Prediction of new Ti-N phases using machine learned interatomic potential

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3.最近一年销售收入在2亿元以上的企业,比例不低于3%。

To find these crucial border points, we employed a clever technique based on the Ford-Fulkerson algorithm. By simulating "flooding" roads with traffic from random start/end points, we could identify the natural bottlenecks – the "minimum cut" in graph theory terms. These bottlenecks became our border points.

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The second approach offers broader feature support, seen in projects like Cloud Hypervisor or QEMU microvm. Built for heavier and more dynamic workloads, it supports hot-plugging memory and CPUs, which is useful for dynamic build runners that need to scale up during compilation. It also supports GPU passthrough, which is essential for AI workloads, while still maintaining the fast boot times of a microVM.