Abstract Code clones are similar code fragments at the syntactic or semantic level, commonly seen in software development. Excessive cloning harms maintainability and may introduce persistent bugs. We analyze cross-language code clone detection at the accurate semantic level. Most existing clone detection approaches target single-language environments and focus mainly on syntactic similarity. However, complex software systems are often developed using multiple programming languages, resulting in semantically similar cross-language code clones. These clones pose challenges beyond the capabilities of current detection tools. In this paper, we propose a novel flow-enhanced graph attention network approach, called FEGAT, to effectively detect cross-language code clones at the semantic level. First, we design a flow-enhanced code graph using abstract syntax tree along with the added control and data flow edges. Then, we input this code graph into the pre-trained model CodeBERT to learn the initial flow-enhanced node representation with semantic information. Third, we design FEGAT to learn flow-enhanced graph representation of cross-language codes from their semantic information and detect clones by computing the similarity score. Finally, we conduct experiments on the AtCoder and CodeChef datasets to evaluate the performance of FEGAT in terms of precision, recall, and F1-score. The experimental results demonstrate that FEGAT outperforms existing cross-language code clone detection tools.

Precise distance measurement using wireless signals is essential for applications such as structural health monitoring, autonomous navigation, and asset tracking. Traditional high-accuracy ranging techniques typically rely on large signal bandwidths, often several hundred megahertz, to resolve carrier-phase ambiguities and achieve centimeter-level precision. However, this results in increased system complexity and high spectral resource consumption. To address these challenges, this paper proposes an optimized dual-differential carrier phase measurement method that achieves centimeter-level accuracy using only a few of discrete frequency bands, each occupying a bandwidth of approximately 20 MHz. Carrier-phase measurements at the selected frequencies are first obtained and wrapped within the (−π,+π) range. Dual-differential operations are then applied between adjacent positions to eliminate systematic phase biases and improve estimation robustness. A weighted least-squares estimator provides initial float ambiguity estimates, which are subsequently resolved using the Modified Least-squares Ambiguity decorrelation adjustment (MLAMBDA) algorithm. Compared with conventional ranging methods, the proposed approach significantly reduces bandwidth requirements while maintaining sub-5 cm relative positioning accuracy. It is particularly well-suited for deployment in bandwidth-constrained Integrated Sensing and Communication (ISAC) applications.

Characterizing the relations among the three bipartite reduced density operators$ \rho _{AB}$,$ \rho _{AC}$and$ \rho _{BC}$of a tripartite mixed state$ \rho _{ABC}$has been an open problem in quantum information. One of such relations has been reduced by [Cadney et al, LAA. 452, 153, 2014] to a conjectured inequality in terms of matrix rank, namely$r(\rho _{AB}) \cdot r( \rho _{AC})\ge r( \rho _{BC})$for any$ \rho _{ABC}$. It is denoted as open problem 41 in the website “Open quantum problems-IQOQI Vienna”. We prove the inequality, and thus establish a complete picture of the four-party linear inequalities in terms of the 0-entropy. Our proof is based on the construction of a novel canonical form of bipartite matrices under local equivalence. We extend our result to inequalities in multipartite systems, as well as the condition when the inequality is saturated.