Lifeboat Foundation

Oct. 5, 2023: (Spaceweather.com) The sun is about to lose something important: Its magnetic poles.

Recent measurements by NASA’s Solar Dynamic Observatory reveal a rapid weakening of magnetic fields in the polar regions of the sun. North and south magnetic poles are on the verge of disappearing. This will lead to a complete reversal of the sun’s global magnetic field perhaps before the end of the year.

An artist’s concept of the sun’s dipolar magnetic field. Credit: NSF/AURA/NSO.

Researchers have developed an easy-to-use optical chip that can configure itself to achieve various functions. The positive real-valued matrix computation they have achieved gives the chip the potential to be used in applications requiring optical neural networks. Optical neural networks can be used for a variety of data-heavy tasks such as image classification, gesture interpretation and speech recognition.

Photonic integrated circuits that can be reconfigured after manufacturing to perform different functions have been developed previously. However, they tend to be difficult to configure because the user needs to understand the internal structure and principles of the chip and individually adjust its basic units.

“Our new chip can be treated as a black box, meaning users don’t need to understand its internal structure to change its function,” said research team leader Jianji Dong from Huazhong University of Science and Technology in China. “They only need to set a training objective, and, with computer control, the chip will self-configure to achieve the desired functionality based on the input and output.”

The many-body interaction of charges (electrons) and nuclei (phonons) plays a critical role in determining the properties and functionalities of molecules and solids. The exact correlated motion of these particles gives rise to different conductivity, energy storage capabilities, phase transitions, and superconductivity. Now, the team of ICREA Prof. at ICFO Jens Biegert has developed attosecond soft X-ray core-level spectroscopy as a method to observe the correlated interaction between charges and phonons in real time.

Attosecond soft X-ray spectroscopy relies on the use of ultrashort pulses with photon energies that cover the entire water-window range. Through high-order harmonic generation with an intense few-cycle short-wavelength infrared pulse, the team has successfully generated a bright 165 attosecond pulse with photon energies of up to 600 eV. By directing this ultrashort soft X-ray pulse into the sample, the high-energy photons can excite the electrons in the K-shell or L-shell to unoccupied or continuum states.

This soft X-ray absorption spectroscopy provides researchers with a powerful tool for unraveling the electronic and structural characteristics of the material at the same time.

Imagine that you have a secret decoder ring that you can use to decipher a secret message with important clues about things around you: where they came from, why they are there, and what will become of them in the future. Now imagine that the secret decoder ring is actually a sensor that can be flown in space to unravel secrets about the matter in the solar system. Where did this matter originate, how did it become energized, and how could it impact humans living on Earth and traveling in space?

The Solar Wind Pickup Ion Composition Energy Spectrometer (SPICES) is like a decoder ring for the plasma (gas consisting of electrically charged particles) in the solar system. It has the potential to reveal important information about how the sun behaves and interacts with planets and their atmospheres, and how the solar system is impacted by its own motion through interstellar space.

The universe is mostly made of hydrogen, but the elements that make up life as well as the planets, comets, and many other celestial bodies are heavier than hydrogen. In fact, these heavier elements, although not as abundant, can hold the key to understanding how numerous processes in the universe work. In our solar system, these “heavy elements”—which are called “heavy ions” when they are electrically charged—can help us trace plasma to its origin at planets, comets, the sun and solar atmosphere, and even to interstellar space.

Speech production is a complex neural phenomenon that has left researchers explaining it tongue-tied. Separating out the complex web of neural regions controlling precise muscle movement in the mouth, jaw and tongue with the regions processing the auditory feedback of hearing your own voice is a complex problem, and one that has to be overcome for the next generation of speech-producing protheses.

Now, a team of researchers from New York University have made key discoveries that help untangle that web, and are using it to build vocal reconstruction technology that recreates the voices of patients who have lost their ability to speak.

The team, co-led by Adeen Flinker, Associate Professor of Biomedical Engineering at NYU Tandon and Neurology at NYU Grossman School of Medicine, and Yao Wang, Professor of Biomedical Engineering and Electrical and Computer Engineering at NYU Tandon, as well as a member of NYU WIRELESS, created and used complex neural networks to recreate speech from brain recordings, and then used that recreation to analyze the processes that drive human speech.

Europe is pushing to create a network infrastructure based on quantum physics.

In May 2023, Dr. Benjamin Lanyon at the University of Innsbruck in Austria took an important step toward creating a new kind of internet: he transferred information along an optical fiber 50 kilometers long using the principles of quantum physics.

Information in quantum physics differs from the units of data—binary digits—stored and processed by computers that form the core of the current World Wide Web. The quantum physics realm covers the properties and interactions of molecules, atoms and even smaller particles such as electrons and photons.

Absorption spectroscopy is an analytical chemistry tool that can determine if a particular substance is present in a sample by measuring the intensity of the light absorbed as a function of wavelength. Measuring the absorbance of an atom or molecule can provide important information about electronic structure, quantum state, sample concentration, phase changes or composition changes, among other variables, including interaction with other molecules and possible technological applications.

Molecules with a high probability of simultaneously absorbing two photons of low-energy light have a wide array of applications: in molecular probes for high-resolution microscopy, as a substrate for data storage in dense three-dimensional structures, or as vectors in medicinal treatments, for example.

Studying the phenomenon by means of direct experimentation is difficult, however, and computer simulation usually complements spectroscopic characterization. Simulation also provides a microscopic view that is hard to obtain in experiments. The problem is that simulations involving relatively large molecules require several days of processing by supercomputers or months by conventional computers.

In a recent publication in EPJ Quantum Technology, Le Bin Ho from Tohoku University’s Frontier Institute for Interdisciplinary Sciences has developed a technique called time-dependent stochastic parameter shift in the realm of quantum computing and quantum machine learning. This breakthrough method revolutionizes the estimation of gradients or derivatives of functions, a crucial step in many computational tasks.

Typically, computing derivatives requires dissecting the function and calculating the rate of change over a small interval. But even classical computers cannot keep dividing indefinitely. In contrast, quantum computers can accomplish this task without having to discrete the function. This feature is achievable because quantum computers operate in a realm known as “quantum space,” characterized by periodicity, and no need for endless subdivisions.

One way to illustrate this concept is by comparing the sizes of two elementary schools on a map. To do this, one might print out maps of the schools and then cut them into smaller pieces. After cutting, these pieces can be arranged into a line, with their total length compared (see Figure 1a). However, the pieces may not form a perfect rectangle, leading to inaccuracies. An infinite subdivision would be required to minimize these errors, an impractical solution, even for classical computers.

Whether improperly closing a door or shanking a kick in soccer, our brains tell us when we’ve made a mistake because these sounds differ from what we expect to hear. While it’s long been established that our neurons spot these errors, it has been unclear whether there are brain cells that have only one job—to signal when a sound is unexpected or “off.”

A team of New York University neuroscientists has now identified a class of neurons—what it calls “prediction-error neurons”—that are not responsive to sounds in general, but only respond when sounds violate expectations, thereby sending a message that a mistake has been made.

“Brains are remarkable at detecting what’s happening in the world, but they are even better at telling you whether what happened was expected or not,” explains David Schneider, an assistant professor in NYU’s Center for Neural Science and the senior author of the study, which appears in JNeurosci. “We found that there are specific neurons in the brain that don’t tell you what happened, but instead tell you what went wrong.”