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New antibodies target ‘dark side’ of influenza virus protein

Researchers at the National Institutes of Health have identified antibodies targeting a hard-to-spot region of the influenza virus, shedding light on the relatively unexplored “dark side” of the neuraminidase (NA) protein head. The antibodies target a region of the NA protein that is common among many influenza viruses, including H3N2 subtype viruses, and could be a new target for countermeasures. The research, led by scientists at the National Institute of Allergy and Infectious Diseases’ Vaccine Research Center, part of NIH, was published today in Immunity.

Influenza, or flu, sickens millions of people across the globe each year and can lead to severe illness and death. While vaccination against influenza reduces the burden of the disease, updated vaccines are needed each season to provide protection against the many strains and subtypes of the rapidly evolving virus. Vaccines that provide protection against a broad range of could prevent outbreaks of new and reemerging flu viruses without the need for yearly reformulation or vaccinations.

One way to improve influenza vaccines and other countermeasures is to identify new targets on the virus’s surface proteins in “conserved” regions—portions that tend to be relatively unchanged between different strains of the virus. Influenza NA is a surface protein containing a globular head portion and a narrow stalk portion.

Motorola shows off a concept smartphone that can wrap around your wrist

During a demonstration, a Motorola representative showed how the phone could bend in various ways to wrap around a wrist or stand up on a table.

When the phone is wrapped around the wrist, the way information is displayed changes. For example, the apps appear at the top of the screen.

The representative said the phone is “contextually aware” so adapts depending on how it has been bent.

The Fastest AI Chip in the World Explained

Fast and cheap for AI inference (responding to chat prompts with very low latency at very high speeds.)


Discussing how it works, benchmarks, how it compares to other AI accelerators and the future outlook!

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Fabrication of mechanochromic gallium nanostructures by capillary interactions

A process that leverages capillary interactions between oligomers in an elastomeric polydimethylsiloxane substrate and deposited Ga enables the formation of Ga nanodroplets with nanoscale gaps in a single step. Gap-plasmon resonances excited within the nanogaps give rise to structural colours that can be tuned by changing the oligomer content in the substrate or by mechanical stretching.

Exploring the Future of Computing: Neuromorphic Engineering

Understanding Neuromorphic Engineering.

Neuromorphic Engineering draws inspiration from the human brain’s architecture and functioning, aiming to create electronic systems that mimic the brain’s ability to process information in a parallel, energy-efficient, and adaptable manner. Unlike traditional computing, which relies on sequential processing, neuromorphic systems leverage neural networks to enable faster and more efficient computation.

Mimicking the Human Brain.

Limitations of Linear Cross-Entropy as a Measure for Quantum Advantage

Popular Summary.

Unequivocally demonstrating that a quantum computer can significantly outperform any existing classical computers will be a milestone in quantum science and technology. Recently, groups at Google and at the University of Science and Technology of China (USTC) announced that they have achieved such quantum computational advantages. The central quantity of interest behind their claims is the linear cross-entropy benchmark (XEB), which has been claimed and used to approximate the fidelity of their quantum experiments and to certify the correctness of their computation results. However, such claims rely on several assumptions, some of which are implicitly assumed. Hence, it is critical to understand when and how XEB can be used for quantum advantage experiments. By combining various tools from computer science, statistical physics, and quantum information, we critically examine the properties of XEB and show that XEB bears several intrinsic vulnerabilities, limiting its utility as a benchmark for quantum advantage.

Concretely, we introduce a novel framework to identify and exploit several vulnerabilities of XEB, which leads to an efficient classical algorithm getting comparable XEB values to Google’s and USTC’s quantum devices (2% 12% of theirs) with just one GPU within 2 s. Furthermore, its performance features better scaling with the system size than that of a noisy quantum device. We observe that this is made possible because the XEB can highly overestimate the fidelity, which implies the existence of “shortcuts” to achieve high XEB values without simulating the system. This is in contrast to the intuition of the hardness of achieving high XEB values by all possible classical algorithms.