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Category: computing – Page 2

Saturday Citations: Yet another solution for universal expansion; computing with brain organoids

This week, researchers reported the discovery of four Late Bronze Age stone megastructures likely used for trapping herds of wild animals. Physicists have proven that a central law of thermodynamics does not apply to atomic-scale objects that are linked via quantum correlation. And two Australian Ph.D. students coded a software solution for the James Webb Space Telescope’s Aperture Masking Interferometer, which has been producing blurry images.

Additionally, researchers are networking tiny human brain organoids into a computing substrate; have proposed that environmental lead exposure may have influenced early human brain evolution; and physicists have provided a to explain accelerating universal expansion without :

Algorithm maps genetic connection between Alzheimer’s and specific neurons

The number of people living with dementia worldwide was estimated at 57 million in 2021 with nearly 10 million new cases recorded each year. In the U.S., dementia impacts more than 6 million lives, and the number of new cases is expected to double over the next few decades, according to a 2025 study. Despite advancements in the field, a full understanding of disease-causing mechanisms is still lacking.

To address this gap, Rice University researchers and collaborators at Boston University have developed a that can help identify which specific types of cells in the body are genetically linked to complex human traits and diseases, including in forms of dementia such as Alzheimer’s and Parkinson’s.

Known as “Single-cell Expression Integration System for Mapping genetically implicated Cell types,” or seismic, the tool helped the team hone in on genetic vulnerabilities in memory-making brain cells that link them to Alzheimer’s ⎯ the first to establish an association based on a genetic link between the disease and these specific neurons. The algorithm outperforms existing tools for identifying that are potentially relevant in complex diseases and is applicable in disease contexts beyond dementia.

Quantum Systems Modeled Without Prior Assumptions

An improved algorithm for learning the static and dynamic properties of a quantum system could have applications in quantum computing, simulation, and sensing.

Quantum systems are notoriously hard to study, control, and simulate. One key reason is that their full characterization requires a vast amount of information. Fortunately, in the past decade, scientists have shown that many physical properties of a quantum system can be efficiently predicted using much less information [1, 2]. Moreover, researchers have built quantum sensors that can measure these properties with a much smaller uncertainty compared with the best classical sensors [3]. Nevertheless, it has been difficult to achieve both efficient predictions and precise measurements at the same time. Now, building on previous breakthroughs in the field, Hong-Ye Hu at Harvard University and his colleagues have demonstrated a new algorithm that characterizes quantum systems of any size with optimal efficiency and precision [4].

Researchers help break thermal conductivity barrier with boron arsenide discovery

University of Houston researchers have made a discovery in thermal conductivity that overturns an existing theory that boron arsenide (BAs) couldn’t compete with the heat conduction of a diamond.

Instead, the team found that high-quality crystals can achieve exceeding 2,100 watts per meter per Kelvin (W/mK) at room temperature—possibly higher than diamond, which has been considered the best heat conductor among isotropic materials.

Published in Materials Today, this research challenges existing theories and could reshape our understanding of heat-conducting materials. It could also bring forth a new semiconductor material with much better thermal management in cell phones, high-powered electronics and .

A tiny chip that can help us see deeper into space

A new imaging system could help us see deeper into the universe than ever before. The same powerful technology could also help us analyze the chemical makeup of everyday materials such as food and medicines much faster and with greater accuracy than current methods.

In a study published in the journal Nature, researchers from Tsinghua University in China have introduced a tiny device called RAFAEL (Reconfigurable, Adaptive, FAst and Efficient Lithium-niobate spectro-imager) that uses advanced photonics to capture light in exceptional detail with high speed.

RAFAEL is designed to dramatically improve spectroscopy, the technique used to study the and chemical composition of matter. It is used for everything from mapping to checking for contaminants in water and diagnosing diseases, and it works by breaking down the light that comes from an object and analyzing the different colors (wavelengths). While incredibly powerful, traditional spectrometers often face a trade-off: To get very fine detail you have to block much of the light. Or if you let in a lot of light, you lose resolution or sensitivity.

New organic thin-film tunnel transistors for wearable and other small electronics

To meet the growing demands of flexible and wearable electronic systems, such as smart watches and biomedical sensors, electronics engineers are seeking high-performance transistors that can efficiently modulate electrical current while maintaining mechanical flexibility.

Thin-film transistors (TFTs), which are comprised of thin layers of conducting, semiconducting and insulating materials, have proved to be particularly promising for large-area flexible and wearable electronics, while also enabling the creation of thinner displays and advanced sensors.

Despite their potential, the energy-efficiency with which these transistors can switch has proved difficult to improve. This is due to the so-called thermionic limit, a theoretical threshold that delineates the lowest possible voltage required for a transistor to boost electrical current by a factor of 10 at room temperature when switching between “off” and “on” states.

Quantum theory faces ‘cultural gaps’ as computational limits reshape entanglement understanding

Quantum researchers in the twenty-first century are part of an international network that requires a great deal of interaction and communication. Around one hundred publications on the topic are produced every day, often by authors who work in close collaboration with one another. New developments and discoveries are quickly integrated into the field, usually within a matter of just a few weeks. Researchers immediately proceed to build on these findings with innovative ideas. That is what the day-to-day life in the field of quantum theory looks like as it celebrates the one-hundredth anniversary of the initial development of quantum mechanics.

In honor of this milestone, UNESCO has declared 2025 the International Year of Quantum Science and Technology. One of the latest discoveries in this special year comes from an international research group led by quantum physicist Jens Eisert, professor at Freie Universität Berlin. The group’s surprising findings have made a significant contribution to scientists’ understanding of .

Their study, “Entanglement Theory with Limited Computational Resources,” was recently published in the journal Nature Physics. The article shows that, in practice, the established method used to measure correlations in quantum mechanics might not function exactly as was previously assumed.

[News] Chinese Scientists Developed a Novel Chip, Crossing a Century-Old Hurdle

According to the Institute for Artificial Intelligence at Peking University, a research team led by Researcher Sun Zhong and his collaborators has recently published a paper in the international journal Nature Electronics, reporting a major breakthrough in novel computing architectures.

The team successfully developed a high-precision and scalable analog matrix computing chip based on resistive random-access memory (RRAM). For the first time, the chip achieves analog computation accuracy rival to that of digital systems, improving the precision of traditional analog computing by an astonishing five orders of magnitude.

Performance evaluations show that when solving large-scale MIMO signal detection and other key scientific problems, the chip’s computational throughput and energy efficiency are hundreds to thousands of times higher than those of today’s top-tier digital processors (GPU).

Dual torque from electron spins drives magnetic domain wall displacement

A research team has taken a major step forward in the field of spintronics, a technology that uses not only the charge but also the spin of electrons to create faster, smarter, and more energy-efficient electronic devices. Their discovery could pave the way for the next generation of memory chips that combine high speed with low power consumption.

In spintronic memory, information is stored using tiny magnetic regions called . A magnetic domain with its magnetic moments pointing upward represents a “1,” while one pointing downward represents a “0.” Data can be read or written by shifting these domains with an .

The boundaries between them, known as domain walls, play a crucial role, as moving domains means moving these walls. Achieving fast and efficient domain wall motion is essential for developing advanced memories such as magnetic shift registers and three-terminal magnetic random access memories (MRAM).

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