Long-range interactions are essential for the correct description of complex systems in many scientific fields. The price to pay for including them in the calculations, however, is a dramatic increase in the overall computational costs. Recently, deep graph networks have been employed as efficient, data-driven surrogate models for predicting properties of complex systems represented as graphs. These models rely on a local and iterative message passing strategy that should, in principle, capture long-range information without explicitly modeling the corresponding interactions. In practice, most deep graph networks cannot really model long-range dependencies due to the intrinsic limitations of (synchronous) message passing, namely oversmoothing, oversquashing, and underreaching. This work proposes a general framework that learns to mitigate these limitations: within a variational inference framework, we endow message passing architectures with the ability to freely adapt their depth and filter messages along the way. With theoretical and empirical arguments, we show that this simple strategy better captures long-range interactions, by surpassing the state of the art on five node and graph prediction datasets suited for this problem. Our approach consistently improves the performances of the baselines tested on these tasks. We complement the exposition with qualitative analyses and ablations to get a deeper understanding of the framework's inner workings.

Dr. Benji Maruyama is the Air Force Research Laboratory team lead for Autonomous Materials and the ... [ ] Autonomous Research System also known as ARES. ARES OS, an open-source software program, is now available online as a free download. In the fight to prevail over America's adversaries by out-innovating them - a fight which has all the hallmarks of a Cold War despite President Biden's assertions to the contrary - increasing the speed at which physical lab experiments can be done and iterated is vital. Air Force Research Laboratory (AFRL) scientist, Dr. Benji Maruyama, is reminding his peers and the public that, "Research is a painfully slow process. Being in a lab and doing experiments takes lots of time."

BOSTON--In July 2018, Curtis Berlinguette, a materials scientist at the University of British Columbia in Vancouver, Canada, realized he was wasting his graduate student's time and talent. He had asked her to refine a key material in solar cells to boost its electrical conductivity. But the number of potential tweaks was overwhelming, from spiking the recipe with traces of metals and other additives to varying the heating and drying times. "There are so many things you can go change, you can quickly go through 10 million [designs] you can test," Berlinguette says. So he and colleagues outsourced the effort to a single-armed robot overseen by an artificial intelligence (AI) algorithm.

The Air Force Research Laboratory is working with engineers at the University of Cincinnati to develop radical new clothing that can charge your cell phone. Researchers are developing a plethora of carbon technology, including'Iron Man' suits that can store power in carbon nanotubes. They say the technology could revolutionise everything from clothing to warplanes. The team say their work could one day lead to'Iron Man' suits that can store power in carbon nanotubes A carbon nanotube is an incredibly small tube-shaped material made of carbon. A nanometer is one-billionth of a meter, or about 10,000 times smaller than a human hair.

Despite the longest growth run in nearly three decades, Japan's economic outlook remains far from robust as uncertainty abounds over wage growth and business investment. Under Abenomics, Prime Minister Shinzo Abe's program of radical monetary easing, fiscal spending and vows of structural reforms, the economy grew at an annualized rate of 0.5 percent in the October-December period, marking the eighth straight quarter of expansion. It slowed from a revised 2.2 percent increase in the previous quarter and was below the potential growth rate of around 1.0 percent. Many economists expect the economy to keep growing at a moderate pace this year, but the biggest wild card could be volatility in financial markets after the recent global stock market rout. The key question now is whether domestic demand -- private consumption and corporate spending -- can pick up further and help the world's third-largest economy sustain its recent growth momentum, economists said.

This paper considers global optimization with a black-box unknown objective function that can be non-convex and non-differentiable. Such a difficult optimization problem arises in many real-world applications, such as parameter tuning in machine learning, engineering design problem, and planning with a complex physics simulator. This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both fast convergence in practice and finite-time error bound in theory. The advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 11 benchmark test functions. Further, we modify the LOGO algorithm to specifically solve a planning problem via policy search with continuous state/action space and long time horizon while maintaining its finite-time error bound. We apply the proposed planning method to accident management of a nuclear power plant. The result of the application study demonstrates the practical utility of our method.