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IMAGE: a) Through computer machine learning training, the optical MLD acquires the capability of identifying decryption keys and decoding a multitude of messages using a single decryptor element. b) The decryption. view more
Credit: by Elena Goi, Xi Chen, Qiming Zhang, Benjamin P. Cumming, Steffen Schoenhardt?Haitao Luan and Min Gu
Today, machine learning based methods are of our everyday life, with millions of users every day unlocking their phones through facial recognition or passing through AI-enabled automated security checks at airports and train stations. Traditionally, the processing of information native to the optical domain is being executed in the electronic domain, requiring energy-hungry specialized electronic hardware and conversion between the two realms. Optical machine learning is emerging as an important field, where the processing of optical information is done directly within the optical domain, power-efficient and at the speed of light.
Credit: NJIT
There is no sustainable cure at present for osteoarthritis, the most common chronic musculoskeletal disorder of the joints. And while joint replacements are successful treatments for older patients with already reduced mobility, they hold less promise for younger patients, with failure in the long-term nearly guaranteed. Biomaterial engineers propose another solution: restoring the damaged tissue itself. The gap between supply and demand for transplantable tissues and organs is continuously increasing, says Murat Guvendiren, an assistant professor of chemical and materials engineering who is developing biomaterials designed to enable the production of fully functional, human-scale tissues and organs that are capable of replacing failed organs. Known as bioinks - hydrogels seeded with live human cells that are 3D-printed in the lab - these materials could potentially be used to construct highly complex and patient-specific tissues and organs, as well as tissue interfa
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IMAGE: The researchers customized this adaptive optics scanning light ophthalmoscope to improve the imaging resolution by strategically blocking light in various locations of the instrument. Using less light is an advantage. view more
Credit: Johnny Tam, National Eye Institute
WASHINGTON Researchers have developed a noninvasive technique that can capture images of rod and cone photoreceptors with unprecedented detail. The advance could lead to new treatments and earlier detection for retinal diseases such as macular degeneration, a leading cause of vision loss. We are hopeful that this technique will better reveal subtle changes in the size, shape and distribution of rod and cone photoreceptors in diseases that affect the retina, said research team leader Johnny Tam from the National Eye Institute. Figuring out what happens to these cells before they are lost is an important step toward developing earlier interventions to treat and prevent blindness.
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VIDEO: A 3D radiation magneto-hydrodynamic FLASH simulation of the experimental platform. The video shows a rendering of the magnetic field as a function of time, with grids and cylindrical shields shown. view more
Credit: University of Rochester/Laboratory for Laser Energetics
The universe is filled with magnetic fields. Understanding how magnetic fields are generated and amplified in plasmas is essential to studying how large structures in the universe were formed and how energy is divided throughout the cosmos.
An international collaboration, co-led by researchers at the University of Rochester, the University of Oxford, and the University of Chicago, conducted experiments that captured for the first time in a laboratory setting the time history of the growth of magnetic fields by the turbulent dynamo, a physical mechanism thought to be responsible for generating and sustaining astrophysical magnetic fields. The experiments accessed conditions relevant
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Robots solving computer games, recognizing human voices, or helping in finding optimal medical treatments: those are only a few astonishing examples of what the field of artificial intelligence has produced in the past years. The ongoing race for better machines has led to the question of how and with what means improvements can be achieved. In parallel, huge recent progress in quantum technologies have confirmed the power of quantum physics, not only for its often peculiar and puzzling theories, but also for real-life applications. Hence, the idea of merging the two fields: on one hand, artificial intelligence with its autonomous machines; on the other hand, quantum physics with its powerful algorithms.