Narrow Results

This paper investigates the influence of the interconnection network topology of a parallel system on the delivery time of an ensemble of messages, called the communication scheme. More specifically, we focus on the impact on the performance of structure in network topology and communication scheme. We introduce causal structure learning algorithms for the modeling of the communication time. The experimental data, from which the models are learned automatically, is retrieved from simulations. The qualitative models provide insight about which and how variables influence the communication performance. Next, a generic property is defined which characterizes the performance of individual communication schemes and network topologies. The property allows the accurate quantitative prediction of the runtime of random communication on random topologies. However, when either communication scheme or network topology exhibit regularities the prediction can become very inaccurate. The causal models can also differ qualitatively and quantitatively. Each combination of communication scheme regularity type, e.g. a one-to-all broadcast, and network topology regularity type, e.g. torus, possibly results in a different model which is based on different characteristics.

Tuomela's philosophical account of joint intentions is formalized in a special setting in which fully specified plans are available for the execution of the intended joint action. Using additional modal logical assumptions the definition is simplified and used to investigate how the presence of a joint intention can be efficiently checked.

A knowledge graph is a visual method that can display the information contained in the knowledge points, core structure, and comprehensive knowledge structure technology. In recent years, with the innovation of science and technology, the business field became keen on knowledge graphs and the graphical display method. However, the application of knowledge graphs in the business field is mainly limited to search engines, question, and answer systems because of the technical difficulties of knowledge extraction and knowledge graph drawing of unstructured text, especially the knowledge extraction of amorphous culture. It can provide knowledgeable service to users by analyzing the knowledge entity contained in encyclopedia knowledge or knowledge base. This paper will focus on the critical link of knowledge extraction of the knowledge graph, adopt a depth learning algorithm to solve this urgent problem and combine with the application of knowledge graph in substation fault to analyze the construction process of substation fault knowledge map based on AI.