Computers Math

Ludwig-Maximilians-Universitaet (LMU) in Munich physicists have introduced a new method that allows biological pattern-forming systems to be systematically characterized with the aid of mathematical analysis. The trick lies in the use of geometry to characterize the dynamics.
Many vital processes that take place in biological cells depend on the formation of self-organizing molecular patterns. For example, defined spatial distributions of specific proteins regulate cell division, cell migration and cell growth. These patterns result from the concerted interactions of many individual macromolecules. Like the collective motions of bird flocks, these processes do not need a central coordinator. Hitherto, mathematical modelling of protein pattern formation in cells has been carried out largely by means of elaborate computer-based simulations. Now, LMU physicists led by Professor Erwin Frey report the development of a new method which provides for the systematic mathematical analysis of pattern formation processes, and uncovers the their underlying physical principles. The new approach is described and validated in a paper that appears in the journal Physical Review X.
The study focuses on what are called ‘mass-conserving’ systems, in which the interactions affect the states of the particles involved, but do not alter the total number of particles present in the system. This condition is fulfilled in systems in which proteins can switch between different conformational states that allow them to bind to a cell membrane or to form different multicomponent complexes, for example. Owing to the complexity of the nonlinear dynamics in these systems, pattern formation has so far been studied with the aid of time-consuming numerical simulations. “Now we can understand the salient features of pattern formation independently of simulations using simple calculations and geometrical constructions,” explains Fridtjof Brauns, lead author of the new paper. “The theory that we present in this report essentially provides a bridge between the mathematical models and the collective behavior of the system’s components.”
The key insight that led to the theory was the recognition that alterations in the local number density of particles will also shift the positions of local chemical equilibria. These shifts in turn generate concentration gradients that drive the diffusive motions of the particles. The authors capture this dynamic interplay with the aid of geometrical structures that characterize the global dynamics in a multidimensional ‘phase space’. The collective properties of systems can be directly derived from the topological relationships between these geometric constructs, because these objects have concrete physical meanings — as representations of the trajectories of shifting chemical equilibria, for instance. “This is the reason why our geometrical description allows us to understand why the patterns we observe in cells arise. In other words, they reveal the physical mechanisms that determine the interplay between the molecular species involved,” says Frey. “Furthermore, the fundamental elements of our theory can be generalized to deal with a wide range of systems, which in turn paves the way to a comprehensive theoretical framework for self-organizing systems.”

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True random numbers are required in fields as diverse as slot machines and data encryption. These numbers need to be truly random, such that they cannot even be predicted by people with detailed knowledge of the method used to generate them.
As a rule, they are generated using physical methods. For instance, thanks to the tiniest high-frequency electron movements, the electrical resistance of a wire is not constant but instead fluctuates slightly in an unpredictable way. That means measurements of this background noise can be used to generate true random numbers.
Now, for the first time, a research team led by Robert Grass, Professor at the Institute of Chemical and Bioengineering, has described a non-physical method of generating such numbers: one that uses biochemical signals and actually works in practice. In the past, the ideas put forward by other scientists for generating random numbers by chemical means tended to be largely theoretical.
DNA synthesis with random building blocks
For this new approach, the ETH Zurich researchers apply the synthesis of DNA molecules, an established chemical research method frequently employed over many years. It is traditionally used to produce a precisely defined DNA sequence. In this case, however, the research team built DNA molecules with 64 building block positions, in which one of the four DNA bases A, C, G and T was randomly located at each position. The scientists achieved this by using a mixture of the four building blocks, rather than just one, at every step of the synthesis.
As a result, a relatively simple synthesis produced a combination of approximately three quadrillion individual molecules. The scientists subsequently used an effective method to determine the DNA sequence of five million of these molecules. This resulted in 12 megabytes of data, which the researchers stored as zeros and ones on a computer.
Huge quantities of randomness in a small space
However, an analysis showed that the distribution of the four building blocks A, C, G and T was not completely even. Either the intricacies of nature or the synthesis method deployed led to the bases G and T being integrated more frequently in the molecules than A and C. Nonetheless, the scientists were able to correct this bias with a simple algorithm, thereby generating perfect random numbers.
The main aim of ETH Professor Grass and his team was to show that random occurrences in chemical reaction can be exploited to generate perfect random numbers. Translating the finding into a direct application was not a prime concern at first. “Compared with other methods, however, ours has the advantage of being able to generate huge quantities of randomness that can be stored in an extremely small space, a single test tube,” Grass says. “We can read out the information and reinterpret it in digital form at a later date. This is impossible with the previous methods.”

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The vision of the future of miniaturisation has produced a series of synthetic molecular motors that are driven by a range of energy sources and can carry out various movements. A research group at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) has now managed to control a catalysis reaction using a light-controlled motor. This takes us one step closer to realising the vision of a nano factory in which combinations of various machines work together, as is the case in biological cells. The results have been published in the Journal of the American Chemical Society.
Laws of mechanics cannot always be applied
Per definition, a motor converts energy into a specific type of kinetic energy. On a molecular level, for example, the protein myosin can produce muscle contractions using chemical energy. Such nanomachines can now be synthetically produced. However, the molecules used are much smaller than proteins and significantly less complex.
‘The laws of mechanical physics cannot simply be applied to the molecular level,’ says Prof. Dr. Henry Dube, Chair of Organic Chemistry I at FAU. Inertia, for example, does not exist at this level, he explains. Triggered by Brownian motion, particles are constantly in motion. ‘Activating a rotating motor is not enough, you need to incorporate a type of ratchet mechanism that prevents it from turning backwards,’ he explains.
In 2015 while at LMU in Munich, Prof. Dube and his team developed a particularly fast molecular motor driven by visible light. In 2018, they developed the first molecular motor that is driven solely by light and functions regardless of the ambient temperature. A year later, they developed a variant capable not only of rotation but also of performing a figure of eight motion. All motors are based on the hemithioindigo molecule, an asymmetric variant of the naturally occurring dye indigo where a sulphur atom takes the place of the nitrogen atom. One part of the molecule rotates in several steps in the opposite direction to the other part of the molecule. The energy-driven steps are triggered by visible light and modify the molecules so that reverse reactions are blocked.
Standard catalysts in use
After coming to FAU, Henry Dube used the rotating motor developed in 2015 to control a separate chemical process for the first time. It moves in four steps around the carbon double bond of the hemithioindigo. Two of the four steps triggered by a photo reaction can be used to control a catalysis reaction. ‘Green light generates a molecular structure that binds a catalyst to the hemithioindigo and blue light releases the catalyst,’ explains the chemist.
A standard catalyst is used that does not have any metal atoms. Using electrostatic forces, the catalyst docks via a hydrogen bond onto an oxygen atom in the ‘motor molecule’. All catalysts that use a hydrogen bond could be used, in principle. ‘The great advantage of hemithioindigo is that its innate structure has a bonding mechanism for catalysts,’ explains Prof. Dube. It would otherwise have to be added using chemical synthesis.
The rotation of the hemithioindigo motor is controlled by visible light. At the same time, the system allows the targeted release and bonding of a catalyst that accelerates or decelerates desired chemical reactions. ‘This project is an important step towards integrating molecular motors in chemical processes simply and in a variety of ways,’ says Prof. Dube. ‘This will let us synthesise complex medication at a high level of precision using molecular machines like a production line in future.’

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Scientists are working on new materials to create neuromorphic computers, with a design based on the human brain. A crucial component is a memristive device, the resistance of which depends on the history of the device — just like the response of our neurons depends on previous input. Materials scientists from the University of Groningen analysed the behaviour of strontium titanium oxide, a platform material for memristor research and used the 2D material graphene to probe it. On 11 November 2020, the results were published in the journal ACS Applied Materials and Interfaces.
Computers are giant calculators, full of switches that have a value of either 0 or 1. Using a great many of these binary systems, computers can perform calculations very rapidly. However, in other respects, computers are not very efficient. Our brain uses less energy for recognizing faces or performing other complex tasks than a standard microprocessor. That is because our brain is made up of neurons that can have many values other than 0 and 1 and because the neurons’ output depends on previous input.
Oxygen vacancies
To create memristors, switches with a memory of past events, strontium titanium oxide (STO) is often used. This material is a perovskite, whose crystal structure depends on temperature, and can become an incipient ferroelectric at low temperatures. The ferroelectric behaviour is lost above 105 Kelvin. The domains and domain walls that accompany these phase transitions are the subject of active research. Yet, it is still not entirely clear why the material behaves the way it does. ‘It is in a league of its own,’ says Tamalika Banerjee, Professor of Spintronics of Functional Materials at the Zernike Institute for Advanced Materials, University of Groningen.
The oxygen atoms in the crystal appear to be key to its behaviour. ‘Oxygen vacancies can move through the crystal and these defects are important,’ says Banerjee. ‘Furthermore, domain walls are present in the material and they move when a voltage is applied to it.’ Numerous studies have sought to find out how this happens, but looking inside this material is complicated. However, Banerjee’s team succeeded in using another material that is in a league of its own: graphene, the two-dimensional carbon sheet.
Conductivity
‘The properties of graphene are defined by its purity,’ says Banerjee, ‘whereas the properties of STO arise from imperfections in the crystal structure. We found that combining them leads to new insights and possibilities.’ Much of this work was carried out by Banerjee’s PhD student Si Chen. She placed graphene strips on top of a flake of STO and measured the conductivity at different temperatures by sweeping a gate voltage between positive and negative values. ‘When there is an excess of either electrons or the positive holes, created by the gate voltage, graphene becomes conductive,’ Chen explains. ‘But at the point where there are very small amounts of electrons and holes, the Dirac point, conductivity is limited.’
In normal circumstances, the minimum conductivity position does not change with the sweeping direction of the gate voltage. However, in the graphene strips on top of STO, there is a large separation between the minimum conductivity positions for the forward sweep and the backward sweep. The effect is very clear at 4 Kelvin, but less pronounced at 105 Kelvin or at 150 Kelvin. Analysis of the results, along with theoretical studies carried out at Uppsala University, shows that oxygen vacancies near the surface of the STO are responsible.
Memory
Banerjee: ‘The phase transitions below 105 Kelvin stretch the crystal structure, creating dipoles. We show that oxygen vacancies accumulate at the domain walls and that these walls offer the channel for the movement of oxygen vacancies. These channels are responsible for memristive behaviour in STO.’ Accumulation of oxygen vacancy channels in the crystal structure of STO explains the shift in the position of the minimum conductivity.
Chen also carried out another experiment: ‘We kept the STO gate voltage at -80 V and measured the resistance in the graphene for almost half an hour. In this period, we observed a change in resistance, indicating a shift from hole to electron conductivity.’ This effect is primarily caused by the accumulation of oxygen vacancies at the STO surface.
All in all, the experiments show that the properties of the combined STO/graphene material change through the movement of both electrons and ions, each at different time scales. Banerjee: ‘By harvesting one or the other, we can use the different response times to create memristive effects, which can be compared to short-term or long-term memory effects.’ The study creates new insights into the behaviour of STO memristors. ‘And the combination with graphene opens up a new path to memristive heterostructures combining ferroelectric materials and 2D materials.’

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Osaka City University scientists and colleagues in Japan have found a way to control an interaction between quantum dots that could greatly improve charge transport, leading to more efficient solar cells. Their findings were published in the journal Nature Communications.
Nanomaterials engineer DaeGwi Kim led a team of scientists at Osaka City University, RIKEN Center for Emergent Matter Science and Kyoto University to investigate ways to control a property called quantum resonance in layered structures of quantum dots called superlattices.
“Our simple method for fine-tuning quantum resonance is an important contribution to both optical materials and nanoscale material processing,” says Kim.
Quantum dots are nanometer-sized semiconductor particles with interesting optical and electronic properties. When light is shone on them, for example, they emit strong light at room temperature, a property called photoluminescence. When quantum dots are close enough to each other, their electronic states are coupled, a phenomenon called quantum resonance. This greatly improves their ability to transport electrons between them. Scientists have been wanting to manufacture devices using this interaction, including solar cells, display technologies, and thermoelectric devices.
However, they have so far found it difficult to control the distances between quantum dots in 1D, 2D and 3D structures. Current fabrication processes use long ligands to hold quantum dots together, which hinders their interactions.
Kim and his colleagues found they could detect and control quantum resonance by using cadmium telluride quantum dots connected with short N-acetyl-L-cysteine ligands. They controlled the distance between quantum dot layers by placing a spacer layer between them made of oppositely charged polyelectrolytes. Quantum resonance is detected between stacked dots when the spacer layer is thinner than two nanometers. The scientists also controlled the distance between quantum dots in a single layer, and thus quantum resonance, by changing the concentration of quantum dots used in the layering process.
The team next plans to study the optical properties, especially photoluminescence, of quantum dot superlattices made using their layer-by-layer approach. “This is extremely important for realizing new optical electronic devices made with quantum dot superlattices,” says Kim.
Kim adds that their fabrication method can be used with other types of water-soluble quantum dots and nanoparticles. “Combining different types of semiconductor quantum dots, or combining semiconductor quantum dots with other nanoparticles, will expand the possibilities of new material design,” says Kim.

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Rochester Institute of Technology students discovered lost text on 15th-century manuscript leaves using an imaging system they developed as freshmen. By using ultraviolet-fluorescence imaging, the students revealed that a manuscript leaf held in RIT’s Cary Graphic Arts Collection was actually a palimpsest, a manuscript on parchment with multiple layers of writing.
At the time the manuscript was written, making parchment was expensive, so leaves were regularly scraped or erased and re-used. While the erased text is invisible to the naked eye, the chemical signature of the initial writing can sometimes be detected using other areas of the light spectrum.
“Using our system, we borrowed several parchments from the Cary Collection here at RIT and when we put one of them under the UV light, it showed this amazing dark French cursive underneath,” said Zoë LaLena, a second-year imaging science student from Fairport, N.Y., who worked on the project. “This was amazing because this document has been in the Cary Collection for about a decade now and no one noticed. And because it’s also from the Ege Collection, in which there’s 30 other known pages from this book, it’s really fascinating that the 29 other pages we know the location of have the potential to also be palimpsests.”
The imaging system was originally built by 19 students enrolled in the Chester F. Carlson Center for Imaging Science’s Innovative Freshman Experience, a yearlong, project-based course that has the imaging science, motion picture science, and photographic sciences programs combine their talents to solve a problem.
When RIT switched to remote instruction in March due to the coronavirus outbreak, the students were unable to finish building it, but thanks to a donation from Jeffrey Harris ’75 (photographic science and instrumentation) and Joyce Pratt, three students received funding to continue to work on the project over the summer. Those three students — LaLena; Lisa Enochs, a second-year student double majoring in motion picture science and imaging science from Mississauga, Ontario; and Malcom Zale, a second-year motion picture science student from Milford, Mass. — finished assembling the system in the fall when classes resumed and began analyzing documents from the Cary Collection.
Steven Galbraith, curator of the Cary Graphic Arts Collection, said he was excited they discovered the manuscript leaf was a palimpsest because similar leaves have been studied extensively by scholars across the country, but never tested with UV light or fully imaged.
Collector, educator, and historian Otto Ege made leaf collections out of medieval manuscripts that were damaged or incomplete and sold them or distributed them to libraries and special collections across North America, including to the Cary Collection. Galbraith said he’s excited because it means that many other cultural and academic institutions with Ege Collection leaves now may have palimpsests in their collection to study.
“The students have supplied incredibly important information about at least two of our manuscript leaves here in the collection and in a sense have discovered two texts that we didn’t know were in the collection,” said Galbraith. “Now we have to figure out what those texts are and that’s the power of spectral imaging in cultural institutions. To fully understand our own collections, we need to know the depth of our collections, and imaging science helps reveal all of that to us.”
The students are interested to see if more manuscript leaves from Ege collections across the country are palimpsests. They imaged another Ege Collection leaf at the Buffalo and Erie County Public Library that turned out to be a palimpsest and are reaching out to other curators across the country. As they begin stitching the lost text back together, paleographers can examine the information they contain.
The students have been selected to share their results at the 2021 International Congress on Medieval Studies and also plan to present the project at next year’s Imagine RIT: Creativity and Innovation Festival.
VIDEO: https://www.youtube.com/watch?v=YEieepHPMA0

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Imagine if robots could learn from watching demonstrations: you could show a domestic robot how to do routine chores or set a dinner table. In the workplace, you could train robots like new employees, showing them how to perform many duties. On the road, your self-driving car could learn how to drive safely by watching you drive around your neighborhood.
Making progress on that vision, USC researchers have designed a system that lets robots autonomously learn complicated tasks from a very small number of demonstrations — even imperfect ones. The paper, titled Learning from Demonstrations Using Signal Temporal Logic, was presented at the Conference on Robot Learning (CoRL), Nov. 18.
The researchers’ system works by evaluating the quality of each demonstration, so it learns from the mistakes it sees, as well as the successes. While current state-of-art methods need at least 100 demonstrations to nail a specific task, this new method allows robots to learn from only a handful of demonstrations. It also allows robots to learn more intuitively, the way humans learn from each other — you watch someone execute a task, even imperfectly, then try yourself. It doesn’t have to be a “perfect” demonstration for humans to glean knowledge from watching each other.
“Many machine learning and reinforcement learning systems require large amounts of data data and hundreds of demonstrations — you need a human to demonstrate over and over again, which is not feasible,” said lead author Aniruddh Puranic, a Ph.D. student in computer science at the USC Viterbi School of Engineering.
“Also, most people don’t have programming knowledge to explicitly state what the robot needs to do, and a human cannot possibly demonstrate everything that a robot needs to know. What if the robot encounters something it hasn’t seen before? This is a key challenge.”
Learning from demonstrations
Learning from demonstrations is becoming increasingly popular in obtaining effective robot control policies — which control the robot’s movements — for complex tasks. But it is susceptible to imperfections in demonstrations and also raises safety concerns as robots may learn unsafe or undesirable actions.

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Also, not all demonstrations are equal: some demonstrations are a better indicator of desired behavior than others and the quality of the demonstrations often depends on the expertise of the user providing the demonstrations.
To address these issues, the researchers integrated “signal temporal logic” or STL to evaluate the quality of demonstrations and automatically rank them to create inherent rewards.
In other words, even if some parts of the demonstrations do not make any sense based on the logic requirements, using this method, the robot can still learn from the imperfect parts. In a way, the system is coming to its own conclusion about the accuracy or success of a demonstration.
“Let’s say robots learn from different types of demonstrations — it could be a hands-on demonstration, videos, or simulations — if I do something that is very unsafe, standard approaches will do one of two things: either, they will completely disregard it, or even worse, the robot will learn the wrong thing,” said co-author Stefanos Nikolaidis, a USC Viterbi assistant professor of computer science.
“In contrast, in a very intelligent way, this work uses some common sense reasoning in the form of logic to understand which parts of the demonstration are good and which parts are not. In essence, this is exactly what also humans do.”
Take, for example, a driving demonstration where someone skips a stop sign. This would be ranked lower by the system than a demonstration of a good driver. But, if during this demonstration, the driver does something intelligent — for instance, applies their brakes to avoid a crash — the robot will still learn from this smart action.

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Adapting to human preferences
Signal temporal logic is an expressive mathematical symbolic language that enables robotic reasoning about current and future outcomes. While previous research in this area has used “linear temporal logic,” STL is preferable in this case, said Jyo Deshmukh, a former Toyota engineer and USC Viterbi assistant professor of computer science .
“When we go into the world of cyber physical systems, like robots and self-driving cars, where time is crucial, linear temporal logic becomes a bit cumbersome, because it reasons about sequences of true/false values for variables, while STL allows reasoning about physical signals.”
Puranic, who is advised by Deshmukh, came up with the idea after taking a hands-on robotics class with Nikolaidis, who has been working on developing robots to learn from YouTube videos. The trio decided to test it out. All three said they were surprised by the extent of the system’s success and the professors both credit Puranic for his hard work.
“Compared to a state-of-the-art algorithm, being used extensively in many robotics applications, you see an order of magnitude difference in how many demonstrations are required,” said Nikolaidis.
The system was tested using a Minecraft-style game simulator, but the researchers said the system could also learn from driving simulators and eventually even videos. Next, the researchers hope to try it out on real robots. They said this approach is well suited for applications where maps are known beforehand but there are dynamic obstacles in the map: robots in household environments, warehouses or even space exploration rovers.
“If we want robots to be good teammates and help people, first they need to learn and adapt to human preference very efficiently,” said Nikolaidis. “Our method provides that.”
“I’m excited to integrate this approach into robotic systems to help them efficiently learn from demonstrations, but also effectively help human teammates in a collaborative task.” More

Researchers have used machine learning, a type of artificial intelligence, to develop a prediction model for the early diagnosis of opioid use disorder. The advance is described in Pharmacology Research & Perspectives.
The model was generated from information in a commercial claim database from 2006 through 2018 of 10 million medical insurance claims from 550,000 patient records. It relied on data such as demographics, chronic conditions, diagnoses and procedures, and medication prescriptions.
The tool led to a diagnosis of opioid use disorder that was on average 14.4 months earlier than it was diagnosed clinically.
“Opioid use disorder has led a very serious epidemic in the U.S. and many other countries, with devastating rates of morbidity and mortality due to missed and delayed diagnoses. The novel ability of our algorithm to identify affected individuals earlier will likely save lives and health care costs,” said senior author Gideon Koren, MD, of Ariel University, in Israel.

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