分类： 物理学 >> 普通物理:统计和量子力学,量子信息等 提交时间： 2024-04-10
摘要： Artificial Neural Network (ANN) has become a powerful tool in the field of scientific research with its powerful information encapsulation ability and convenient variational optimization method. In particular, there have been many recent advances in computational physics to solve variational problems. Deep neural network (DNN) is used to represent the wave function to solve quantum many-body problems using variational optimization. In this work we used a new Physics-Informed Neural Network (PINN) to represent the Cumulative Distribution Function (CDF) of some classical problems in quantum mechanics and to obtain their ground state wave function and ground state energy through the CDF. By benchmarking against the exact solution, the error of the results can be controlled at a very low level. This new network architecture and optimization method can provide a new choice for solving quantum many- body problems.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-18 合作期刊: 《数据智能（英文）》
摘要： With the rapid growth of the linked data on the Web, the quality assessment of the RDF data set becomes particularly important, especially for the quality and accessibility of the linked data. In most cases, RDF data sets are shared online, leading to a high maintenance cost for the quality assessment. This also potentially pollutes Internet data. Recently blockchain technology has shown the potential in many applications. Using the blockchain storage quality assessment results can reduce the centralization of the authority, and the quality assessment results have characteristics such as non-tampering. To this end, we propose an RDF data quality assessment model in a decentralized environment, pointing out a new dimension of RDF data quality. We use the blockchain to record the data quality assessment results and design a detailed update strategy for the quality assessment results. We have implemented a system DCQA to test and verify the feasibility of the quality assessment model. The proposed method can provide users with better cost-effective results when knowledge is independently protected.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-28 合作期刊: 《数据智能（英文）》
摘要： Temporal information is pervasive and crucial in medical records and other clinical text, as it formulates the development process of medical conditions and is vital for clinical decision making. However, providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is challenging. In order to capture complex temporal semantics in clinical text, we propose a novel Clinical Time Ontology (CTO) as an extension from OWL framework. More specifically, we identified eight time#2; related problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time, cyclic time, irregular time, negations and other complex aspects of clinical time. Then, we extended Allens and TEOs temporal relations and defined the relation concept description between complex and simple time. Simultaneously, we provided a formulaic and graphical presentation of complex time and complex time relationships. We carried out empirical study on the expressiveness and usability of CTO using real-world healthcare datasets. Finally, experiment results demonstrate that CTO could faithfully represent and reason over 93% of the temporal expressions, and it can cover a wider range of time-related classes in clinical domain.
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