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On a lattice with 2+1-flavor dynamical domain-wall fermions at the physical pion mass, we calculate the decay constants of,, and. The lattice size is, which corresponds to a spatial extension offm, with a lattice spacing offm. For the valence light, strange, and charm quarks, we use overlap fermions at several mass points close to their physical values. Our results at the physical point areMeV,MeV,MeV,MeV, andMeV. The couplings ofandto the tensor current () can be derived from ratiosand, respectively, which are the first lattice quantum chromodynamics (QCD) results. We also obtain ratiosand, which reflect the size of heavy quark symmetry breaking in charmed mesons. Ratiosandcan be taken as a measure ofSU(3) flavor symmetry breaking.

 

A plant can be thought of as a colony comprising numerous growth buds, each developing to its own rhythm. Such lack of synchrony impedes efforts to describe core principles of plant morphogenesis, dissect the underlying mechanisms, and identify regulators. Here, we use the minimalist known angiosperm to overcome this challenge and provide a model system for plant morphogenesis. We present a detailed morphological description of the monocot Wolffia australiana, as well as high-quality genome information. Further, we developed the plant-on-chip culture system and demonstrate the application of advanced technologies such as single-nucleus RNA-sequencing, protein structure prediction, and gene editing. We provide proof-of-concept examples that illustrate how W. australiana can decipher the core regulatory mechanisms of plant morphogenesis.

 

In the Software Defined Networking (SDN) and Network Function Virtualization (NFV) era, it is critical to enable dynamic network access control. Traditionally, network access control policies are statically predefined as router entries or firewall rules. SDN enables more flexibility by re-actively installing flow rules into the switches to achieve dynamic network access control. However, SDN is limited in capturing network anomalies, which are usually important signs of security threats. In this paper, we propose to employ anomaly-based Intrusion Detection System (IDS) to capture network anomalies and generate SDN flow rules to enable dynamic network access control. We gain the knowledge of network anomalies from anomaly-based IDS by training an interpretable model to explain its outcome. Based on the explanation, we derive access control policies. We demonstrate the feasibility of our approach by explaining the outcome of an anomaly-based IDS built upon a Recurrent Neural Network (RNN) and generating SDN flow rules based on our explanation.