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Traditional robot task planning methods face challenges when dealing with highly unstructured environments and complex tasks. We propose a task planning method that combines human expertise with an LLM and have designed an LLM prompt template, Think_Net_Prompt, with stronger expressive power to represent structured professional knowledge. We further propose a method to progressively decompose tasks and generate a task tree to reduce the planning volume for each task, and we have designed a strategy to decouple robot task planning. By dividing different planning entities and separating the task from the actual machine binding process, the task planning process becomes more flexible. Research results show that our method performs well in handling specified code formats, understanding the relationship between tasks and subtasks, and extracting parameters from text descriptions. However, there are also problems such as limited complexity of task logic handling, ambiguity in the quantity of parts and the precise location of assembly. Improving the precision of task description and cognitive structure can bring certain improvements. https://github.com/NOMIzy/Think_Net_Prompt

Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries' data of the international trading network. A recommendation system is then used to identify the products corresponding to the production capacity of each individual country, but are somehow overlook by the country. Then, we simulate the evolution of the country's fitness if it would have followed the recommendations. The result of this work is the combination combine these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries.

Recently, a novel direct counterfactual quantum communication protocol was proposed using chained quantum Zeno effect. We found that this protocol is far from being widely used in practical channels, due to the side effect of 'chained', which leads to a dramatic increase of the equivalent optical distance between Alice and Bob. Therefore, not only the transmission time of a single bit increases in multiple times, but also the protocol is more sensitive to the noise. Here, we proposed an improved protocol, in which quantum interference is employed to destroy the nested structure induced by 'chained' effect. Moreover, we proved that a better counterfactuality is easier to be achieved, and showed that our protocol outperforms the former in the presence of noises., Comment: 6 pages, 4 figures