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Category: robotics/AI – Page 181

Self-learning neural network cracks iconic black holes

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the Event Horizon Telescope, they now predict, among other things, that the black hole at the center of our Milky Way is spinning at near top speed.

The astronomers have published their results and methodology in three papers in the journal Astronomy & Astrophysics.

In 2019, the Event Horizon Telescope Collaboration released the first image of a supermassive black hole at the center of the galaxy M87. In 2022, they presented an image of the black hole in our Milky Way, Sagittarius A*. However, the data behind the images still contained a wealth of hard-to-crack information. An international team of researchers trained a neural network to extract as much information as possible from the data.

AI-designed waveguides pave the way for next-generation photonic devices

A team of researchers at the University of California, Los Angeles (UCLA) has introduced a novel framework for designing and creating universal diffractive waveguides that can control the flow of light in highly specific and complex ways.

This new technology uses (AI), specifically deep learning, to design a series of structured surfaces that guide light with high efficiency and can perform a wide range of functions that are challenging for conventional waveguides.

The work is published in the journal Nature Communications.

Quantum machine learning: Small-scale photonic quantum processor can already outperform classical counterparts

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing.

An experimental study shows that already small-scale quantum computers can boost the performance of algorithms.

This was demonstrated on a photonic quantum processor by an international team of researchers at the University of Vienna. The work, published in Nature Photonics, shows promising for optical quantum computers.

Tumor diagnostics: AI model detects more than 170 types of cancer

The MRI shows a brain tumor in an inauspicious location, and a brain biopsy will entail high risks for a patient who had consulted doctors due to double vision. Situations such as this case prompted researchers at Charité—Universitätsmedizin Berlin to look for new diagnostic procedures. The result is an AI model.

The model makes use of specific characteristics in the genetic material of tumors—their epigenetic fingerprint, obtained for example from , among other things. As the team shows in the journal Nature Cancer, the new model classifies tumors quickly and very reliably.

Today, far more types of tumors are known than the organs from which they arise. Each tumor has its own characteristics: certain tissue features, growth rates and metabolic peculiarities. Nevertheless, tumor types with similar molecular characteristics can be grouped together. The treatment of the individual disease depends decisively on the type of tumor.

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