A recent study of thousands of galaxy clusters may ease a debate about the clumpiness of cosmic matter and reinforce the standard model of cosmology.
By mapping the largest structures in the universe in X-rays, cosmologists have found striking agreement with their standard theoretical model of how the universe evolves.
Computer graphic simulations can represent natural phenomena such as tornados, underwater, vortices, and liquid foams more accurately thanks to an advancement in creating artificial intelligence (AI) neural networks.
Working with a multi-institutional team of researchers, Georgia Tech Assistant Professor Bo Zhu combined computer graphic simulations with machine learning models to create enhanced simulations of known phenomena. The new benchmark could lead to researchers constructing representations of other phenomena that have yet to be simulated.
Zhu co-authored the paper “Fluid Simulation on Neural Flow Maps.” The Association for Computing Machinery’s Special Interest Group in Computer Graphics and Interactive Technology (SIGGRAPH) gave it a best paper award in December at the SIGGRAPH Asia conference in Sydney, Australia.
Microsoft presents ResLoRA
Identity residual mapping in low-rank adaption.
Join the discussion on this paper page.
We are always making strides to unravel the mysteries of our universe. Now, a small observatory nestled in the Andes mountains of northern Chile has provided a snapshot of the cosmos in space. This one is clearer than we imagined.
The U.S. National Science Foundation Cosmology Large Angular Scale Surveyor (CLASS), spearheaded by astrophysicists from Johns Hopkins University, mapped a whopping 75 percent of the sky.
One major area of our lives that uses largely “hidden” AI is transportation. Millions of flights and train trips are coordinated by AI all over the world. These AI systems are meant to optimize schedules to reduce costs and maximize efficiency.
Artificial intelligence can also manage real-time road traffic by analyzing traffic patterns, volume and other factors, and then adjusting traffic lights and signals accordingly. Navigation apps like Google Maps also use AI optimization algorithms to find the best path in their navigation systems.
AI is also present in various everyday items. Robot vacuum cleaners use AI software to process all their sensor inputs and deftly navigate our homes.
Researchers develop a computer from an array of VCSELs with optical feedback.
In our data-driven era, solving complex problems efficiently is crucial. However, traditional computers often struggle with this task when dealing with a large number of interacting variables, leading to inefficiencies such as the von Neumann bottleneck. A new type of collective state computing has emerged to address this issue by mapping these optimization problems onto something called the Ising problem in magnetism.
Understanding the Ising Problem.
Weak laser light confined at the apex of a scanning tunnelling microscope tip can drive the tautomerization of a free-base phthalocyanine with atomic-scale precision. The combination of tip-enhanced photoluminescence spectroscopy and hyperspectral mapping paired with theoretical modelling then unravel an excited-state mediated reaction.
RNA structures are built from cryogenic electron microscopy maps using deep learning and backbone tracing.
“It’s the largest coherent structure that we know of, and it’s really, really close to us,” said study co-author, Dr. Catherine Zucker.
A recent study published in Nature investigates further evidence that a gaseous cloud both looks and behaves like an oscillating ocean wave, giving birth to new stars as it traverses the Milky Way Galaxy, which has since been dubbed the Radcliffe Wave. This study was conducted by an international team of researchers led by the Center for Astrophysics | Harvard & Smithsonian and holds the potential to help astronomers better understand the beautiful and fascinating aspects of our cosmos.
Image obtained from an animation of the Radcliffe Wave with our Sun (yellow dot). (Credit: Ralf Konietzka, Alyssa Goodman, and WorldWide Telescope)
Continue reading “Galactic Symphony: The Oscillating Wonders of the Radcliffe Wave” »
A new study by researchers at UC San Francisco provides new insight into how the brain processes musical melodies. Through precise mapping of the cerebral cortex, the study uncovered that our brains process music by not only discerning pitch and the direction of pitch changes but also by predicting the sequence of upcoming notes, each task managed by distinct sets of neurons. The findings have been published in Science Advances.
Previous research had established that our brains possess specialized mechanisms for processing speech sounds, particularly in recognizing pitch changes that convey meaning and emotion. The researchers hypothesized that a similar, perhaps specialized, set of neurons might exist for music, dedicated to predicting the sequence of notes in a melody, akin to how certain neurons predict speech sounds.
“Music is both uniquely human and universally human. Studying the neuroscience of music can therefore reveal something fundamental about what it means to be human,” said lead author Narayan Sankaran, a postdoctoral fellow in the Kavli Center for Ethics, Science, and the Public at UC Berkeley, who conducted the study while a researcher in the lab of UCSF’s Edward Chang.
