爱好 发表于 2025-4-1 05:45:54
http://reply.papertrans.cn/103/10216/1021542/1021542_61.pngJOT 发表于 2025-4-1 09:57:34
Yanting Jiang,Di Wu systematic description of a cyclic load. Then, the books use two probabilistic fatigue theories toestablish the limit state function of a component under cyclic load, and further to present how to calculate the reliability of a component under a cyclic loading spectrum. Finally, the book presents hPromotion 发表于 2025-4-1 13:58:14
http://reply.papertrans.cn/103/10216/1021542/1021542_63.pngdermatomyositis 发表于 2025-4-1 18:02:58
http://reply.papertrans.cn/103/10216/1021542/1021542_64.png流浪 发表于 2025-4-1 19:28:30
http://reply.papertrans.cn/103/10216/1021542/1021542_65.png空气传播 发表于 2025-4-2 02:43:36
Shuning Hou,Xueqing Zhao,Ning Liu,Xin Shi,Yun Wang,Guigang Zhang rise to non-equivalent double fault mutants, and hence, cannot be discarded. Moreover, we have developed several supplementary test case selection strategies to detect double faults that cannot be detected by existing test case selection strategies which aim at single-fault detection.insidious 发表于 2025-4-2 05:16:25
http://reply.papertrans.cn/103/10216/1021542/1021542_67.pngNonconformist 发表于 2025-4-2 08:33:24
Temporal Knowledge Graph Embedding for Link Predictioncing the position embedding characterizing the dynamic information of temporal knowledge graph, TKGE can generate the evolutional embedding of entities and relations for downstream applications, such as link prediction, recommender system, and so on. We conduct experiments on several real datasets.碳水化合物 发表于 2025-4-2 11:16:50
Temporal Knowledge Graph Embedding for Link Predictioncing the position embedding characterizing the dynamic information of temporal knowledge graph, TKGE can generate the evolutional embedding of entities and relations for downstream applications, such as link prediction, recommender system, and so on. We conduct experiments on several real datasets.BUMP 发表于 2025-4-2 16:53:07
Fusion of Natural Language and Knowledge Graph for Multi-hop Reasoninghe natural language. We tested the performance of the NLKGF model on two datasets requiring multi-hop reasoning. The experimental results show that NLKGF beats advanced benchmark models in multi-hop reasoning tasks, which proves superiority of our model.