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Have you ever wondered how the Sun creates the energy that we get from it every day and how the other elements besides hydrogen have formed in our universe? Perhaps you know that this is due to fusion reactions where four nuclei of hydrogen join together to produce a helium nucleus. Such nucleosynthesis processes are possible solely due to the existence, in the first place, of stable deuterons, which are made up of a proton and a neutron.

Probing deeper, one finds that a deuteron consists of six light quarks. Interestingly, the strong interaction between quarks, which brings stability to deuterons, also allows for various other six-quark combinations, leading to the possible formation of many other deuteron-like nuclei. However, no such nuclei, though theoretically speculated about and searched for experimentally many times, have yet been observed.

All this may get changed with an exciting new finding, where, using a state-of-the-art first-principles calculation of lattice quantum chromodynamics (QCD), the basic theory of strong interactions, a definite prediction of the existence of other deuteron-like nuclei has been made by TIFR’s physicists. Using the computational facility of the Indian Lattice Gauge Theory Initiative (ILGTI), Prof. Nilmani Mathur and postdoctoral fellow Parikshit Junnarkar in the Department of Theoretical Physics have predicted a set of exotic nuclei, which are not to be found in the Periodic Table. The masses of these new exotic nuclei have also been calculated precisely.

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Neuralink seeks to build a brain-machine interface that would connect human brains with computers. No tests have been performed in humans, but the company hopes to obtain FDA approval and begin human trials in 2020. Musk said the technology essentially provides humans the option of “merging with AI.”

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Excellent lecture. Darwin’s turtle, sharks and clams 500 years old, talking about Liz Parrish at an hour and 8. And then a tour.

My mission is to drastically improve your life by helping you break bad habits, build and keep new healthy habits to make you the best version of yourself. I read the books and do all the research and share my findings with you!

This video is DR. BILL ANDREWS PRESENTATION & TOUR OF SIERRA SCIENCES ON OCTOBER 11TH, 2019. Brent Nally recorded, edited and produced this video. My apologies for the poor audio and camera work in the first few minutes. Infinite gratitude to Bill for opening up Sierra Sciences to us. Here’s a link to purchase IsaGenesis. You have to sign up first: https://getstarted.isagenix.com/VF234XXQV001

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These days, it seems like every brand is trying to leverage machine learning to imbue their products with special powers — and, most importantly, make an extra buck in the process.

But does your next electric toothbrush really need a dose of AI? Oral-B’s says its new $220 electric toothbrush, called “Oral-B GENIUS X with Artificial Intelligence,” will leverage data from sensors inside the brush head and Bluetooth to deliver AI-derived brushing tips through an app. The future is now, huh?

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Physicists can explore tailored physical systems to rapidly solve challenging computational tasks by developing spin simulators, combinatorial optimization and focusing light through scattering media. In a new report on Science Advances, C. Tradonsky and a group of researchers in the Departments of Physics in Israel and India addressed the phase retrieval problem by reconstructing an object from its scattered intensity distribution. The experimental process addressed an existing problem in disciplines ranging from X-ray imaging to astrophysics that lack techniques to reconstruct an object of interest, where scientists typically use indirect iterative algorithms that are inherently slow.

In the new optical approach, Tradonsky et al conversely used a digital degenerate cavity laser (DDCL) mode to rapidly and efficiently reconstruct the object of interest. The experimental results suggested that the gain competition between the many lasing modes acted as a highly parallel computer to rapidly dissolve the phase retrieval problem. The approach applies to two-dimensional (2-D) objects with known compact support and complex-valued objects, to generalize imaging through scattering media, while accomplishing other challenging computational tasks.

To calculate the intensity distribution of light scattered far from an unknown object relatively easily, researchers can compute the source of the absolute value of an object’s Fourier transform. The reconstruction of an object from its scattered intensity distribution is, however, ill-posed, since phase information can be lost and diverse phase distributions in the work can result in different reconstructions. Scientists must therefore obtain prior information about an object’s shape, positivity, spatial symmetry or sparsity for more precise object reconstructions. Such examples are found in astronomy, short-pulse characterization studies, X-ray diffraction, radar detection, speech recognition and when imaging across turbid media. During the reconstruction of objects with a finite extent (compact support), researchers offer a unique solution to the phase retrieval problem, as long as they model the same scattered intensity at a sufficiently higher resolution.

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