Lifeboat Foundation

A research group led by Professor Kenji Ohmori at the Institute for Molecular Science, National Institutes of Natural Sciences are using an artificial crystal of 30,000 atoms aligned in a cubic array with a spacing of 0.5 micron, cooled to near absolute zero temperature. By manipulating the atoms with a special laser light that blinks for 10 picoseconds, they succeeded in executing quantum simulation of a model of magnetic materials.

Their novel “ultrafast quantum computer” scheme demonstrated last year was applied to quantum simulation. Their achievement shows that their novel “ultrafast quantum simulator” is an epoch-making platform, as it can avoid the issue of external noise, one of the biggest concerns for quantum simulators. The “ultrafast quantum simulator” is expected to contribute to the design of functional materials and the resolution of social problems.

Their results were published online in Physical Review Letters.

Pat Bennett’s prescription is a bit more complicated than “Take a couple of aspirins and call me in the morning.” But a quartet of baby-aspirin-sized sensors implanted in her brain are aimed at addressing a condition that’s frustrated her and others: the loss of the ability to speak intelligibly. The devices transmit signals from a couple of speech-related regions in Bennett’s brain to state-of-the-art software that decodes her brain activity and converts it to text displayed on a computer screen.

Bennett, now 68, is a former human resources director and onetime equestrian who jogged daily. In 2012, she was diagnosed with amyotrophic lateral sclerosis, a progressive neurodegenerative disease that attacks neurons controlling movement, causing physical weakness and eventual paralysis.

Continue reading “Brain implants, software guide speech-disabled person’s intended words to computer screen” »

The human brain, with its intricate network of approximately 86 billion neurons, is arguably among the most complex specimens scientists have ever encountered. It holds an immense, yet currently immeasurable, wealth of information, positioning it as the pinnacle of computational devices.

Grasping this level of intricacy is challenging, making it essential for us to employ advanced technologies that can decode the minute, intricate interactions happening within the brain at microscopic levels. Thus, imaging emerges as a pivotal instrument in the realm of neuroscience.

The new imaging and virtual reconstruction technology developed by Johann Danzl’s group at ISTA is a big leap in imaging brain activity and is aptly named LIONESS – Live Information Optimized Nanoscopy Enabling Saturated Segmentation. LIONESS is a pipeline to image, reconstruct, and analyze live brain tissue with a comprehensiveness and spatial resolution not possible until now.

This is leading to even better brain engineering 👏 🙌 👌 😀 😄.

Computer-augmented brains, cures to blindness, and rebuilding the brain after injury all sound like science fiction. Today, these disruptive technologies aren’t just for Netflix, “Terminator,” and comic book fodder — in recent years, these advances are closer to reality than some might realize, and they have the ability to revolutionize neurological care.

Neurologic disease is now the world’s leading cause of disability, and upwards of 11 million people have some form of permanent neurological problem from traumatic brain injuries and stroke. For example, if a traumatic brain injury has damaged the motor cortex — the region of the brain involved in voluntary movements — patients could become paralyzed, without hope of regaining full function. Or some stroke patients can suffer from aphasia, the inability to speak or understand language, due to damage to the brain regions that control speech and language comprehension.

Continue reading “Science Fiction Meets Neuro-Reality: Organoids to Rebuild the Brain” »

We often believe computers are more efficient than humans. After all, computers can complete a complex math equation in a moment and can also recall the name of that one actor we keep forgetting. However, human brains can process complicated layers of information quickly, accurately, and with almost no energy input: recognizing a face after only seeing it once or instantly knowing the difference between a mountain and the ocean. These simple human tasks require enormous processing and energy input from computers, and even then, with varying degrees of accuracy.

Creating brain-like computers with minimal energy requirements would revolutionize nearly every aspect of modern life. Funded by the Department of Energy, Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C) — a nationwide consortium led by the University of California San Diego — has been at the forefront of this research.

UC San Diego Assistant Professor of Physics Alex Frañó is co-director of Q-MEEN-C and thinks of the center’s work in phases. In the first phase, he worked closely with President Emeritus of University of California and Professor of Physics Robert Dynes, as well as Rutgers Professor of Engineering Shriram Ramanathan. Together, their teams were successful in finding ways to create or mimic the properties of a single brain element (such as a neuron or synapse) in a quantum material.

Most of today’s EVs use lithium-ion batteries, the same kind you’ll find in your smartphone or laptop. These batteries all have two electrodes (one positive and one negative), and the negative one is usually made of graphite.

While the battery is being charged, the lithium ions flow from the side of the battery with the positive electrode to the side with the negative electrode. If the charging happens too fast, the flow can be disrupted, causing the battery to short circuit.

StoreDot’s EV battery replaces the graphite electrode with one made from nanoparticles based on the chemical element germanium — this allows the ions to flow more smoothly and quickly, enabling a faster charge.

Advance lays the groundwork for miniature devices for spectroscopy, communications, and quantum computing. Researchers have created chip-based photonic resonators that operate in the ultraviolet (UV) and visible regions of the spectrum and exhibit a record low UV light loss. The new resonators lay the groundwork for increasing the size, complexity, and fidelity of UV photonic integrated circuit (PIC) design, which could enable new miniature chip-based devices for applications such as spectroscopic sensing, underwater communication, and quantum information processing.

Founded in 2021, Virginia-based Procyon Photonics is a startup aiming to change the future of computing hardware with its focus on optical computing. What makes the company unique is that their entire team consists of current high school students, and its co-founder, CEO, and CTO, Sathvik Redrouthu, holds the distinction of being the world’s youngest CEO in the photonic and optical computing sector.

Optical computing represents an innovative leap from traditional computing, which relies on electrons moving through wires and transistors. Instead, this relatively nascent field seeks to harness photons — particles of light — as the fundamental elements in computational processes. The promise of optical computing is compelling enough that industry giants like IBM and Microsoft, among others, are heavily investing in its research and development.

Procyon is attempting to differentiate itself in this competitive landscape not just by its youth, but with their technology. The team is pioneering a unique, industry-leading optical chip, and has published a conference paper detailing how a specialized form of matrix algebra could be executed on an optoelectronic chip.

In a press release, Bujack, who creates scientific visualizations at Los Alamos National Laboratory, called the current mathematical models used for color perceptions incorrect and requiring a “paradigm shift.”

A surprise finding

Being able to accurately model human color perception has a tremendous impact on automating image processing, computer graphics, and visualization. Bujack’s team first set out to develop algorithms that would automatically enhance color maps used in data visualization to make it easier to read them.