Lifeboat Foundation

The “rank” of a graph is the number of loops it has; for each rank of graphs, there exists a moduli space. The size of this space grows quickly — if you fix the lengths of the graph’s edges, there are three graphs of rank 2, 15 of rank 3,111 of rank 4, and 2,314,204,852 of rank 10. On the moduli space, these lengths can vary, introducing even more complexity.

The shape of the moduli space for graphs of a given rank is determined by relationships between the graphs. As you walk around the space, nearby graphs should be similar, and should morph smoothly into one another. But these relationships are complicated, leaving the moduli space with mathematically unsettling features, such as regions where three walls of the moduli space pass through one another.

Mathematicians can study the structure of a space or shape using objects called cohomology classes, which can help reveal how a space is put together. For instance, consider one of mathematicians’ favorite shapes, the doughnut. On the doughnut, cohomology classes are simply loops.

Fivetran has scaled, in part, by narrowing its original focus.

Supercooled droplets can typically freeze on surfaces in nature, and have broad-scale influence on industries where they can adversely impact technical efficiency and reliability. Superhydrophobic surfaces are therefore a materials engineering solution to rapidly shed water and reduce ice adhesion to form promising candidates that resist icing.

However, the impact of supercooled droplet freezing and their effects on droplet-substrate interactions as well as resultant applications across ice-phobic surfaces remain to be explored in physics and materials engineering.

In a new report in Nature Physics, Henry Lambley and a research team in mechanical and processing engineering at the ETH Zurich, Switzerland, studied frozen supercooled droplets resting on textured surfaces. They induced freezing by evacuating the surrounding atmosphere and determined the surface properties required to promote ice formation.

Scientists have taken a key step toward harnessing a form of artificial intelligence known as deep reinforcement learning, or DRL, to protect computer networks.

When faced with sophisticated cyberattacks in a rigorous simulation setting, deep reinforcement learning was effective at stopping adversaries from reaching their goals up to 95 percent of the time. The outcome offers promise for a role for autonomous AI in proactive cyber defense.

Scientists from the Department of Energy’s Pacific Northwest National Laboratory documented their findings in a research paper and presented their work Feb. 14 at a workshop on AI for Cybersecurity during the annual meeting of the Association for the Advancement of Artificial Intelligence in Washington, D.C.

In a recent study published in the journal Immunity, researchers reviewed the T cell response in cancer and autoimmunity to propose strategies for more effective immunotherapies.

Review: Learning from the nexus of autoimmunity and cancer. Image Credit: Meletios Verras / Shutterstock.

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Sketches found inside Leonardo da Vinci’s sketchbooks, show that he had already grasped the essence of Einstein’s 1907 ‘Equivalence Principle’ centuries before the physicist.