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Sony is ending recordable disc production.

The treated mice were known as “supermodel grannies” in the lab because of their youthful appearance.

They were healthier, stronger and developed fewer cancers than their unmedicated peers.

The drug is already being tested in people, but whether it would have the same anti-ageing effect is unknown.

The Department of Energy’s Oak Ridge National Laboratory has publicly released a new set of additive manufacturing data that industry and researchers can use to evaluate and improve the quality of 3D-printed components. The breadth of the datasets can significantly boost efforts to verify the quality of additively manufactured parts using only information gathered during printing, without requiring expensive and time-consuming post-production analysis.

Data has been routinely captured over a decade at DOE’s Manufacturing Demonstration Facility, or MDF, at ORNL, where early-stage research in advanced manufacturing coupled with comprehensive analysis of the resulting components has created a vast trove of information about how 3D printers perform. Years of experience pushing the boundaries of 3D printing with novel materials, machines and controls have provided ORNL with the unique ability to develop and share comprehensive datasets. The newest dataset is now available for free through an online platform.

The conventional manufacturing industry benefits from centuries of quality-control experience. However, additive manufacturing is a newer, non-traditional approach that typically relies on expensive evaluation techniques for monitoring the quality of parts. These techniques might include destructive mechanical testing or non-destructive X-ray computed tomography, which creates detailed cross-sectional images of objects without damaging them.

Qichen Fu, Minsik Cho, Thomas Merth, Sachin Mehta, Mohammad Rastegari, Mahyar Najibi Apple & Meta 2024

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At sea, biologists discovered microbial partners that together produce nitrogen, a nutrient essential for life. The pair are in the process of merging into a single organism.

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Implementing error correction in a quantum computer requires putting together a lot of different things. Of course, you want to start with good physical qubits that have as low a physical error rate that you can achieve. You want to add in an error correction algorithm, like the surface code, color code, q-LDPC, or others that can be implemented in your architecture, and you need a fast real time error decoder that can look at the circuit output and very quickly determine what the error is so it can be corrected. The error decoder portion doesn’t get as much attention in the media as the other things, but it is a very critical portion of the solution. Riverlane is concentrating on providing products for this with a series of solutions they name Deltaflow which consists of both a classical ASIC chip along with software. The Deltaflow solution consists of a powerful error decoding layer for identifying errors and sending back corrective instructions, a universal interface that communicates with the computer;s control system, and a orchestration layer for coordinating activities.

Riverlane has released its Deltaflow Error Correction Stack Roadmap that show yearly updates to the technology to support an increase in the number of QuOps (error free Quantum Operations) by 10X every year. We reported last year on a chip called DD1 that is part of their Deltaflow 1 solution that is capable of supporting 1,000 QuOps using a surface code error correction algorithm. And now, Riverlane is defining solutions that will achieve 10,000 QuOps with Deltaflow 2 later this year, 100,000 QuOps with Deltaflow 3 in 2025, and 1,000,000 QuOps, also called MegaQuops in 2026, with their Deltaflow Mega solution.

One characteristic that Riverlane is emphasizing in these designs is to perform the decoding in real time in order to keep the latencies low. Although it is fine for an academic paper to send the ancilla data off to a classical computer and have it determine the error, it might take milliseconds for the operation to complete. That won’t cut it in a production environment running real jobs. With their Deltaflow chips, these operations can be performed at megahertz rates and Riverlane has implemented techniques such as a streaming, sliding window, and parallized decoding approaches to increase the throughput of the decoder chips as much as possible. In future chips they will be implementing “fast logic” capabilities for Clifford gates using approaches including lattice surgery and transversal CZ gates.

For the first time, scientists have discovered that a small region of our brain shuts down to take microsecond-long naps while we’re awake. What’s more, these same areas ‘flicker’ awake while we’re asleep. These new findings could offer pivotal insights into neurodevelopmental and neurodegenerative diseases, which are linked to sleep dysregulation.

Scientists from Washington University in St. Louis (WashU) and the University of California Santa Cruz (UCSC) made these findings by accident, noticing how brain waves in one tiny area of the brain shut down suddenly for just milliseconds when we’re awake. And in this same region, those brain waves jolt suddenly, for the same amount of time, when we’re asleep.

“With powerful tools and new computational methods, there’s so much to be gained by challenging our most basic assumptions and revisiting the question of ‘what is a state?’” said Keith Hengen, Assistant Professor of Biology at WashU. “Sleep or wake is the single greatest determinant of your behavior, and then everything else falls out from there. So if we don’t understand what sleep and wake actually are, it seems like we’ve missed the boat.”

Successfully innovating optoelectronic semiconductor devices depends a lot on moving charges and excitons—electron-hole pairs—in specified directions for the purpose of creating fuels or electricity.