7 research outputs found
Decision Diagram Based Symbolic Algorithm for Evaluating the Reliability of a Multistate Flow Network
Evaluating the reliability of Multistate Flow Network (MFN) is an NP-hard problem. Ordered binary decision diagram (OBDD) or variants thereof, such as multivalued decision diagram (MDD), are compact and efficient data structures suitable for dealing with large-scale problems. Two symbolic algorithms for evaluating the reliability of MFN, MFN_OBDD and MFN_MDD, are proposed in this paper. In the algorithms, several operating functions are defined to prune the generated decision diagrams. Thereby the state space of capacity combinations is further compressed and the operational complexity of the decision diagrams is further reduced. Meanwhile, the related theoretical proofs and complexity analysis are carried out. Experimental results show the following: (1) compared to the existing decomposition algorithm, the proposed algorithms take less memory space and fewer loops. (2) The number of nodes and the number of variables of MDD generated in MFN_MDD algorithm are much smaller than those of OBDD built in the MFN_OBDD algorithm. (3) In two cases with the same number of arcs, the proposed algorithms are more suitable for calculating the reliability of sparse networks
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
We present an open-source and extensible knowledge extraction toolkit DeepKE,
supporting complicated low-resource, document-level and multimodal scenarios in
the knowledge base population. DeepKE implements various information extraction
tasks, including named entity recognition, relation extraction and attribute
extraction. With a unified framework, DeepKE allows developers and researchers
to customize datasets and models to extract information from unstructured data
according to their requirements. Specifically, DeepKE not only provides various
functional modules and model implementation for different tasks and scenarios
but also organizes all components by consistent frameworks to maintain
sufficient modularity and extensibility. We release the source code at GitHub
in https://github.com/zjunlp/DeepKE with Google Colab tutorials and
comprehensive documents for beginners. Besides, we present an online system in
http://deepke.openkg.cn/EN/re_doc_show.html for real-time extraction of various
tasks, and a demo video.Comment: Work in progress and the project website is http://deepke.zjukg.cn