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Star light, star bright as explained by math

$2 5 million grant will support algebra teachers professional development

 E-Mail Credit: MU College of Education COLUMBIA, Mo. In her 16 years teaching algebra, Amy Dwiggins has seen countless students struggle with the seemingly abstract subject. But the creation of new, online professional development materials at the University of Missouri could help teachers like Dwiggins, who teaches Algebra 2 and Geometry at Macon High School in Macon, Missouri, teach their students more effectively. Algebra can be a tough subject to learn if the students don t find it applicable to their daily lives, Dwiggins said. But we use critical thinking and problem-solving skills all the time, and math is a big part of that. So we, as teachers, need to find creative ways to spark students interests in a way that makes them more motivated to learn.

Brain-on-a-chip would need little training

 E-Mail IMAGE: Advances in artificial intelligence technology is leading to the development of neural networks that mimic the biology of the brain. view more  Credit: © 2021 KAUST A biomimicking spiking neural network on a microchip has enabled KAUST researchers to lay the foundation for developing more efficient hardware-based artificial intelligence computing systems. Artificial intelligence technology is developing rapidly, with an explosion of new applications across advanced automation, data mining and interpretation, healthcare and marketing, to name a few. Such systems are based on a mathematical artificial neural network (ANN) composed of layers of decision-making nodes. Labeled data is first fed into the system to train the model to respond a certain way, then the decision-making rules are locked in and the model is put into service on standard computing hardware.

Are our oil and gas pipelines safe during an earthquake?

 E-Mail IMAGE: The new research shows that current methods used for calculating stress received by the underground pipelines during an earthquake are incorrect. view more  Credit: Peter the Great St.Petersburg Polytechnic University Underground pipelines that transport oil and gas are very important engineering communications worldwide. Some of these underground communications are built and operated in earthquake-prone areas. Seismic safety or seismic stability of underground pipelines began to be intensively studied since the 1950s. Since then, a number of methodologies were proposed for calculating stress received by an underground pipeline during an earthquake. The purpose of these methodologies was to make an accurate prediction on the structural stress received by a pipeline during an earthquake, and thus it would allow to decide how resilient the pipeline must be made in the first place. It is important to find a right balance between pipeline cost and its stru

Machine learning at speed

 E-Mail IMAGE: Technology developed through a KAUST-led collaboration with Intel, Microsoft and the University of Washington can dramatically increase the speed of machine learning on parallelized computing systems. view more  Credit: © 2021 KAUST; Anastasia Serin. Inserting lightweight optimization code in high-speed network devices has enabled a KAUST-led collaboration to increase the speed of machine learning on parallelized computing systems five-fold. This in-network aggregation technology, developed with researchers and systems architects at Intel, Microsoft and the University of Washington, can provide dramatic speed improvements using readily available programmable network hardware. The fundamental benefit of artificial intelligence (AI) that gives it so much power to understand and interact with the world is the machine-learning step, in which the model is trained using large sets of labeled training data. The more data the AI is trained on, the better t

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