Scienceblogtwo

Without losing accuracy, researchers trained a machine learning tool to model the physics of electrons traveling on a lattice with much fewer equations than would ordinarily be needed. A difficult quantum problem that formerly required 100,000 equations has been condensed by physicists employing artificial intelligence into a manageable assignment requiring as few as four equations. Accuracy was maintained throughout this entire process. The research may completely alter how scientists examine systems with plenty of interacting electrons. The method may also help in the design of materials with exceptionally valued features like superconductivity or usefulness for the production of clean energy if it is transferable to other issues. According to research main author Domenico Di Sante, "we start with this gigantic object with all these coupled differential equations and then we use machine learning to transform it into something so small you can count it on your fingers." He i...

The long-term implications of declining land mammals on food webs are now best understood according to a new study that was published in the journal Science. It's a hideous sight. "We estimate that more than 50% of mammal food web links have perished in that time, but only approximately 6% of terrestrial mammals have gone extinct," said ecologist Evan Fricke, the study's primary author. "And the complexity of the mammal food web depends on the mammals that are most likely to decline, both historically and currently." All the relationships between predators and their prey in a specific area are known as a food web. Complex food webs are necessary for population control in a way that permits more species to coexist, hence enhancing ecosystem diversity and stability. However, animal extinctions might lessen this complexity, which would make an ecosystem less resilient. Illustration showing every mammal species that, if not for range contractions and extinctions...

With the help of machine learning, scientists have been able to better understand how the brain creates complex human traits by identifying patterns of brain activity linked to traits like impulsivity, attributes like working memory, and diseases like depression. These techniques allow scientists to create models of these linkages, which can then be applied to forecast human behavior and health. It only functions, though, if models are inclusive, which prior research has demonstrated is not the case. There are those people that simply do not fit any model. In a study that was just published in the journal Nature, researchers from Yale University examined who these models tend to fail in, why that happens, and what can be done to correct it. The study's principal investigator, an M.D.-Ph.D. Abigail Greene, a Yale School of Medicine student, says that in order to be most useful, models must be applicable to any particular person. She explained that the model must be applicable to the...