To fully evaluate the overall performance of different NLP models in a given domain, many evaluation benchmarks are proposed, such as GLUE, SuperGLUE and CLUE. The field of natural language understanding has traditionally focused on benchmarks for various tasks in languages such as Chinese, English, and multilingual, however, there has been a lack of attention given to the area of classical Chinese, also known as "wen yan wen (文言文)", which has a rich history spanning thousands of years and holds significant cultural and academic value.For the prosperity of the NLP community, in this paper, we introduce the WYWEB evaluation benchmark, which consists of nine NLP tasks in classical Chinese, implementing sentence classification, sequence labeling, reading comprehension, and machine translation. We evaluate the existing pre-trained language models, which are all struggling with this benchmark. We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on classical Chinese NLU. The github repository is https://github.com/baudzhou/WYWEB.
Abstract:In water distribution systems, water leakage from cracked water pipes is a major concern for water providers. Generally, the relationship between the leakage rate and the water pressure can be modeled by a power function developed from the orifice equation. This paper presents an approximate solution for the computation of the steady-state leakage rate through a longitudinal line crack of a water distribution pipe considering the surrounding soil properties. The derived solution agrees well with results of numerical simulations. Compared with the traditional models, the new solution allows assessment of all the parameters that related with leakage including the pressure head inside the pipe, hydraulic conductivity, crack size and its position, and pipe size and its depth.
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