In paid collaboration with reMarkable, I am happy to recommend this paper tablet which has made my workflow much more efficient and enjoyable.
Science and Technology: I don’t want to die.
A friend of the academic, who died in the US in 2021, says his brain has been cryonically preserved in accordance with his final wishes.
For years, niobium was considered an underperformer when it came to superconducting qubits. Now, scientists supported by Q-NEXT have found a way to engineer a high-performing niobium-based qubit and take advantage of niobium’s superior qualities.
When it comes to quantum technology, niobium is making a comeback.
For the past 15 years, niobium has been sitting on the bench after experiencing a few mediocre at-bats as a core qubit material.
In this first article in a series on philosophy and science, we take a look at materialism and why it is fundamental to science.
A short disclaimer before we read further: I’m a materialist. Materialism is a branch of philosophy to which the sciences, particularly the physical and life sciences, owe a lot. Materialism posits that the material world — matter — exists, and everything in the Universe, including consciousness, is made from or is a product of matter. An objective reality exists and we can understand it. Without materialism, physics, chemistry, and biology as we know it wouldn’t exist.
Another branch of philosophy, idealism, is in direct contradiction to materialism. Idealism states that, instead of matter, the mind and consciousness are fundamental to reality; that they are immaterial and therefore independent of the material world.
Speaker’s Bio: Catherine (Katie) Schuman is a research scientist at Oak Ridge National Laboratory (ORNL). She received her Ph.D. in Computer Science from the University of Tennessee (UT) in 2015, where she completed her dissertation on the use of evolutionary algorithms to train spiking neural networks for neuromorphic systems. She is continuing her study of algorithms for neuromorphic computing at ORNL. Katie has an adjunct faculty appointment with the Department of Electrical Engineering and Computer Science at UT, where she co-leads the TENNLab neuromorphic computing research group. Katie received the U.S. Department of Energy Early Career Award in 2019.
Talk Abstract: Neuromorphic computing is a popular technology for the future of computing. Much of the focus in neuromorphic computing research and development has focused on new architectures, devices, and materials, rather than in the software, algorithms, and applications of these systems. In this talk, I will overview the field of neuromorphic from the computer science perspective. I will give an introduction to spiking neural networks, as well as some of the most common algorithms used in the field. Finally, I will discuss the potential for using neuromorphic systems in real-world applications from scientific data analysis to autonomous vehicles.
Researchers await the outcome of an ongoing investigation into dozens of instances of alleged image problems spanning 29 publications over a period of 23 years.
In creating five new isotopes, an international research team working at the Facility for Rare Isotope Beams (FRIB) at Michigan State University has brought the stars closer to Earth.
The isotopes —known as thulium-182, thulium-183, ytterbium-186, ytterbium-187 and lutetium-190—are reported in the journal Physical Review Letters.
These represent the first batch of new isotopes made at FRIB, a user facility for the U.S. Department of Energy Office of Science, or DOE-SC, supporting the mission of the DOE-SC Office of Nuclear Physics. The new isotopes show that FRIB is nearing the creation of nuclear specimens that currently only exist when ultradense celestial bodies known as neutron stars crash into each other.
In a new study, scientists have investigated the newly discovered class of altermagnetic materials for their thermal properties, offering insights into the distinctive nature of altermagnets for spin-caloritronic applications.
Magnetism is an old and well-researched topic, lending itself to many applications, like motors and transformers. However, new magnetic materials and phenomena are being studied and discovered, one of which is altermagnets.
Altermagnets exhibit a unique blend of magnetic characteristics, setting them apart from conventional magnetic materials like ferromagnets and antiferromagnets. These materials exhibit properties observed in both ferromagnets and antiferromagnets, making their study enticing.
One of the country’s best-known science museums, San Francisco’s Exploratorium, is located less than three miles north of Gladstone Institutes—proximity that has resulted in creative, high-science collaborations like the permanent exhibit featured in the latest issue of Stem Cell Reports.
Among the museum’s most popular exhibits, “Give Heart Cells A Beat” opens a rare window into the microscopic world of the beating human heart, using technology and materials made possible through Gladstone’s science and expertise. With the exhibit, the team created the first interactive museum experience that allows the public to interact directly with living cardiomyocytes.
We are in the middle of a data-driven science boom. Huge, complex data sets, often with large numbers of individually measured and annotated ‘features’, are fodder for voracious artificial intelligence (AI) and machine-learning systems, with details of new applications being published almost daily.
But publication in itself is not synonymous with factuality. Just because a paper, method or data set is published does not mean that it is correct and free from mistakes. Without checking for accuracy and validity before using these resources, scientists will surely encounter errors. In fact, they already have.
In the past few months, members of our bioinformatics and systems-biology laboratory have reviewed state-of-the-art machine-learning methods for predicting the metabolic pathways that metabolites belong to, on the basis of the molecules’ chemical structures1. We wanted to find, implement and potentially improve the best methods for identifying how metabolic pathways are perturbed under different conditions: for instance, in diseased versus normal tissues.
