreating the wheel built by the authors of the original study.
Casey S. Greene, PhD, director of the University of Colorado School of Medicine’s Center for Health AI, is a corresponding author of the article, which he co-authored with first author Benjamin J. Heil, a member of Greene’s research team, and researchers from the United States, Canada, and Europe.
“Ultimately all science requires trust — no scientist can reproduce the results from every paper they read,” Greene and his co-authors write. “The question, then, is how to ensure that machine-learning analyses in the life sciences can be trusted.”
Greene and his co-authors outline standards to qualify for one of three levels of accessibility: bronze, silver, and gold. These standards each set minimum levels for sharing study materials so that other life science researchers can trust the work and, if warranted, validate the work and build on it.
To qualify for a bronze standard, life science researchers would need to make their data, code, and models publicly available. In machine learning, computers learn from training data and having access to that data enables scientists to look for problems that can confound the process. The code tells future researchers how the computer was told to carry out the steps of the work.
In machine learning, the resulting model is critically important. For future researchers, knowing the original research team’s model is critical for understanding how it relates to the data it is supposed to analyze. Without access to the model, other researchers cannot determine biases that might influence the work. For example, it can be difficult to determine whether an algorithm favors one group of people over another.
“Being unable to examine a model also makes trusting it difficult,” the authors write.
The silver standard calls for the data, models, and code provided at the bronze level, and adds more information about the system in which to run the code. For the next scientists, that information makes it theoretically possible that they could duplicate the training process.
To qualify for the gold standard, researchers must add an “easy button” to their work to make it possible for future researchers to reproduce the previous analysis with a single command. The original researchers must automate all steps of their analysis so that “the burden of reproducing their work is as small as possible.” For the next scientists, this information makes it practically possible to duplicate the training process and either adapt or extend it.
Greene and his co-authors also offer recommendations for documenting the steps and sharing them.
The Nature Methods article is an important contribution to the continuing refinement of the use of machine learning and other data-analysis methods in health sciences and other fields where trust is particularly important. Greene is one of several leaders recently recruited by the CU School of Medicine to establish a program in developing and applying robust data science methodologies to advance biomedical research, education, and clinical care. More

For millennia, humanity has observed the changing phases of the Moon. The rise and fall of sunlight reflected off the Moon, as it presents its different faces to us, is known as a “phase curve.” Measuring phase curves of the Moon and Solar System planets is an ancient branch of astronomy that goes back at least a century. The shapes of these phase curves encode information on the surfaces and atmospheres of these celestial bodies. In modern times, astronomers have measured the phase curves of exoplanets using space telescopes such as Hubble, Spitzer, TESS and CHEOPS. These observations are compared with theoretical predictions. In order to do so, one needs a way of calculating these phase curves. It involves seeking a solution to a difficult mathematical problem concerning the physics of radiation.
Approaches for the calculation of phase curves have existed since the 18th century. The oldest of these solutions goes back to the Swiss mathematician, physicist and astronomer, Johann Heinrich Lambert, who lived in the 18th century. “Lambert’s law of reflection” is attributed to him. The problem of calculating reflected light from Solar System planets was posed by the American astronomer Henry Norris Russell in an influential 1916 paper. Another well-known 1981 solution is attributed to the American lunar scientist Bruce Hapke, who built on the classic work of the Indian-American Nobel laureate Subrahmanyan Chandrasekhar in 1960. Hapke pioneered the study of the Moon using mathematical solutions of phase curves. The Soviet physicist Viktor Sobolev also made important contributions to the study of reflected light from celestial bodies in his influential 1975 textbook. Inspired by the work of these scientists, theoretical astrophysicist Kevin Heng of the Center for Space and Habitability CSH at the University of Bern has discovered an entire family of new mathematical solutions for calculating phase curves. The paper, authored by Kevin Heng in collaboration with Brett Morris from the National Center of Competence in Research NCCR PlanetS — which the University of Bern manages together with the University of Geneva — and Daniel Kitzmann from the CSH, has just been published in Nature Astronomy.
Generally applicable solutions
“I was fortunate that this rich body of work had already been done by these great scientists. Hapke had discovered a simpler way to write down the classic solution of Chandrasekhar, who famously solved the radiative transfer equation for isotropic scattering. Sobolev had realised that one can study the problem in at least two mathematical coordinate systems.” Sara Seager brought the problem to Heng’s attention by her summary of it in her 2010 textbook.
By combining these insights, Heng was able to write down mathematical solutions for the strength of reflection (the albedo) and the shape of the phase curve, both completely on paper and without resorting to a computer. “The ground-breaking aspect of these solutions is that they are valid for any law of reflection, which means they can be used in very general ways. The defining moment came for me when I compared these pen-and-paper calculations to what other researchers had done using computer calculations. I was blown away by how well they matched,” said Heng.
Successful analysis of the phase curve of Jupiter
“What excites me is not just the discovery of new theory, but also its major implications for interpreting data,” says Heng. For example, the Cassini spacecraft measured phase curves of Jupiter in the early 2000s, but an in-depth analysis of the data had not previously been done, probably because the calculations were too computationally expensive. With this new family of solutions, Heng was able to analyze the Cassini phase curves and infer that the atmosphere of Jupiter is filled with clouds made up of large, irregular particles of different sizes. This parallel study has just been published by the Astrophysical Journal Letters, in collaboration with Cassini data expert and planetary scientist Liming Li of Houston University in Texas, U.S.A.
New possibilities for the analysis of data from space telescopes
“The ability to write down mathematical solutions for phase curves of reflected light on paper means that one can use them to analyze data in seconds,” said Heng. It opens up new ways of interpreting data that were previously infeasible. Heng is collaborating with Pierre Auclair-Desrotour (formerly CSH, currently at Paris Observatory) to further generalize these mathematical solutions. “Pierre Auclair-Desrotour is a more talented applied mathematician than I am, and we promise exciting results in the near future,” said Heng.
In the Nature Astronomy paper, Heng and his co-authors demonstrated a novel way of analyzing the phase curve of the exoplanet Kepler-7b from the Kepler space telescope. Brett Morris led the data analysis part of the paper. “Brett Morris leads the data analysis for the CHEOPS mission in my research group, and his modern data science approach was critical for successfully applying the mathematical solutions to real data,” explained Heng. They are currently collaborating with scientists from the American-led TESS space telescope to analyze TESS phase curve data. Heng envisions that these new solutions will lead to novel ways of analyzing phase curve data from the upcoming, 10-billion-dollar James Webb Space Telescope, which is due to launch later in 2021. “What excites me most of all is that these mathematical solutions will remain valid long after I am gone, and will probably make their way into standard textbooks,” said Heng.
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In a move that could one day free the world’s countertops from their snarl of charging cords, researchers at the University of Michigan and University of Tokyo have developed a system to safely deliver electricity over the air, potentially turning entire buildings into wireless charging zones.
Detailed in a new study published in Nature Electronics, the technology can deliver 50 watts of power using magnetic fields.
Study author Alanson Sample, U-M professor of computer science and engineering, says that in addition to untethering phones and laptops, the technology could also power implanted medical devices and open new possibilities for mobile robotics in homes and manufacturing facilities. The team is also working on implementing the system in spaces that are smaller than room-size, for example a toolbox that charges tools placed inside it.
“This really ups the power of the ubiquitous computing world — you could put a computer in anything without ever having to worry about charging or plugging in,” Sample said. “There are a lot of clinical applications as well; today’s heart implants, for example, require a wire that runs from the pump through the body to an external power supply. This could eliminate that, reducing the risk of infection and improving patients’ quality of life.”
The team, led by researchers at the University of Tokyo, demonstrated the technology in a purpose-built aluminum test room measuring approximately 10 feet by 10 feet. They wirelessly powered lamps, fans and cell phones that could draw current from anywhere in the room regardless of the placement of people and furniture.
The system is a major improvement over previous attempts at wireless charging systems, which used potentially harmful microwave radiation or required devices to be placed on dedicated charging pads, the researchers say. Instead, it uses a conductive surface on room walls and a conductive pole to generate magnetic fields. More

The Human Cell Atlas is the world’s largest, growing single-cell reference atlas. It contains references of millions of cells across tissues, organs and developmental stages. These references help physicians to understand the influences of aging, environment and disease on a cell — and ultimately diagnose and treat patients better. Yet, reference atlases do not come without challenges. Single-cell datasets may contain measurement errors (batch effect), the global availability of computational resources is limited and the sharing of raw data is often legally restricted.
Researchers from Helmholtz Zentrum München and the Technical University of Munich (TUM) developed a novel algorithm called “scArches,” short for single-cell architecture surgery. The biggest advantage: “Instead of sharing raw data between clinics or research centers, the algorithm uses transfer learning to compare new datasets from single-cell genomics with existing references and thus preserves privacy and anonymity. This also makes annotating and interpreting of new data sets very easy and democratizes the usage of single-cell reference atlases dramatically,” says Mohammad Lotfollahi, the leading scientist of the algorithm.
Example COVID-19
The researchers applied scArches to study COVID-19 in several lung bronchial samples. They compared the cells of COVID-19 patients to healthy references using single-cell transcriptomics. The algorithm was able to separate diseased cells from the references and thus enabled the user to pinpoint the cells in need for treatment, for both mild and severe COVID-19 cases. Biological variation between patients did not affect the quality of the mapping process.
Fabian Theis: “Our vision is that in the future we will use cell references as easily as we nowadays do for genome references. In other word, if you want to bake a cake, you usually do not want to try coming up with your own recipe — instead you just look one up in a cookbook. With scArches, we formalize and simplify this lookup process.”
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Increasingly, movies featuring humanoid robots, like Terminator or Ex Machina, are showing the titular “robot” akin to humans not only in intelligence but also appearance. What if Terminator-esque robots became the norm, making it difficult for us to tell them apart from actual human beings?
This is the premise of a new study published in PLOS ONE, which evaluated how human beings respond to images of people with the same face. It is not too far-fetched to imagine a future where human-like androids are mass-produced and are indistinguishable from flesh-and-blood human beings. Robotics and artificial intelligence are advancing at an unprecedented rate, with very closely human-like robots and CG characters, such as Geminoid, Saya, and Sophia already having been produced. Developers are optimistic they will one day create robots that surpass the uncanny valley — a well-known phenomenon where humanoids elicit unpleasant and negative emotions in viewers when their appearance becomes similar to that of humans.
In such a future, how would we react?
A team of researchers from Kyushu University, Ritsumeikan University, and Kansai University, collaboratively conducted a series of six experiments involving different batches of hundreds of people to try and find that answer.
The first experiment involved rating the subjective eeriness, emotional valence, and realism of a photoshopped photograph of six human subjects with the exact same face (clone image), six people with different faces (non-clone image), and one person (single image). The second experiment comprised rating another set of clone images and non-clone images, while the third experiment consisted of rating clone and non-clone images of dogs. The fourth experiment had two parts: rating clone images of two sets of twins and then rating clone faces of twins, triplets, quadruplets, and quintuplets. The fifth experiment involved clone images of Japanese animation and cartoon characters. And the sixth and final experiment involved evaluating the subjective eeriness and realism of a different set of clone and non-clone images while also answering the Disgust Scale Revised to analyze disgust sensitivity.
The results were striking. Participants from the first study rated individuals with clone faces as eerier and more improbable than those with different faces and a single person’s face.
The researchers termed this negative emotional response as the clone devaluation effect.
“The clone devaluation effect was stronger when the number of clone faces increased from two to four,” says lead author Dr. Fumiya Yonemitsu from Graduate School of Human-Environment Studies at Kyushu University, who is also a Research Fellow of Japan Society for the Promotion of Science. “This effect did not occur when each clone face was indistinguishable, like animal faces in experiment three involving dogs.”
According to him, “We also noticed that the duplication of identity, that is the personality and mind unique to a person, rather than their facial features, has an important role in this effect. Clone faces with the duplication of identity were eerier, as the fourth experiment showed. The clone devaluation effect became weaker when clone faces existed in the lower reality of the context, such as in the fifth experiment. Furthermore, the eeriness of clone faces stemming from improbability could be positively predicted by disgust, in particular animal-reminder disgust, as noticed in the sixth experiment. Taken together, these results suggest that clone faces induce eeriness and that the clone devaluation effect is related to realism and disgust reaction.”
These results show that human faces provide important information for identifying individuals because human beings have a one-to-one correspondence between face and identity. Clone faces violate this principle, which may make humans misjudge the identity of people with clone faces as being the same.
So, what does this mean for a future in which humanoids are inevitable? According to the researchers, we need to think critically about introducing new technology in robotics or human cloning because of the potential for unpleasant psychological reactions other than the uncanny valley phenomenon.
“Our study clearly shows that uncomfortable situations could occur due to the rapid development of technology. But we believe our findings can play an important role in the smooth acceptance of new technologies and enhance people’s enjoyment of their benefits”, observes co-author Dr. Akihiko Gobara, Senior Researcher from BKC Research Organization of Social Science at Ritsumeikan University.
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Researchers from the University of Kent, the Research Institute for Environment Treatment and Vita-Market Ltd have discovered a universal mathematical formula that can describe any bird’s egg existing in nature, a feat which has been unsuccessful until now.
Egg-shape has long attracted the attention of mathematicians, engineers, and biologists from an analytical point of view. The shape has been highly regarded for its evolution as large enough to incubate an embryo, small enough to exit the body in the most efficient way, not roll away once laid, is structurally sound enough to bear weight and be the beginning of life for so many species. The egg has been called the “perfect shape.”
Analysis of all egg shapes used four geometric figures: sphere, ellipsoid, ovoid, and pyriform (conical or pear-shaped), with a mathematical formula for the pyriform yet to be derived.
To rectify this, researchers introduced an additional function into the ovoid formula, developing a mathematical model to fit a completely novel geometric shape characterized as the last stage in the evolution of the sphere-ellipsoid, which it is applicable to any egg geometry.
This new universal mathematical formula for egg shape is based on four parameters: egg length, maximum breadth, shift of the vertical axis, and the diameter at one quarter of the egg length.
This long sought-for universal formula is a significant step in understanding not only the egg shape itself, but also how and why it evolved, thus making widespread biological and technological applications possible.
Mathematical descriptions of all basic egg shapes have already found applications in food research, mechanical engineering, agriculture, biosciences, architecture and aeronautics. As an example, this formula can be applied to engineering construction of thin walled vessels of an egg shape, which should be stronger than typical spherical ones.
This new formula is an important breakthrough with multiple applications including: Competent scientific description of a biological object. Now that an egg can be described via mathematical formula, work in fields of biological systematics, optimization of technological parameters, egg incubation and selection of poultry will be greatly simplified. Accurate and simple determination of the physical characteristics of a biological object. The external properties of an egg are vital for researchers and engineers who develop technologies for incubating, processing, storing and sorting eggs. There is a need for a simple identification process using egg volume, surface area, radius of curvature and other indicators for describing the contours of the egg, which this formula provides. Future biology-inspired engineering. The egg is a natural biological system studied to design engineering systems and state-of-the-art technologies. The egg-shaped geometric figure is adopted in architecture, such as London City Hall’s roof and the Gherkin, and construction as it can withstand maximum loads with a minimum consumption of materials, to which this formula can now be easily applied.Darren Griffin, Professor of Genetics in the University of Kent and PI on the research, said: “Biological evolutionary processes such as egg formation must be investigated for mathematical description as a basis for research in evolutionary biology, as demonstrated with this formula. This universal formula can be applied across fundamental disciplines, especially the food and poultry industry, and will serve as an impetus for further investigations inspired by the egg as a research object.”
Dr Michael Romanov, Visiting Researcher at the University of Kent, said: “This mathematical equation underlines our understanding and appreciation of a certain philosophical harmony between mathematics and biology, and from those two a way towards further comprehension of our universe, understood neatly in the shape of an egg.”
Dr Valeriy Narushin, former visiting researcher at the University of Kent, said: “We look forward to seeing the application of this formula across industries, from art to technology, architecture to agriculture. This breakthrough reveals why such collaborative research from separate disciplines is essential.”
The paper “Egg and math: introducing a universal formula for egg shape” is published in Annals of the New York Academy of Sciences (Valeriy G. Narushin, Research Institute for Environment Treatment and Vita-Market Ltd, Ukraine; Dr Michael N. Romanov, University of Kent; Professor Darren K. Griffin, University of Kent).
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