BKG检测系统多线程优化及Python实现

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资源摘要信息:"MY101 BKG数字系统 - 0821是一个关于数字系统设计的资源,特别关注于0821版本的BKG检测多线程应用。该资源的标题和描述表明它主要涉及的可能是计算机编程领域,尤其是Python语言,因为标签中出现了'python'。考虑到BKG通常代表背景知识图谱(Background Knowledge Graph)或后台(Backend)等含义,我们可以推断该资源可能与后端系统设计、多线程编程以及与数字系统相关联的复杂算法实现有关。资源的具体内容可能包括但不限于以下知识点: 1. Python编程基础:包括Python语言的语法、数据类型、控制结构、函数定义、模块使用等基础知识点,这些都是编写多线程程序的前提。 2. 多线程编程概念:多线程是允许同时进行多个线程来执行任务的一种编程方式,Python通过threading模块来支持多线程。理解如何创建线程、线程的生命周期、线程同步和通信(如锁、信号量、事件等)是此资源可能涉及的内容。 3. 背景知识图谱(BKG)的应用:BKG可能用于复杂数据处理场景,例如推荐系统、搜索算法等,资源可能会涉及如何使用多线程来优化BKG的计算性能和响应速度。 4. 系统优化和调试:在多线程环境下,数据共享和同步成为一大挑战。资源可能包含如何对多线程数字系统进行性能调优,以及在发生线程安全问题时的调试方法。 5. Python在数字系统设计中的应用:Python作为一种高级编程语言,在数字系统设计中具有易读性和简洁性的优势。资源可能包含Python在后端服务、算法实现以及数据处理中的具体应用案例。 6. 系统测试与维护:在完善数字系统设计的过程中,系统测试是不可或缺的一步。资源可能讨论如何进行单元测试、集成测试,以及在多线程环境下保持系统稳定性和可靠性的策略。 由于该资源的文件名称列表中只有一个文件名'MY101 BKG digital system',我们可以推断此资源可能是一份文档、教程、源代码或相关项目文件。文档可能包括详细的说明、代码示例、算法图解、以及对实现细节的讨论。此外,标题中的'0821'可能指代特定的版本号,意味着这可能是0821版本的更新或修订,这可能涉及对之前版本的改进、新的功能特性或者修复了之前版本中出现的bug。 总而言之,这个资源可能是一个关于使用Python语言实现和优化多线程BKG数字系统的综合性指南或教程,涵盖了从基础知识到高级应用的广泛话题,适合那些有志于深入研究后端系统开发和多线程编程的IT专业人士。"

Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思

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代码报错properties of null (reading 'addEventListener') at 2.3bkg.html:138:38下面是代码<!DOCTYPE html> <html> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>后台管理界面示例</title> <style> body { margin: 0; padding: 0; font-family: Arial, Helvetica, sans-serif; } header { background-color: #333; color: #fff; display: flex; justify-content: space-between; align-items: center; padding: 20px 20px 20px 20px; position: fixed; top: 0; left: 0; right: 0; z-index: 1; } header h1 { margin: 0; font-size: 24px; } header .user { display: flex; align-items: center; cursor: pointer; } header .user img { width: 30px; height: 30px; border-radius: 50%; margin-right: 10px; } .sidebar { background-color: #eee; position: absolute; top: 70px; left: -200px; bottom: 0; width: 200px; padding: 10px; overflow: auto; z-index: 1; transition: left 0.3s ease-in-out; } .sidebar.show { left: 0; } .sidebar h2 { margin: 0; font-size: 18px; margin-bottom: 10px; } .sidebar ul { padding: 0; margin: 0; list-style: none; } .sidebar li { margin-bottom: 5px; } .sidebar a { display: block; padding: 5px 10px; color: #333; border-radius: 5px; text-decoration: none; background-color: #fff; transition: background-color 0.2s ease-in-out; } .sidebar a:hover { background-color: #ddd; } .content { margin: 60px 0 0 220px; padding: 10px; background-color: #f5f5f5; min-height: calc(100vh - 70px - 10px); } </style> </head> <body>

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