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Scientists from Korea’s POSTECH and the US’ Northeastern University have successfully manipulated light using non-Hermitian meta-gratings, turning optical loss into a beneficial tool. They’ve developed a new method for controlling light direction using specially designed meta-grating couplers. This breakthrough could advance quantum sensor research and lead to a range of new applications, such as disease diagnosis and pollution detection.

Light is a very delicate and vulnerable physical phenomenon. Light can be absorbed or reflected at the surface of a material depending on the matter’s properties or change its form and be converted into thermal energy. Upon reaching a metallic material’s surface, light also tends to lose energy to the electrons inside the metal, a broad range of phenomena we call “optical loss.”

Production of ultra-small optical elements that utilize light in various ways is very difficult since the smaller the size of an optical component results in a greater optical loss. However, in recent years, the non-Hermitian theory, which uses optical loss in an entirely different way, has been applied to optics research. New findings in physics are being made adopting non-Hermitian theory that embraces optical loss, exploring ways to make use of the phenomenon, unlike general physics where optical loss is perceived as an imperfect component of an optical system. A ‘blessing in disguise’ is that which initially seems to be a disaster but which ultimately results in good luck. This research story is a blessing in disguise in physics.

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How many times have you wished you could play back your dream on your computer or phone? With this new discovery, the technology might be closer than you think.

In a research published last week on the arXiv server, researchers at the National University of Singapore and the Chinese University of Hong Kong reported that they have developed a process capable of generating video from brain scans.

Deeply fascinating paper wherein Venkataramani et al. describe how synaptic inputs from neurons onto glioma tumor cells induce electrical activity in the tumors and stimulate their growth and invasiveness. This knowledge could lead to new treatments involving inhibition of the synapses onto gliomas which might provide hope for fighting an otherwise largely incurable form of cancer. #neurobiology #oncology #cancer #medicine


Neurons form glutamatergic synapses with glioma cells in mice and humans, and inhibition of AMPA receptors reduces glioma cell invasion and growth.

The clock’s accuracy would be as high as one second for every 300 billion years.

A collaboration between researchers from various institutes in Germany has brought us a step closer to building the first-ever nuclear clock. In experiments carried out at the European Organization for Nuclear Research (CERN), the researchers measure the radiative decay of thorium-229 nuclear isomer, the first instance of having achieved this feat and a critical component for building nuclear clocks.

For years atomic clocks have been our standard of accuracy when it comes to clocks.


Blackjack3D/iStock.

UC San Diego engineers developed the low-cost clip that enables easy and affordable monitoring in resource-poor communities.

University of California San Diego engineers have created a low-cost clip that makes use of the camera and flash on a smartphone to measure blood pressure at the user’s fingertip.

This innovative clip, which can be produced at scale for as little as 10 cents, has the potential to revolutionize routine blood pressure monitoring and make it available to people in resource-poor regions.

Computer vision specialist Landing AI has a unique calling card: Its co-founder and CEO is a tech rock star.

At Google Brain, Andrew Ng became famous for showing how deep learning could recognize cats in a sea of images with uncanny speed and accuracy. Later, he founded Coursera, where his machine learning courses have attracted nearly 5 million students.

Today, Ng is best known for his views on data-centric AI — that improving AI performance now requires more focus on datasets and less on refining neural network models. It’s a philosophy coded into Landing AI’s flagship product, LandingLens.