Computers Math

One of the major challenges in particle physics is how to interpret large data sets that consist of many different observables in the context of models with different parameters.
A new paper published in EPJ Plus, authored by Ursula Laa from the Institute of Statistics at BOKU University, Vienna, and German Valencia from the School of Physics and Astronomy, Monash University, Clayton, Australia, looks at the simplification of large data set and many parameter problems using tools to split large parameter spaces into a small number of regions.
“We applied our tools to the so-called B-anomaly problem. In this problem there is a large number of experimental results and a theory that predicts them in terms of several parameters,” Laa says. “The problem has received much attention because the preferred parameters to explain the observations do not correspond to those predicted by the standard model of particle physics, and as such the results would imply new physics.”
Valencia continues by explaining the paper shows how the Pandemonium tool can provide an interactive graphical way to study the connections between characteristics in the observations and regions of parameter space.
“In the B-anomaly problem, for example, we can clearly visualise the tension between two important observables that have been singled out in the past,” Valencia says. “We can also see which improved measurements would be best to address that tension.
“This can be most helpful in prioritising future experiments to address unresolved questions.”
Laa elaborates by explaining that the methods developed and used by the duo are applicable to many other problems, in particular for models and observables that are less well understood than the applications discussed in the paper, such as multi Higgs models.
“A challenge is the visualization of multidimensional parameter spaces, the current interface only allows the user to visualise high dimensional data spaces interactively,” Laa concludes. “The challenge is to automate this, which will be addressed in future work, using techniques from dimension reduction.”
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Lithium-ion batteries, fuel cells and many other devices depend on the high mobility of ions in order to work properly. But there a large number of obstacles to such mobility. A research team led by Jennifer L. M. Rupp of the Technical University of Munich (TUM) and Harry L. Tuller of the Massachusetts Institute of Technology (MIT) have now shown for the first time that light can be used to increase the mobility of ions and improve the performance of such devices.
A charge can be transported by a material in a number of different ways. The most familiar is the electrical conductivity of metals, where the charge is borne by electrons. In many devices, however, ions transport the charge. One example is lithium-ion batteries in which lithium ions move during charging and discharging. Similarly, fuel cells rely on the transport of hydrogen and oxygen ions in order to conduct electricity.
Ceramics are currently being investigated as solid electrolytes for transporting oxygen ions. But: “What we find is that the ionic conductivity — the rate at which the ions can move and, therefore, how efficient the resulting device can be — is often markedly degraded by the fact that the ions get blocked at grain boundaries,” says Prof. Harry L. Tuller of the Massachusetts Institute of Technology.
Light puts ions on the go
In their current publication Tuller and his colleague Jennifer L. M. Rupp, Professor for solid-state electrolyte chemistry at the Technical University of Munich, show how light can be used to reduce the barriers encountered by ions at ceramic grain boundaries.
Many devices based on ion conductivity, such as solid-oxide fuel cells, have to operate at very high temperatures in order for the ions to be able to overcome the grain boundary barriers. Operating temperatures of up to 700° Celsius, however, present their own challenges: Materials age faster and the infrastructure for maintaining these high temperatures is costly. More

Working out isn’t known for being fun. But new active video and virtual reality games may help change that.
Exergaming, or active video gaming, may be the perfect introduction to helping people be more active, according to new research from the University of Georgia.
Just about anyone can find an exergame to match their interests. Dance Dance Revolution, EA Sports Active and Beat Saber are some of the more popular exergames. Active gaming options exist for most gaming consoles, like Xbox and Nintendo. And previous research has shown that exergaming can have physical benefits, particularly when it takes the place of traditional sedentary video gaming.
This study showed that exergamers felt high levels of satisfaction and a sense of autonomy over their exercise regimen.
“When an individual feels autonomous, they’re more likely to exercise or exergame on their own,” said Sami Yli-Piipari, co-author of the study and an associate professor in the Department of Kinesiology in the Mary Frances Early College of Education. “They feel ownership over what they are doing, and they’re doing it for themselves, so it’s more likely they will keep up the activity.”
Traditional exercise, such as weightlifting or running, doesn’t appeal to some people. But they might be open to active video gaming because it doesn’t seem like exercise. It’s just fun. More

Significant advances in artificial intelligence (AI) over the past decade have relied upon extensive training of algorithms using massive, open-source databases. But when such datasets are used “off label” and applied in unintended ways, the results are subject to machine learning bias that compromises the integrity of the AI algorithm, according to a new study by researchers at the University of California, Berkeley, and the University of Texas at Austin.
The findings, published this week in the Proceedings of the National Academy of Sciences, highlight the problems that arise when data published for one task are used to train algorithms for a different one.
The researchers noticed this issue when they failed to replicate the promising results of a medical imaging study. “After several months of work, we realized that the image data used in the paper had been preprocessed,” said study principal investigator Michael Lustig, UC Berkeley professor of electrical engineering and computer sciences. “We wanted to raise awareness of the problem so researchers can be more careful and publish results that are more realistic.”
The proliferation of free online databases over the years has helped support the development of AI algorithms in medical imaging. For magnetic resonance imaging (MRI), in particular, improvements in algorithms can translate into faster scanning. Obtaining an MR image involves first acquiring raw measurements that code a representation of the image. Image reconstruction algorithms then decode the measurements to produce the images that clinicians use for diagnostics.
Some datasets, such as the well-known ImageNet, include millions of images. Datasets that include medical images can be used to train AI algorithms used to decode the measurements obtained in a scan. Study lead author Efrat Shimron, a postdoctoral researcher in Lustig’s lab, said new and inexperienced AI researchers may be unaware that the files in these medical databases are often preprocessed, not raw.
As many digital photographers know, raw image files contain more data than their compressed counterparts, so training AI algorithms on databases of raw MRI measurements is important. But such databases are scarce, so software developers sometimes download databases with processed MR images, synthesize seemingly raw measurements from them, and then use those to develop their image reconstruction algorithms. More

One of the ultimate goals of medical science is to develop personalized disease diagnostics and therapeutics. With a patient’s genetic information, doctors could tailor treatments to individuals, leading to safer and more effective care.
Recent work from a team of Northwestern Engineering researchers has moved the field closer to realizing this future.
Led by Professor Horacio Espinosa, the research team developed a new version of its Nanofountain Probe Electroporation (NFP-E), a tool used to deliver molecules into single-cells using electricity. The enhanced method leverages artificial intelligence (AI) to execute cell engineering tasks such as cell nuclei localization and probe detection. Other processes such as probe motion, probe-to-cell contact detection, and electroporation-mediated delivery of foreign cargo into single cells are also automated, minimizing user intervention.
“NFP-E can handle small starting samples without any significant cell loss in the entire protocol,” said Espinosa, James N. and Nancy J. Farley Professor in Manufacturing and Entrepreneurship at the McCormick School of Engineering and the study’s corresponding author. “This is an advantage over other cell engineering methods such as bulk electroporation, which require millions of cells and lead to significant cell losses. The automated NFP-E, combined with its ability to selectively target and manipulate single cells in micro-arrays, can be useful in fundamental research, such as deciphering intracellular dynamics and cell-to-cell communication studies as well as biological applications such as cell line generation.”
Espinosa and graduate students Prithvijit Mukherjee, Cesar A. Patino, and Nibir Pathak reported their work in the paper “Deep Learning Assisted Automated Single Cell Electroporation Platform for Effective Genetic Manipulation of Hard-to-Transfect Cells” published March 21 in Small.
“Genetic manipulation of human induced pluripotent stem cells (hiPSCs) by introducing exogenous cargo has a wide range of applications in disease diagnostics, therapeutic discovery, and regenerative medicine,” said Mukherjee, a PhD student in the Espinosa group who is joining the microfluidics group at Illumina. More

In a world first, a team co-led by a physicist at City University of Hong Kong (CityU) has discovered that excitons — excited electrons bound to empty electron “holes” — can exist stably and travel rapidly through metal. Because excitons can be generated by energy from light and have no electrical charge, this discovery makes them potential candidates as a higher-speed alternative to free electrons as a carrier of digital information.
Excitons form when certain materials absorb energy from light to excite electrons, the negatively charged particles in atoms. The electrons are boosted to a higher energy level to leave positively charged spaces or “holes” in their original position. Owing to electrostatic attraction, a hole and an excited electron can pair up without recombining, forming an exciton that behaves like an uncharged particle.
“When an exciton’s electron recombines with a hole, energy is emitted as light, which could be harnessed for data transfer in the optoelectronics industry,” says team co-leader Dr Ma Junzhang, Assistant Professor in the CityU Department of Physics. “Excitons would be better data carriers than free electrons, whose negative charge slows them down, but excitons are very unstable, especially in metals. In fact, before our study, stable and mobile excitons were thought to be impossible in metals.”
The researchers succeeded in generating and detecting excitons in metal because of a combination of optimal test conditions and unique characteristics of their chosen material, tantalum triselenide, TaSe3. The research was headed by CityU and the Paul Scherrer Institute (PSI) in Switzerland, and the results were published in Nature Materials in an article titled “Multiple mobile excitons manifested as sidebands in quasi-one-dimensional metallic TaSe3.” The joint corresponding authors of the paper were Dr Ma Junzhang, and Professor Shi Ming and Dr Markus Müller from PSI. Collaborators included researchers from Rutgers University, Princeton University, Stanford University, and other institutions.
Importance of excitons as robust information carriers
The exciton is expected to play an important role in the future of information transmission thanks to both its charge neutrality and ability to move through a solid. Unlike negatively charged free electrons, excitons are unhindered by external electric fields, magnetic fields, and defects in the surrounding material. More

A research team reported the direct measurement of dielectric tensors of anisotropic structures including the spatial variations of principal refractive indices and directors. The group also demonstrated quantitative tomographic measurements of various nematic liquid-crystal structures and their fast 3D nonequilibrium dynamics using a 3D label-free tomographic method. The method was described in Nature Materials.
Light-matter interactions are described by the dielectric tensor. Despite their importance in basic science and applications, it has not been possible to measure 3D dielectric tensors directly. The main challenge was due to the vectorial nature of light scattering from a 3D anisotropic structure. Previous approaches only addressed 3D anisotropic information indirectly and were limited to two-dimensional, qualitative, strict sample conditions or assumptions.
The research team developed a method enabling the tomographic reconstruction of 3D dielectric tensors without any preparation or assumptions. A sample is illuminated with a laser beam with various angles and circularly polarization states. Then, the light fields scattered from a sample are holographically measured and converted into vectorial diffraction components. Finally, by inversely solving a vectorial wave equation, the 3D dielectric tensor is reconstructed.
Professor YongKeun Park said, “There were a greater number of unknowns in direct measuring than with the conventional approach. We applied our approach to measure additional holographic images by slightly tilting the incident angle.”
He said that the slightly tilted illumination provides an additional orthogonal polarization, which makes the underdetermined problem become the determined problem. “Although scattered fields are dependent on the illumination angle, the Fourier differentiation theorem enables the extraction of the same dielectric tensor for the slightly tilted illumination,” Professor Park added.
His team’s method was validated by reconstructing well-known liquid crystal (LC) structures, including the twisted nematic, hybrid aligned nematic, radial, and bipolar configurations. Furthermore, the research team demonstrated the experimental measurements of the non-equilibrium dynamics of annihilating, nucleating, and merging LC droplets, and the LC polymer network with repeating 3D topological defects.
“This is the first experimental measurement of non-equilibrium dynamics and 3D topological defects in LC structures in a label-free manner. Our method enables the exploration of inaccessible nematic structures and interactions in non-equilibrium dynamics,” first author Dr. Seungwoo Shin explained.
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Researchers at Linköping University have, by way of a number of theoretical calculations, shown that magnesium diboride becomes superconductive at a higher temperature when it is stretched. The discovery is a big step toward finding superconductive materials that are useful in real-world situations.
“Magnesiumdiboride or MgB2 is an interesting material. It’s a hard material that is used for instance in aircraft production and normally it becomes superconductive at a relatively high temperature, 39 K, or -234 C°,” says Erik Johansson, who recently completed his doctorate at the Division of Theoretical Physics.
Erik Johansson is also principal author of an article published in the Journal of Applied Physics that have attracted broad attention. The results have been identified by the editor as particularly important for the future.
“Magnesium boride has an uncomplicated structure which means that the calculations on the supercomputers here at the National Supercomputer Centre in Linköping can focus on complex phenomena like superconductivity,” he says.
Access to renewable energy is fundamental for a sustainable world, but even renewable energy disappears in the form of losses during transmission in the electrical networks. These losses are due to the fact that even materials that are good conductors have a certain resistance, resulting in losses in the form of heat. For this reason, scientists worldwide are trying to find materials that are superconductive, that is, that conduct electricity with no losses at all. Such materials exist, but superconductivity mostly arises very close to absolute 0, i.e. 0 K or -273,15 °C. Many years of research have resulted in complicated new materials with a maximum critical temperature of maybe 200 K, that is, -73 °C. At temperatures under the critical temperature, the materials become superconductive. Research has also shown that superconductivity can be achieved in certain metallic materials at extremely high pressure.
If the scientists are successful in increasing the critical temperature, there will be greater opportunities to use the phenomenon of superconductivity in practical applications.
“The main goal is to find a material that is superconductive at normal pressure and room temperature. The beauty of our study is that we present a smart way of increasing the critical temperature without having to use massively high pressure, and without using complicated structures or sensitive materials. Magnesium diboride behaves in the opposite way to many other materials, where high pressure increases the ability to superconduct. Instead, here we can stretch the material by a few per cent and get a huge increase in the critical temperature,” says Erik Johansson.
In the nanoscale, the atoms vibrate even in really hard and solid materials. In the scientists’ calculations of magnesium diboride, it emerges that when the material is stretched, the atoms are pulled away from each other and the frequency of the vibrations changes. This means that in this material, the critical temperature increases — in one case from 39 K to 77 K. If magnesium diboride is instead subjected to high pressure, its superconductivity decreases.
The discovery of this phenomenon paves the way for calculations and tests of other similar materials or material combinations that can increase the critical temperature further.
“One possibility could be to mix magnesium diboride with another metal diboride, creating a nanolabyrinth of stretched MgB2 with a high superconductive temperature,” says Björn Alling, docent and senior lecturer at the Division of Theoretical Physics and director of the National Supercomputer Centre at Linköping University.
The research has been funded by the Knut and Alice Wallenberg Foundation, the Swedish Research Council and the Swedish Foundation for Strategic Research, among others. It has been conducted with support from the government’s strategic venture, Advanced Functional Materials, AFM, at Linköping University.
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Materials provided by Linköping University. Original written by Monica Westman Svenselius. Note: Content may be edited for style and length. More