Measuring robustness of community structure in complex networks

The theory of community structure is a powerful tool for real networks, which can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the robustness of community structure is an urgent and important task. In this letter, we employ the critical threshold of resolution parameter in Hamiltonian function, $\gamma_C$, to measure the robustness of a network. According to spectral theory, a rigorous proof shows that the index we proposed is inversely proportional to robustness of community structure. Furthermore, by utilizing the co-evolution model, we provides a new efficient method for computing the value of $\gamma_C$. The research can be applied to broad clustering problems in network analysis and data mining due to its solid mathematical basis and experimental effects.Comment: 6 pages, 4 figures. arXiv admin note: text overlap with arXiv:1303.7434 by other author

Threshold Resummation Effects in Neutral Higgs Boson Production by Bottom Quark Fusion at the CERN Large Hadron Collider

We investigate the QCD effects in the production of neutral Higgs bosons via bottom quark fusion in both the standard model and the minimal supersymmetric standard model at the CERN Large Hadron Collider. We include the next-to-leading order (NLO) QCD corrections (including supersymmetric QCD) and the threshold resummation effects. We use the soft-collinear effective theory to resum the large logarithms near threshold from soft gluon emission. Our results show that the resummation effects can enhance the total cross sections by about 5% compared with the NLO results.Comment: 29pages, 14 figures, version to appear in Physical Review

Analysis of Chlorogenic Acid Oxidation Pathway in Simulated Enzymatic Honeysuckle Browning System by High Performance Liquid Chromatography and Mass Spectrometry

Purpose: To investigate the pathways involved in the oxidation of chlorogenic acid (CA) and phenol metabolism in honeysuckle buds.Methods: A model that mimics CA oxidation by honeysuckle polyphenol oxidase (PPO) by controlling the reaction temperature or reaction duration was employed, and the resulting products were analyzed by high-performance liquid chromatography followed by mass spectrometry (LC-MS).Results: The pathway of CA oxidation by PPO in honeysuckle involves CA, hydroxyl - CA, CA-oquinone derivatives, and CA-p-quinone derivatives.Conclusion: CA oxidation and the enzymatic browning reaction significantly impact the quality of honeysuckle and other fresh agricultural products.Keywords: Honeysuckle, Chlorogenic acid, Enzymatic browning, Mimic system, Oxidation pathway, High-performance liquid chromatography-mass spectrometr

SkCoder: A Sketch-based Approach for Automatic Code Generation

Recently, deep learning techniques have shown great success in automatic code generation. Inspired by the code reuse, some researchers propose copy-based approaches that can copy the content from similar code snippets to obtain better performance. Practically, human developers recognize the content in the similar code that is relevant to their needs, which can be viewed as a code sketch. The sketch is further edited to the desired code. However, existing copy-based approaches ignore the code sketches and tend to repeat the similar code without necessary modifications, which leads to generating wrong results. In this paper, we propose a sketch-based code generation approach named SkCoder to mimic developers' code reuse behavior. Given a natural language requirement, SkCoder retrieves a similar code snippet, extracts relevant parts as a code sketch, and edits the sketch into the desired code. Our motivations are that the extracted sketch provides a well-formed pattern for telling models "how to write". The post-editing further adds requirement-specific details to the sketch and outputs the complete code. We conduct experiments on two public datasets and a new dataset collected by this work. We compare our approach to 20 baselines using 5 widely used metrics. Experimental results show that (1) SkCoder can generate more correct programs, and outperforms the state-of-the-art - CodeT5-base by 30.30%, 35.39%, and 29.62% on three datasets. (2) Our approach is effective to multiple code generation models and improves them by up to 120.1% in Pass@1. (3) We investigate three plausible code sketches and discuss the importance of sketches. (4) We manually evaluate the generated code and prove the superiority of our SkCoder in three aspects.Comment: Accepted by the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023