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Text-to-image AI systems are going to be huge.


Google has released its latest text-to-image AI system, named Imagen, and the results are extremely impressive. However, the company warns the system is also prone to racial and gender biases, and isn’t releasing Imagen publicly.

The semiconductor industry has been growing steadily ever since its first steps in the mid-twentieth century and, thanks to the high-speed information and communication technologies it enabled, it has given way to the rapid digitalization of society. Today, in line with a tight global energy demand, there is a growing need for faster, more integrated, and more energy-efficient semiconductor devices.

However, modern semiconductor processes have already reached the nanometer scale, and the design of novel high-performance materials now involves the structural analysis of semiconductor nanofilms. Reflection high-energy electron diffraction (RHEED) is a widely used analytical method for this purpose. RHEED can be used to determine the structures that form on the surface of thin films at the atomic level and can even capture structural changes in real time as the thin film is being synthesized!

Unfortunately, for all its benefits, RHEED is sometimes hindered by the fact that its output patterns are complex and difficult to interpret. In virtually all cases, a highly skilled experimenter is needed to make sense of the huge amounts of data that RHEED can produce in the form of diffraction patterns. But what if we could make machine learning do most of the work when processing RHEED data?

Though the planets are outside of their star’s habitable zone, as they orbit too closely, “there might be more planets in the system,” according to Avi Shporer, one of the scientists involved in the new study. “There are many multiplanet systems hosting five or six planets, especially around small stars like this one. Hopefully, we will find more, and one might be in the habitable zone. That’s optimistic thinking.”

Either way, the multiplanet system will likely be a focal point for future studies, shedding new insight into planetary formation and the evolution of alien worlds, helping the astronomical community better understand how our own planet came into existence.

Neutron stars are incredibly dense remnants of stars that collapsed and exploded as supernova, making them spin at immense speeds. By definition, they have lived for millennia, died, and then been reborn as something new.

In any case, a press statement reveals that astronomers discovered one of the youngest known neutron stars while analyzing data from the VLA Sky Survey (VLASS).

Using a newly developed technique, scientists at the Max Planck Institute for Nuclear Physics (MPIK) in Heidelberg have measured the very small difference in the magnetic properties of two isotopes of highly charged neon in an ion trap with previously inaccessible accuracy. Comparison with equally extremely precise theoretical calculations of this difference allows a record-level test of quantum electrodynamics (QED). The agreement of the results is an impressive confirmation of the standard model of physics, allowing conclusions regarding the properties of nuclei and setting limits for new physics and dark matter.

Electrons are some of the most fundamental building blocks of the matter we know. They are characterized by some very distinctive properties, such as their negative charge and the existence of a very specific intrinsic angular momentum, also called spin. As a charged particle with spin, each electron has a magnetic moment that aligns itself in a magnetic field similar to a compass needle. The strength of this magnetic moment, given by the so-called g-factor, can be predicted with extraordinary accuracy by quantum electrodynamics. This calculation agrees with the experimentally measured g-factor to within 12 digits, one of the most precise matches of theory and experiment in physics to date. However, the magnetic moment of the electron changes as soon as it is no longer a “free” particle, i.e., unaffected by other influences, but instead is bound to an atomic nucleus, for example.

(https://hscrb.harvard.edu/labs/whited-lab/) is an Assistant Professor of Stem Cell and Regenerative Biology at Harvard University where her lab focuses on limb regeneration in axolotl salamanders and where they develop tools to manipulate gene expression during limb regeneration, and explore signaling events following wound healing that initiate the regenerative process.

Dr. Whited earned a B.A. in Philosophy and a B.S. in Biological Sciences from the University of Missouri, and obtained her Ph.D. in Biology from MIT, where she studied in Dr. Paul Garrity’s laboratory.

Dr. Whited’s thesis focused on molecular mechanisms controlling the development and maintenance of cellular architectures in the Drosophila nervous system. During this work, Dr. Whited became interested in processes that may be required long after initial developmental events to ensure cells do not revert to immature behaviors, as well as processes that provoke such events in response to injury. She worked in the laboratory of Dr. Cliff Tabin (Harvard Medical School Department of Genetics) as a postdoc studying total limb regeneration in axolotl salamanders. During this time, Whited developed several molecular tools that can be used to interrogate regenerating axolotl limbs, which is one of the core focuses of her lab today.

Dr. Whited is also Co-Founder of Matice Biosciences, a company leveraging regenerative biology for the next generation of skincare and consumer scar products.