Computers Math

Researchers have developed a new approach to machine learning that ‘learns how to learn’ and out-performs current machine learning methods for drug design, which in turn could accelerate the search for new disease treatments.
The method, called transformational machine learning (TML), was developed by a team from the UK, Sweden, India and Netherlands. It learns from multiple problems and improves performance while it learns.
TML could accelerate the identification and production of new drugs by improving the machine learning systems which are used to identify them. The results are reported in the Proceedings of the National Academy of Sciences.
Most types of machine learning (ML) use labelled examples, and these examples are almost always represented in the computer using intrinsic features, such as the colour or shape of an object. The computer then forms general rules that relate the features to the labels.
“It’s sort of like teaching a child to identify different animals: this is a rabbit, this is a donkey and so on,” said Professor Ross King from Cambridge’s Department of Chemical Engineering and Biotechnology, who led the research. “If you teach a machine learning algorithm what a rabbit looks like, it will be able to tell whether an animal is or isn’t a rabbit. This is the way that most machine learning works — it deals with problems one at a time.”
However, this is not the way that human learning works: instead of dealing with a single issue at a time, we get better at learning because we have learned things in the past.
“To develop TML, we applied this approach to machine learning, and developed a system that learns information from previous problems it has encountered in order to better learn new problems,” said King, who is also a Fellow at The Alan Turing Institute. “Where a typical ML system has to start from scratch when learning to identify a new type of animal — say a kitten — TML can use the similarity to existing animals: kittens are cute like rabbits, but don’t have long ears like rabbits and donkeys. This makes TML a much more powerful approach to machine learning.”
The researchers demonstrated the effectiveness of their idea on thousands of problems from across science and engineering. They say it shows particular promise in the area of drug discovery, where this approach speeds up the process by checking what other ML models say about a particular molecule. A typical ML approach will search for drug molecules of a particular shape, for example. TML instead uses the connection of the drugs to other drug discovery problems.
“I was surprised how well it works — better than anything else we know for drug design,” said King. “It’s better at choosing drugs than humans are — and without the best science, we won’t get the best results.”
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Materials provided by University of Cambridge. The original text of this story is licensed under a Creative Commons License. Note: Content may be edited for style and length. More

Research has long strived to develop computers to work as energy efficiently as our brains. A study, led by researchers at the University of Gothenburg, has succeeded for the first time in combining a memory function with a calculation function in the same component. The discovery opens the way for more efficient technologies, everything from mobile phones to self-driving cars.
In recent years, computers have been able to tackle advanced cognitive tasks, like language and image recognition or displaying superhuman chess skills, thanks in large part to artificial intelligence (AI). At the same time, the human brain is still unmatched in its ability to perform tasks effectively and energy efficiently.
“Finding new ways of performing calculations that resemble the brain’s energy-efficient processes has been a major goal of research for decades. Cognitive tasks, like image and voice recognition, require significant computer power, and mobile applications, in particular, like mobile phones, drones and satellites, require energy efficient solutions,” says Johan Åkerman, professor of applied spintronics at the University of Gothenburg.
Important breakthrough
Working with a research team at Tohoko University, Åkerman led a study that has now taken an important step forward in achieving this goal. In the study, now published in the highly ranked journal Nature Materials, the researchers succeeded for the first time in linking the two main tools for advanced calculations: oscillator networks and memristors.
Åkerman describes oscillators as oscillating circuits that can perform calculations and that are comparable to human nerve cells. Memristors are programable resistors that can also perform calculations and that have integrated memory. This makes them comparable to memory cells. Integrating the two is a major advancement by the researchers. More

NIMS has developed an ionic artificial vision device capable of increasing the edge contrast between the darker and lighter areas of an mage in a manner similar to that of human vision. This first-ever synthetic mimicry of human optical illusions was achieved using ionic migration and interaction within solids. It may be possible to use the device to develop compact, energy-efficient visual sensing and image processing hardware systems capable of processing analog signals.
Numerous artificial intelligence (AI) systems developers have recently shown a great deal of interest in research on various sensors and analog information processing systems inspired by human sensory mechanisms. Most AI systems on which research is being conducted require sophisticated software/programs and complex circuit configurations, including custom-designed processing modules equipped with arithmetic circuits and memory. These systems have disadvantages, however, in that they are large and consume a great deal of power.
The NIMS research team recently developed an ionic artificial vision device composed of an array of mixed conductor channels placed on a solid electrolyte at regular intervals. This device simulates the way in which human retinal neurons (i.e., photoreceptors, horizontal cells and bipolar cells) process visual signals by responding to input voltage pulses (equivalent to electrical signals from photoreceptors). This causes ions within the solid electrolyte (equivalent to a horizontal cell) to migrate across the mixed conductor channels, which then changes the output channel current (equivalent to a bipolar cell response). By employing such steps, the device, independent of software, was able to process input image signals and produce an output image with increased edge contrast between darker and lighter areas in a manner similar to the way in which the human visual system can increase edge contrast between different colors and shapes by means of visual lateral inhibition.
The human eye produces various optical illusions associated with tilt angle, size, color and movement, in addition to darkness/lightness, and this process is believed to play a crucial role in the visual identification of different objects. The ionic artificial vision device described here may potentially be used to reproduce these other types of optical illusions. The research team involved hopes to develop visual sensing systems capable of performing human retinal functions by integrating the subject device with other components, including photoreceptor circuits.
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Scientists have developed new materials for next-generation electronics so tiny that they are not only indistinguishable when closely packed, but they also don’t reflect enough light to show fine details, such as colors, with even the most powerful optical microscopes. Under an optical microscope, carbon nanotubes, for example, look grayish. The inability to distinguish fine details and differences between individual pieces of nanomaterials makes it hard for scientists to study their unique properties and discover ways to perfect them for industrial use.
In a new report in Nature Communications, researchers from UC Riverside describe a revolutionary imaging technology that compresses lamp light into a nanometer-sized spot. It holds that light at the end of a silver nanowire like a Hogwarts student practicing the “Lumos” spell, and uses it to reveal previously invisible details, including colors.
The advance, improving color-imaging resolution to an unprecedented 6 nanometer level, will help scientists see nanomaterials in enough detail to make them more useful in electronics and other applications.
Ming Liu and Ruoxue Yan, associate professors in UC Riverside’s Marlan and Rosemary Bourns College of Engineering, developed this unique tool with a superfocusing technique developed by the team. The technique has been used in previous work to observe the vibration of molecular bonds at 1-nanometer spatial resolution without the need of any focusing lens.
In the new report, Liu and Yan modified the tool to measure signals spanning the whole visible wavelength range, which can be used to render the color and depict the electronic band structures of the object instead of only molecule vibrations. The tool squeezes the light from a tungsten lamp into a silver nanowire with near-zero scattering or reflection, where light is carried by the oscillation wave of free electrons at the silver surface.
The condensed light leaves the silver nanowire tip, which has a radius of just 5 nanometers, in a conical path, like the light beam from a flashlight. When the tip passes over an object, its influence on the beam shape and color is detected and recorded.
“It is like using your thumb to control the water spray from a hose,” Liu said, “You know how to get the desired spraying pattern by changing the thumb position, and likewise, in the experiment, we read the light pattern to retrieve the details of the object blocking the 5 nm-sized light nozzle.”
The light is then focused into a spectrometer, where it forms a tiny ring shape. By scanning the probe over an area and recording two spectra for each pixel, the researchers can formulate the absorption and scattering images with colors. The originally grayish carbon nanotubes receive their first color photograph, and an individual carbon nanotube now has the chance to exhibit its unique color.
“The atomically smooth sharp-tip silver nanowire and its nearly scatterless optical coupling and focusing is critical for the imaging,” Yan said. “Otherwise there would be intense stray light in the background that ruins the whole effort. ”
The researchers expect that the new technology can be an important tool to help the semiconductor industry make uniform nanomaterials with consistent properties for use in electronic devices. The new full-color nano-imaging technique could also be used to improve understanding of catalysis, quantum optics, and nanoelectronics.
Liu, Yan, and Ma were joined in the research by Xuezhi Ma, a postdoctoral scholar at Temple University who worked on the project as part of his doctoral research at UCR Riverside. Researchers also included UCR students Qiushi Liu, Ning Yu, Da Xu, Sanggon Kim, Zebin Liu, Kaili Jiang, and professor Bryan Wong. The paper, titled “6 nm super-resolution optical transmission and scattering spectroscopic imaging of carbon nanotubes using a nanometer-scale white light source,” is available here.
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Materials provided by University of California – Riverside. Original written by Holly Ober. Note: Content may be edited for style and length. More

A cell stores all of its genetic material in its nucleus, in the form of chromosomes, but that’s not all that’s tucked away in there. The nucleus is also home to small bodies called nucleoli — clusters of proteins and RNA that help build ribosomes.
Using computer simulations, MIT chemists have now discovered how these bodies interact with chromosomes in the nucleus, and how those interactions help the nucleoli exist as stable droplets within the nucleus.
Their findings also suggest that chromatin-nuclear body interactions lead the genome to take on a gel-like structure, which helps to promote stable interactions between the genome and transcription machineries. These interactions help control gene expression.
“This model has inspired us to think that the genome may have gel-like features that could help the system encode important contacts and help further translate those contacts into functional outputs,” says Bin Zhang, the Pfizer-Laubach Career Development Associate Professor of Chemistry at MIT, an associate member of the Broad Institute of Harvard and MIT, and the senior author of the study.
MIT graduate student Yifeng Qi is the lead author of the paper, which appears today in Nature Communications.
Modeling droplets
Much of Zhang’s research focuses on modeling the three-dimensional structure of the genome and analyzing how that structure influences gene regulation. More

Forcing electrons to flow perpendicularly to a heat flow requires an external magnetic field – this is known as the Nernst effect. In a permanently magnetized material (a ferromagnet), an anomalous Nernst effect (ANE) exists that can generate electricity from heat even without a magnetic field. The anomalous Nernst effect scales with the magnetic moment of the ferromagnet. An antiferromagnet, with two compensating magnetic sublattices shows no external magnetic moment and no measurable external magnetic field and therefore should not exhibit any ANE. However, we have recently understood that by the new concept of topology can be applied to achieve large Nernst effects in magnets. In particular, we have learned that the quantity known as the Berry phase is related to the ANE and can greatly increase it. However, the ANE in antiferromagnets is still largely unexplored, in part because the ANE was not thought to exist. Remarkably, a joint research team from the Max Planck Institute for Chemical Physics of Solids in Dresden, Germany, together with collaborators at the Ohio State University and the University of Cincinnati, has found a large anomalous Nernst effect, larger than is known in almost all ferromagnets in YbMnBi2, an antiferromagnet.

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Researchers from Basel and Bochum have succeeded in addressing an apparently unattainable energy transition in an artificial atom using laser light. Making use of the so-called radiative Auger process, they were the first team to specifically excite it. In this process, an electron falls from a higher to a lower energy level and, as a result, emits its energy partly in the form of light and partly by transferring it to another electron. The artificial atoms are narrowly defined areas in semiconductors that could one day form the basis for quantum communication. The findings are described by the team from the University of Basel and Ruhr-Universität Bochum together with colleagues from Münster and Wroclaw in “Nature Communications,” published online on 12 November 2021.
Electrons move between energy states
Atoms consist of a nucleus and electrons that travel around the nucleus. These electrons can assume different energy levels. Electrons that are more tightly bound to the nucleus, i.e. closer to it, have a lower energy than electrons that are further away from the nucleus. However, the electrons can’t assume any arbitrary energy levels — only certain levels are possible.
If an electron acquires energy, for example by absorbing a light particle, i.e. photon, it can be raised to a higher energy level. If an electron falls to a lower energy level, energy is released. This energy can be emitted in the form of a photon. But it can also be transferred to one of the other electrons; in this case, only some of the energy is released as light, the rest is absorbed by the other electron. This process is known as the radiative Auger process.
Exciting a unique energy transition with two lasers
By irradiating light particles, electrons can not only be lifted to a higher energy level; they can also be stimulated to give off energy by an incident light particle. The energy of the incident light particle must always correspond exactly to the difference in the two energy levels between which the electron is to be transferred. The researchers have used two lasers: one moved electrons between a low and a high energy level; the other between the high and a medium energy level. This middle energy level corresponds to a non-equilibrium level: the transfer to the middle level doesn’t exist without a radiative Auger process. In addition, a transition between the low and the medium energy level shouldn’t have occurred, because the relevant light was not irradiated. However, precisely this seemingly impossible transition occurred in reality due to the energy transfer from one electron to another in the radiative Auger process.
The ultrapure semiconductor samples for the experiment were produced by Dr. Julian Ritzmann at Ruhr-Universität Bochum under the supervision of Dr. Arne Ludwig at the Chair for Applied Solid State Physics headed by Professor Andreas Wieck. The measurements were carried out by a team from the University of Basel run by Clemens Spinnler, Liang Zhai, Giang Nguyen and Dr. Matthias Löbl in the group headed by Professor Richard Warburton.
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Most human diseases can be traced to malfunctioning parts of a cell — a tumor is able to grow because a gene wasn’t accurately translated into a particular protein or a metabolic disease arises because mitochondria aren’t firing properly, for example. But to understand what parts of a cell can go wrong in a disease, scientists first need to have a complete list of parts.
By combining microscopy, biochemistry techniques and artificial intelligence, researchers at University of California San Diego School of Medicine and collaborators have taken what they think may turn out to be a significant leap forward in the understanding of human cells.
The technique, known as Multi-Scale Integrated Cell (MuSIC), is described November 24, 2021 in Nature.
“If you imagine a cell, you probably picture the colorful diagram in your cell biology textbook, with mitochondria, endoplasmic reticulum and nucleus. But is that the whole story? Definitely not,” said Trey Ideker, PhD, professor at UC San Diego School of Medicine and Moores Cancer Center. “Scientists have long realized there’s more that we don’t know than we know, but now we finally have a way to look deeper.”
Ideker led the study with Emma Lundberg, PhD, of KTH Royal Institute of Technology in Stockholm, Sweden and Stanford University.
In the pilot study, MuSIC revealed approximately 70 components contained within a human kidney cell line, half of which had never been seen before. In one example, the researchers spotted a group of proteins forming an unfamiliar structure. Working with UC San Diego colleague Gene Yeo, PhD, they eventually determined the structure to be a new complex of proteins that binds RNA. The complex is likely involved in splicing, an important cellular event that enables the translation of genes to proteins, and helps determine which genes are activated at which times. More