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Based on the action of the mapping class group on the space of measured foliations, we construct a new boundary of the mapping class group and study the structure of this boundary. As an application, for any point in Teichmüller space, we consider the orbit of this point under the action of the mapping class group and describe the closure of this orbit in the Thurston compactification and the Gardiner–Masur compactification of Teichmüller space. We also construct some new points in the Gardiner–Masur boundary of Teichmüller space. [ABSTRACT FROM AUTHOR]

The traditional high-temperature preparation process of polyimide can cause many problems and limits the wider application in extreme conditions. An important challenge to be solved urgently is the reduction of imidization temperature. In this work, twelve kinds of polyimide films with different chain rigidity were prepared at low temperature of 200 °C, in the absence or presence of imidazole used as the catalyst. The molecular rigidity and free volume were theoretically calculated, and relationship between structure and properties were systematically studied. The results show that imidization reaction under low temperatures is significantly affected by the rigidity of molecular chains. The rigid structure of polyimide is not conducive to the low-temperature imidization, but this adverse effect can be eliminated by adding catalyst, resulting the notably increased imidization degree. The optical and thermal properties can be improved to a certain extent for the chemically catalyzed system, resulting in relatively higher heat resistance and thermal stability. While the mechanical performance could be determined by complicating factors, greatly different from polyimide films prepared by high temperature method. To investigate aggregation structures of films, the effect of chain rigidity and catalyst on the stacking or orientation of molecular chains was further elaborated. This work can contribute to the understanding of chemically catalyzed imidization that is rarely reported in the existing research, and will provide guidance for the low-temperature preparation of high-performance polyimides. [ABSTRACT FROM AUTHOR]

Although deep learning models show powerful performance, they are still easily deceived by adversarial samples. Some methods for generating adversarial samples have the drawback of high time loss, which is problematic for adversarial training, and the existing adversarial training methods are difficult to adapt to the dynamic nature of the model, so it is still challenging to study an efficient adversarial training method. In this paper, we propose an adversarial training method, the core of which is the improved adversarial sample generation method AGFAT for adversarial training and the improved dynamic adversarial training method AGFAT-DAT. AGFAT uses a word frequency-based approach to identify significant words, filter replacement candidates, and use an efficient semantic constraint module as a means to reduce the time of adversarial sample generation; AGFAT-DAT is a dynamic adversarial training approach that uses a cyclic attack on the model after adversarial training and generates adversarial samples for adversarial training again. It is demonstrated that the proposed method can significantly reduce the generation time of adversarial samples, and the adversarial-trained model can also effectively defend against other types of word-level adversarial attacks. [ABSTRACT FROM AUTHOR]