A Logical Approach to Discrete Math a book for programming logics
2022-01-04 16:39:37 20.27MB formal method programming
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Logical Link Control Protocol Technical Specification
2021-12-30 21:36:48 429KB NFC LLCP
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离线安装包,亲测可用
2021-12-01 09:01:48 212KB linux
The tests in this specification verify that the Logical Layer of Router in a Router Assembly is compliant with the USB4 Specification. If the Router Assembly contains one or more On-Board Re-timers, additional tests verify that the Logical Layer of the Re-timer(s) in the Router Assembly are compliant the USB4 Re-timer Specification.
2021-08-10 09:07:38 1.99MB usb 资源达人分享计划
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The tests in this specification verify that the Logical Layer of Router in a Router Assembly is compliant with the USB Specification. If the Router Assembly contains one or more On-Board Re-timers, additional tests verify that the Logical Layer of the Re-timer(s) in the Router Assembly are compliant the USB4 Re-timer Specification.
2021-08-07 18:09:06 1.95MB usb 资源达人分享计划
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该网站是为蒙大拿州立大学的写作课程而创建的。 我负责编程、设计和制图。 这些信息是由我班上的另外两名学生收集的。
2021-07-18 17:03:24 88KB 开源软件
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国外原版逻辑学,哈佛大学畅销读物. 国外原版逻辑学,哈佛大学畅销读物.
2021-06-26 13:28:42 2.38MB logical 逻辑学
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渣打银行Logical测试题含答案评析Logical
2021-06-08 19:37:28 778KB Logical
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Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict what drugs are likely to target proteins involved with both diseases X and Y?—a query that requires reasoning about all possible proteins that might interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries—a flexible but tractable subset of first-order logic—on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.
2021-06-07 11:07:52 1.3MB NLP
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刚考了TalentQ的试题。分享一下logical的。相信我保你可以拿高分
2021-04-24 13:19:47 271KB Talent Logica
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