Deep-frozen corals, cryopreserved in the hope of restoring ocean ecosystems, are growing up. Could the futuristic technique eventually save dying reefs?
“This work brings us a step closer to realizing the full potential of physical reservoirs to create computers that not only require significantly less energy, but also adapt their computational properties to perform optimally across various tasks, just like our brains,” said Dr. Oscar Lee.
A recent study published in Nature Materials examines a breakthrough approach in physical reservoir computing, also known as a neuromorphic or brain-inspired method and involves using a material’s physical properties to adhere to a myriad of machine learning duties. This study was conducted by an international team of researchers and holds the potential to help physical reservoir computing serve as a framework towards making machine learning more energy efficient.
Artist rendition of connected chiral (twisted) magnets used as a computing avenue for brain-inspired, physical reservoir computing. (Credit: Dr. Oscar Lee)
For the study, the researchers used a magnetic field and temperature variances on chiral (twisted) magnets—which served as the computing channel—they found the materials could be used for a myriad of machine learning needs. What makes this discovery extraordinary is that physical reservoir computing has been found to have limits, specifically pertaining to its ability to be rearranged. Additionally, the team discovered that the chiral magnets performed better at certain computing tasks based on changes in the magnetic field phases used throughout the experiments.
The devices are controlled via voice commands or a smartphone app.
Active noise control technology is used by noise-canceling headphones to minimize or completely block out outside noise. These headphones are popular because they offer a quieter, more immersive listening experience—especially in noisy areas. However, despite the many advancements in the technology, people still don’t have much control over which sounds their headphones block out and which they let pass.
Semantic hearing
Now, deep learning algorithms have been developed by a group of academics at the University of Washington that enable users to select which noises to filter through their headphones in real-time. The system has been named “semantic hearing” by its creators.
The AI tools were complemented by quantum biology and bioengineering approaches.
Philip Gray/ORNL, U.S. Dept. of Energy.
Combining several advances.
The new devices reduce energy use by up to a thousand times.
Sant’Anna.
Now, researchers at Scuola Superiore Sant’Anna are seeking to change this. The results could forever revolutionize how actuators are used and significantly expand the industries where they can be applied.
“Regardless of the size of a city, well planned urban land patterns can reduce population exposures to weather extremes.”
Urban planning and design are crucial for creating resilient cities that can withstand and adapt to the impacts of climate change.
Now, University of Delaware researcher Jing Gao, assistant professor in the College of Earth, Ocean and Environment and a resident faculty member in the Data Science Institute, and colleague Melissa Bukovsky, associate professor in the Haub School of Environment and Natural Resources at the University of Wyoming, are exploring how future populations’ exposure to weather extremes under climatic circumstances present at the end of the twenty-first century will be impacted by changes in urban design.
Research on regulatory T cells is building towards a durable and long-lasting treatment for many solid cancers.
Canadian scientists have established for the first time a new mechanism and role for LDL in the development of type 2 diabetes, beyond its traditional role in the development of cardiovascular disease in humans.
Announced today for World Diabetes Day, the work was carried out by Université de Montréal professor May Faraj, director of the nutrition, lipoproteins and cardiometabolic diseases research unit at the Montreal Clinical Research Institute.
Her study, titled “Native low-density lipoproteins are priming signals of the NLRP3 inflammasome/interleukin1β pathway in human adipose tissue and macrophages,” is published in Scientific Reports.
Absolutely empty—that is how most of us envision the vacuum. Yet, in reality, it is filled with an energetic flickering: the quantum fluctuations.
Experts are currently preparing a laser experiment intended to verify these vacuum fluctuations in a novel way, which could potentially provide clues to new laws in physics. A research team from the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) has developed a series of proposals designed to help conduct the experiment more effectively—thus increasing the chances of success. The team presents its findings in Physical Review D.
The physics world has long been aware that the vacuum is not entirely void but is filled with vacuum fluctuations—an ominous quantum flickering in time and space. Although it cannot be captured directly, its influence can be indirectly observed, for example, through changes in the electromagnetic fields of tiny particles.
In CRISPR-Cas and related nuclease-mediated genome editing, target recognition is based on guide RNAs (gRNAs) that are complementary to selected DNA regions. While single site targeting is fundamental for localized genome editing, targeting to expanded and multiple chromosome elements is desirable for various biological applications such as genome mapping and epigenome editing that make use of different fusion proteins with enzymatically dead Cas9. The current gRNA design tools are not suitable for this task, as these are optimized for defining single gRNAs for unique loci. Here, we introduce CRISPR-broad, a standalone, open-source application that defines gRNAs with multiple but specific targets in large continuous or spread regions of the genome, as defined by the user.