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The article examines the quality and velocity of China's economic growth since it embarked on reform in the 1970s and its entry into the market economy in the 1990s. China has posted an expanded gross domestic product (GDP) which reflects more jobs, higher incomes and better livelihoods. The Chinese economy grew an average 9.9 percent year-on-year between 1979 and 2010.

We define a data exchange format for numerical values of Wilson coefficients of local operators parameterising low-energy effects of physics beyond the Standard Model. The format facilitates interfacing model-specific Wilson coefficient calculators, renormalisation group (RG) runners, and observable calculators. It is designed to be unambiguous (defining a non-redundant set of operators with fixed normalisation in each basis), extensible (allowing the addition of new EFTs or bases by the user), and robust (being based on industry standard file formats with parsers implemented in many programming languages). We have implemented the format for the Standard Model EFT (SMEFT) and for the weak effective theory (WET) below the electroweak scale and have added interfaces to a number of public codes dealing with SMEFT or WET. We also provide command-line utilities and a Python module for convenient manipulation of WCxf files, including translation between different bases and matching from SMEFT to WET. [ABSTRACT FROM AUTHOR]

Owing to the extreme complexity of detecting weak signals submerged in chaotic interference, traditional neural network detection methods based on hypothesis testing are difficult to sufficiently portray the data and chaotic noise evolution characterization for achieving high effective training, and the hypothesis test usually has the shortcomings of poor generalization performance. In this article, a double-layer robust broad logistic regression (DRBLR) is proposed to overcome the aforementioned issues and achieve highly accurate detection. The proposed method reconstructs the observation signal into a high-dimensional phase space with fixed-size tuples, and it extracts both the chaotic evolution trend and nonlinear structure from these spatiotemporal sequences by broad enhancement and manifold embedding operation of the Robust Manifold Broad Learning System. After that, the weights in Logistic Regression with two layers can be trained based on the obtained features and their corresponding pulse signal labels. The strategy of the DRBLR model leads to a robust learning paradigm and obtains better generalization performance. Experiments to detect weak pulse signals are conducted using Lorenz and sunspot datasets. Simulation results illustrate that the proposed method can better capture the characteristics of the chaotic interference evolution and effectively detect the weak pulse signal in the chaotic noise background. [ABSTRACT FROM AUTHOR]