DHM-Net: Deep Hypergraph Modeling for Robust Feature Matching.

We present a novel deep hypergraph modeling architecture (called DHM-Net) for feature matching in this paper. Our network focuses on learning reliable correspondences between two sets of initial feature points by establishing a dynamic hypergraph structure that models group-wise relationships and assigns weights to each node. Compared to existing feature matching methods that only consider pair-wise relationships via a simple graph, our dynamic hypergraph is capable of modeling nonlinear higher-order group-wise relationships among correspondences in an interaction capturing and attention representation learning fashion.

Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond.

Climate change is one of several drivers of recurrent outbreaks and geographical range expansion of infectious diseases in Europe. We propose a framework for the co-production of policy-relevant indicators and decision-support tools that track past, present, and future climate-induced disease risks across hazard, exposure, and vulnerability domains at the animal, human, and environmental interface. This entails the co-development of early warning and response systems and tools to assess the costs and benefits of climate change adaptation and mitigation measures across sectors, to increase health system resilience at regional and local levels and reveal novel policy entry points and opportunities.

Surface Texturing-Plasma Nitriding Duplex Treatment for Improving Tribological Performance of AISI 316 Stainless Steel.

Surface texturing-plasma nitriding duplex treatment was conducted on AISI 316 stainless steel to improve its tribological performance. Tribological behaviors of ground 316 substrates, plasma-nitrided 316 (PN-316), surface-textured 316 (ST-316), and duplex-treated 316 (DT-316) in air and under grease lubrication were investigated using a pin-on-disc rotary tribometer against counterparts of high carbon chromium bearing steel GCr15 and silicon nitride Si₃N₄ balls. The variations in friction coefficient, mass loss, and worn trace morphology of the tested samples were systemically investigated and analyzed.

Cool tadpoles from Arctic environments waste fewer nutrients - high gross growth efficiencies lead to low consumer-mediated nutrient recycling in the North.

Endothermic organisms can adapt to short growing seasons, low temperatures and nutrient limitation by developing high growth rates and high gross growth efficiencies (GGEs). Animals with high GGEs are better at assimilating limiting nutrients and thus should recycle (or lose) fewer nutrients. Longer guts in relation to body mass may facilitate higher GGE under resource limitation.

Hot tadpoles from cold environments need more nutrients--life history and stoichiometry reflects latitudinal adaptation.

1. High-latitude species (and populations within species) are adapted to short and cold summers. They often have high growth and development rates to fully use the short growing season and mature before the onset of winter.