The science and technology innovation policy has a guarantee effect on the improvement of China's science and technology innovation ability. Since the founding of New China, the development of China's science and technology innovation policy has generally experienced key stages such as the initial formation, gradual deepening and continuous consolidation of science and technology innovation ideas. After more than 70 years of development, the practical experience of China's science and technology industry shows that adhering to the overall leadership of the Party is the fundamental guarantee for the development of science and technology, adhering to the self-reliance of science and technology is the strategic support for achieving high-quality development, and adhering to the "people-centered" is all the basis for achieving a strong science and technology country. Facing the future, China's scientific and technological innovation and development should be based on the innovative concept of combining independence and openness, with the new nationwide system to tackle key core technologies in key industries as the starting point, and with the cultivation and introduction of scientific and technological innovation talents as the source, so as to promote the construction of a world scientific and technological power.

As an important concept at present, the essence of new quality productivity is the organic combination of science and industrial system, so that industrial development and scientific development can be strengthened, and also the competitiveness of science and industry of the whole country can be fundamentally promoted. The key to grasping the new quality productivity lies in "new" and "quality". The new represents a new model which is fundamentally different from the traditional old model, while quality represents the characteristics of upgrading the fundamental attributes of the industry through science and technology. The main directions of new quality productive forces are the transformation and upgrading of traditional industries by science and technology and the frontier fields of new science and technology industries. The key strategy is the reform to break the realistic separation state between industry and scientific system, so as to completely liberate the productive forces.

With the continuous development of generative artificial intelligence (AI) technology, along with its integration with deep learning, the technology of content generative artificial intelligence, AIGC, has emerged. It has given rise to a series of large-scale content-generative models, such as GPT and DALL-E. These large models are capable of generating high-quality, realistic content and have demonstrated powerful performance in fields like natural language processing, computer vision, and speech recognition. With the ongoing advancement of large model technology, research paradigms such as scaling up models with more data and pre-training models with prompts have emerged, providing strong technical support for researchers. Building intelligent agents on the basis of content-generative models enables decision-making, facilitating interaction between the digital world and the real world, thereby moving towards more generalized decision-making models. This helps people analyze and understand large amounts of complex data quickly and accurately, supporting decision-making processes in various physical and social domains. Therefore, decision generative artificial intelligence technology, AIGA, will become a new track in the field of artificial intelligence. It has already shown exciting innovations in areas such as chess competition, traffic congestion, and medical diagnosis. This article mainly introduces the development of AIGC and AIGA, especially their integration with large models, and summarizes their technical characteristics and the challenges they face.

[Objective/Significance] Considering that static topic models are difficult to meet users' dynamic analysis needs, in order to solve the problems of high computational costs or deep influence from subjective factors in existing dynamic topic models, this study proposes a text time window partitioning algorithm based on the LDA model, starting from time window similarity. [Method/Process] This study constructs a time window similarity index that integrates differences between time windows and consistency within time windows. This study constructs a time window partitioning algorithm based on this indicator and conducts empirical research using the innovation research field as an example. [Results/Conclusions] By analyzing the average JS divergence between topics under the optimal number of topics within each time window, as well as the average JS divergence between different topics between adjacent time windows, the partitioning results obtained by the algorithm proposed in this study are significantly better than those obtained by multiple fixed time window length partitioning methods, verifying the effectiveness of using the improved LDA model proposed in this study for text time window partitioning. The algorithm proposed in this study to some extent solves the shortcomings of existing dynamic topic models such as high computational costs and strong subjectivity, increases the objectivity and accuracy of text time window partitioning results, and can provide technical support for related research such as theme evolution.

[Objective/Significance] With the rapid development of artificial intelligence, computer vision is one of the important branches of artificial intelligence, and its new hot technical topics are constantly emerging. By analyzing the evolution of cutting-edge hot topics in the field of computer vision, we can gain an in-depth understanding of the hot technologies, development directions and evolutionary paths in the field of computer vision, and provide guidance and reference for related research and practice. [Method/Process] Based on computer vision-related patent data in the Derwent patent database, the BERTopic model was used to extract topics, and the BERT-based DTM model was used to further analyze the evolution of the topics, and summarize the research frontiers and hot topics of computer vision in recent years. [Results/Conclusions] Hot research topics in the field of computer vision are mainly concentrated in the fields of autonomous driving, multi-modal artificial intelligence, human-computer interaction, and three-dimensional modeling. Computer vision as a whole shows huge development space and potential.

[Objective/Significance] The renaming of the "Library and Information Science" discipline to "Information Resource Management" signifies that the development of the field of library and information science has officially entered a new stage. In the process of steady advancement of library and information science research, the funding of research projects has promoted the research output in this field in China. [Method/Process] Based on CSSCI database, using methods of keyword co-occurrence networks, community detection, and visualization, the study reveals the thematic structure of funded and non-funded papers in the field of library and information science in China, summarizes their development trends, and makes comparisons. [Results/Conclusions] Research without funding focuses more on library development theory and specific cultural services, as well as the construction of information service projects, while funded research pays more attention to the specific application of emerging computer technologies in the field of library and information science in the context of big data.