Computers Math

In recent years, artificial intelligence has become ubiquitous, with applications such as speech interpretation, image recognition, medical diagnosis, and many more. At the same time, quantum technology has been proven capable of computational power well beyond the reach of even the world’s largest supercomputer. Physicists at the University of Vienna have now demonstrated a new device, called quantum memristor, which may allow to combine these two worlds, thus unlocking unprecedented capabilities. The experiment, carried out in collaboration with the National Research Council (CNR) and the Politecnico di Milano in Italy, has been realized on an integrated quantum processor operating on single photons. The work is published in the current issue of the journal Nature Photonics.
At the heart of all artificial intelligence applications are mathematical models called neural networks. These models are inspired by the biological structure of the human brain, made of interconnected nodes. Just like our brain learns by constantly rearranging the connections between neurons, neural networks can be mathematically trained by tuning their internal structure until they become capable of human-level tasks: recognizing our face, interpreting medical images for diagnosis, even driving our cars. Having integrated devices capable of performing the computations involved in neural networks quickly and efficiently has thus become a major research focus, both academic and industrial.
One of the major game changers in the field was the discovery of the memristor, made in 2008. This device changes its resistance depending on a memory of the past current, hence the name memory-resistor, or memristor. Immediately after its discovery, scientists realized that (among many other applications) the peculiar behavior of memristors was surprisingly similar to that of neural synapses. The memristor has thus become a fundamental building block of neuromorphic architectures.
A group of experimental physicists from the University of Vienna, the National Research Council (CNR) and the Politecnico di Milano led by Prof. Philip Walther and Dr. Roberto Osellame, have now demonstrated that it is possible to engineer a device that has the same behavior as a memristor, while acting on quantum states and being able to encode and transmit quantum information. In other words, a quantum memristor. Realizing such device is challenging because the dynamics of a memristor tends to contradict the typical quantum behavior.
By using single photons, i.e. single quantum particles of lights, and exploiting their unique ability to propagate simultaneously in a superposition of two or more paths, the physicists have overcome the challenge. In their experiment, single photons propagate along waveguides laser-written on a glass substrate and are guided on a superposition of several paths. One of these paths is used to measure the flux of photons going through the device and this quantity, through a complex electronic feedback scheme, modulates the transmission on the other output, thus achieving the desired memristive behavior. Besides demonstrating the quantum memristor, the researchers have provided simulations showing that optical networks with quantum memristor can be used to learn on both classical and quantum tasks, hinting at the fact that the quantum memristor may be the missing link between artificial intelligence and quantum computing.
“Unlocking the full potential of quantum resources within artificial intelligence is one of the greatest challenges of the current research in quantum physics and computer science,” says Michele Spagnolo, who is first author of the publication in the journal “Nature Photonics.” The group of Philip Walther of the University of Vienna has also recently demonstrated that robots can learn faster when using quantum resources and borrowing schemes from quantum computation. This new achievement represents one more step towards a future where quantum artificial intelligence become reality.
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Researchers have developed a new analog photonic correlator that can be used to locate an object transmitting a radio signal. Because the new correlator is faster than other methods and works with a wide range of radio frequency signals, it could be useful for locating cell phones, signal jammers or a variety of tracking tags.
“The photonic architecture we developed uses no moving parts and enables real-time signal processing,” said Hugues Guillet de Chatellus from Université Grenoble Alpes-CNRS in France. “Real-time processing helps ensure there isn’t any downtime, which is critical for defense applications, for example.”
In Optica, Optica Publishing Group’s journal for high-impact research, Guillet de Chatellus and colleagues describe the new photonic correlator and demonstrate its ability to identify the location of a radio frequency transmitter. The device is considerably simpler than today’s analog or digital correlators and uses off-the-shelf telecommunications components.
“Many of today’s radio signals have large bandwidths because they carry a great deal of information,” said Guillet de Chatellus. “Our photonic approach offers a simple method for correlating signals with bandwidths of up to a few GHz, a larger bandwidth than is available from commercial approaches based on purely digital techniques.”
Using light to calculate correlation
The new photonic correlator can be used to compute what is known as a cross-correlation function for two signals emitted from one source and detected by two antennas. This measures the similarity of the signals as a function of the displacement of one signal relative to the other and provides information about their relative delay, which can be used to calculate the location of the signal’s source. More

If a sticker on a banana can make it show up as a toaster, how might strategic vandalism warp how an autonomous vehicle perceives a stop sign? Now, an immune-inspired defense system for neural networks can ward off such attacks, designed by engineers, biologists and mathematicians at the University of Michigan.
Deep neural networks are a subset of machine learning algorithms used for a wide variety of classification problems. These include image identification and machine vision (used by autonomous vehicles and other robots), natural language processing, language translation and fraud detection. However, it is possible for a nefarious person or group to adjust the input slightly and send the algorithm down the wrong train of thought, so to speak. To protect algorithms against such attacks, the Michigan team developed the Robust Adversarial Immune-inspired Learning System.
“RAILS represents the very first approach to adversarial learning that is modeled after the adaptive immune system, which operates differently than the innate immune system,” said Alfred Hero, the John H. Holland Distinguished University Professor, who co-led the work published in IEEE Access.
While the innate immune system mounts a general attack on pathogens, the mammalian immune system can generate new cells designed to defend against specific pathogens. It turns out that deep neural networks, already inspired by the brain’s system of information processing, can take advantage of this biological process, too.
“The immune system is built for surprises,” said Indika Rajapakse, associate professor of computational medicine and bioinformatics and co-leader of the study. “It has an amazing design and will always find a solution.”
RAILS works by mimicking the natural defenses of the immune system to identify and ultimately take care of suspicious inputs to the neural network. To begin developing it, the biological team studied how the adaptive immune systems of mice responded to an antigen. The experiment used the tissues of genetically modified mice that express fluorescent markers on their B cells. More

Scientists at Osaka University have simulated heat transport at the smallest scales using a molecular dynamics computer simulation. By studying the motions of the individual particles that make up the boundary between a solid and a liquid, they have been able to calculate heat flux with unprecedented precision. This work may lead to significant improvements in our ability to fabricate nanoscale devices, as well as functional surfaces and nanofluidic devices.
The process by which heat is transferred at the point where a solid meets a liquid may seem to be a simple physics problem. Traditionally, macroscopic quantities — such as density, pressure, temperature, and heat capacity — were used to compute the rate at which thermal energy moves between materials. However, properly accounting for the motion of individual molecules, while observing the laws of conservation of energy and momentum, adds a great deal of complexity. Improved atomic-scale computer simulations would be invaluable to more accurately understanding a wide array of real-world applications, especially within the field of nanotechnology.
Now, a team of researchers at Osaka University has developed a new numerical technique to visualize a modeled heat flux at the atomic scale for the first time. “To fundamentally understand thermal transport through a solid-liquid interface, the transport properties of atoms and molecules must be considered,” first author of the study Kunio Fujiwara explains. “We modeled the heat flux near a solid-liquid interface region with sub-atomic spatial resolution by using classical molecular dynamics simulations. This allowed us to create images of the three-dimensional structure of the energy flow while heat was being transferred between the layers.”
Using the popular Lennard-Jones potential to calculate the interactions between adjacent atoms, the team found that the direction of heat flux strongly depends on the sub-atomic stresses in the structures of the solids or liquids.
“Before, there was no good way to visualize heat flux at atomic scale,” senior author Masahiko Shibahara says. “These findings should allow us to elucidate and modify the thermal transport based on the 3D heat flux configuration.”
This may allow for customized nanoscale manufacturing to be carried out more efficiently.
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Scientists are using cutting-edge artificial intelligence to help extract complex information from large collections of museum specimens.
A team from Cardiff University is using state-of-the-art techniques to automatically segment and capture information from museum specimens and perform important data quality improvement without the need of human input.
They have been working with museums from across Europe, including the Natural History Museum, London, to refine and validate their new methods and contribute to the mammoth task of digitising hundreds of millions of specimens.
With more than 3 billion biological and geological specimens curated in natural history museums around the world, the digitization of museum specimens, in which physical information from a particular specimen is transformed into a digital format, has become an increasingly important task for museums as they adapt to an increasingly digital world.
A treasure trove of digital information is invaluable for scientists trying to model the past, present and future of organisms and our planet, and could be key to tackling some of the biggest societal challenges our world faces today, from conserving biodiversity and tackling climate change to finding new ways to cope with emerging diseases like COVID-19.
The digitization process also helps to reduce the amount of manual handling of specimens, many of which are very delicate and prone to damage. Having suitable data and images available online can reduce the risk to the physical collection and protect specimens for future generations. More

An artificial intelligence (AI)-based computer algorithm accurately predicted a person’s likelihood of suffering heart problems related to clogged arteries based on voice recordings alone, in a study presented at the American College of Cardiology’s 71st Annual Scientific Session.
Researchers found that people with a high voice biomarker score were 2.6 times more likely to suffer major problems associated with coronary artery disease (CAD), a buildup of plaque in the heart’s arteries, and three times more likely to show evidence of plaque buildup in medical tests compared with those who had a low score. While the technology is not yet ready for use in the clinic, the demonstration suggests voice analysis could be a powerful screening tool in identifying patients who may benefit from closer monitoring for CAD-related events. Researchers said this approach could be particularly useful in remote health care delivery and telehealth.
“Telemedicine is non-invasive, cost-effective and efficient and has become increasingly important during the pandemic,” said Jaskanwal Deep Singh Sara, MD, a cardiology fellow at Mayo Clinic and the study’s lead author. “We’re not suggesting that voice analysis technology would replace doctors or replace existing methods of health care delivery, but we think there’s a huge opportunity for voice technology to act as an adjunct to existing strategies. Providing a voice sample is very intuitive and even enjoyable for patients, and it could become a scalable means for us to enhance patient management.
The study represents the first time voice analysis has been used to predict CAD outcomes in patients who were tracked prospectively after an initial screening. Previous studies retrospectively examined voice markers associated with CAD and heart failure. Other research groups have explored the use of similar technology for a range of disorders, including Parkinson’s disease, Alzheimer’s disease and COVID-19.
For the new study, researchers recruited 108 patients who were referred for a coronary angiogram, an X-ray imaging procedure used to assess the condition of the heart’s arteries. Participants were asked to record three 30-second voice samples using the Vocalis Health smartphone application. For the first sample, participants read from a prepared text. For the second sample, they were asked to speak freely about a positive experience, and for the third, they spoke freely about a negative experience.
The Vocalis Health algorithm then analyzed participants’ voice samples. The AI-based system had been trained to analyze more than 80 features of voice recordings, such as frequency, amplitude, pitch and cadence, based on a training set of over 10,000 voice samples collected in Israel. In previous studies, researchers identified six features that were highly correlated with CAD. For the new study, researchers combined these features into a single score, expressed as a number between -1 and 1 for each individual. One-third of patients were categorized as having a high score and two-thirds had a low score.
“We can’t hear these particular features ourselves,” Sara said. “This technology is using machine learning to quantify something that isn’t easily quantifiable for us using our human brains and our human ears.”
Study participants were tracked for two years. Of those with a high voice biomarker score, 58.3% visited the hospital for chest pain or suffered acute coronary syndrome (a type of major heart problem that includes heart attacks), the study’s composite primary endpoint, compared with 30.6% of those with a low voice biomarker score. Participants with a high voice biomarker score were also more likely to have a positive stress test or be diagnosed with CAD during a subsequent angiogram (the composite secondary endpoint).
Scientists have not concluded why certain voice features seem to be indicative of CAD, but Sara said the autonomic nervous system may play a role. This part of the nervous system regulates bodily functions that are not under conscious control, which includes both the voice box and many aspects of the cardiovascular system, such as heart rate and blood pressure. Therefore, it is possible that the voice could provide clues about how the autonomic nervous system is functioning, and by extension, provide insights into cardiovascular health, Sara said.
The study was conducted with English speakers in the Midwestern U.S. using software trained on voice samples collected in Israel. Sara said more tests are needed to determine whether the approach is generalizable and scalable across languages, countries, cultures and health care settings. He added that it will also be important to address security and privacy issues before incorporating such technology into telemedicine or on-site health assessments.
“It’s definitely an exciting field, but there’s still a lot of work to be done,” Sara said. “We have to know the limitations of the data we have, and we need to conduct more studies in more diverse populations, larger trials and more prospective studies like this one.” More

Tiny biological computers made of DNA could revolutionize the way we diagnose and treat a slew of diseases, once the technology is fully fleshed out. However, a major stumbling block for these DNA-based devices, which can operate in both cells and liquid solutions, has been how short-lived they are. Just one use and the computers are spent.
Now, researchers at the National Institute of Standards and Technology (NIST) may have developed long-lived biological computers that could potentially persist inside cells. In a paper published in the journal Science Advances, the authors forgo the traditional DNA-based approach, opting instead to use the nucleic acid RNA to build computers. The results demonstrate that the RNA circuits are as dependable and versatile as their DNA-based counterparts. What’s more, living cells may be able to create these RNA circuits continuously, something that is not readily possible with DNA circuits, further positioning RNA as a promising candidate for powerful, long-lasting biological computers.
Much like the computer or smart device you are likely reading this on, biological computers can be programmed to carry out different kinds of tasks.
“The difference is, instead of coding with ones and zeroes, you write strings of A, T, C and G, which are the four chemical bases that make up DNA,” said Samuel Schaffter, NIST postdoctoral researcher and lead author of the study.
By assembling a specific sequence of bases into a strand of nucleic acid, researchers can dictate what it binds to. A strand could be engineered to attach to specific bits of DNA, RNA or some proteins associated with a disease, then trigger chemical reactions with other strands in the same circuit to process chemical information and eventually produce some sort of useful output.
That output might be a detectable signal that could aid medical diagnostics, or it could be a therapeutic drug to treat a disease. More

Reseachers have analysed samples of NdBi crystals which display interesting magnetic properties. In their experiments including measurements at BESSY II they could find evidence for so called Fermi arcs in the antiferromagnetic state of the sample at low temperatures. This observation is not yet explained by existing theoretical ideas and opens up exciting possibilities to make use of these kind of materials for innovative information technologies based on the electron spin rather than the charge.
Neodymium-Bismuth crystals belong to the wide range of materials with interesting magnetic properties. The Fermi surface which is measured in the experiments contains information on the transport properties of charge carriers in the crystal. While usually the Fermi surface consists of closed contours, disconnected sections known as Fermi arcs are very rare and can be signatures of unusual electronic states.
Unusual magnetic splittings
In a study, published now in Nature, the team presents experimental evidence for such Fermi arcs. They observed an unusual magnetic splitting in the antiferromagnetic state of the samples below a temperature of 24 Kelvin (the Néel-temperature). This splitting creates bands of opposing curvature, which changes with temperature together with the antiferromagnetic order.
These findings are very important because they are fundamentally different from previously theoretically considered and experimentally reported cases of magnetic splittings. In the case of well-known Zeeman and Rashba splittings, the curvature of the bands is always preserved. Since both splittings are important for spintronics, these new findings could lead to novel applications, especially as the focus of spintronics research is currently moving from traditional ferromagnetic to antiferromagnetic materials.
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