Processing 是一门开源编程语言和与之配套的集成开发环境（IDE）的名称。Processing 在电子艺术和视觉设计社区被用来教授编程基础，并运用于大量的新媒体和互动艺术作品中。
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论文题目： Learning Conceptual-Contextual Embeddings for Medical Text
论文摘要：
对于自然语言理解任务来说，外部知识通常是有用的。本文介绍了一个上下文文本表示模型，称为概念上下文(CC)嵌入，它将结构化的知识合并到文本表示中。与实体嵌入方法不同，文中提到的方法将知识图编码到上下文模型中。就像预先训练好的语言模型一样，CC嵌入可以很容易地在广泛的任务中重用。模型利用语义泛化，有效地编码了庞大的UMLS数据库。电子实验健康记录(EHRs)和医疗文本处理基准表明，而使得模型大大提高了监督医疗NLP任务的性能。
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From ancient to modern times, acoustic structures have been used to control the propagation of acoustic waves. However, the design of the acoustic structures has remained widely a time-consuming and computational resource-consuming iterative process. In recent years, Deep Learning has attracted unprecedented attention for its ability to tackle hard problems with huge datasets, which has achieved state-of-the-art results in various tasks. In this work, an acoustic structure design method is proposed based on deep learning. Taking the design of multi-order Helmholtz resonator for instance, we experimentally demonstrate the effectiveness of the proposed method. Our method is not only able to give a very accurate prediction of the geometry of the acoustic structures with multiple strong-coupling parameters, but also capable of improving the performance of evolutionary approaches in optimization for a desired property. Compared with the conventional numerical methods, our method is more efficient, universal and automatic, which has a wide range of potential applications, such as speech enhancement, sound absorption and insulation.
最新论文
From ancient to modern times, acoustic structures have been used to control the propagation of acoustic waves. However, the design of the acoustic structures has remained widely a time-consuming and computational resource-consuming iterative process. In recent years, Deep Learning has attracted unprecedented attention for its ability to tackle hard problems with huge datasets, which has achieved state-of-the-art results in various tasks. In this work, an acoustic structure design method is proposed based on deep learning. Taking the design of multi-order Helmholtz resonator for instance, we experimentally demonstrate the effectiveness of the proposed method. Our method is not only able to give a very accurate prediction of the geometry of the acoustic structures with multiple strong-coupling parameters, but also capable of improving the performance of evolutionary approaches in optimization for a desired property. Compared with the conventional numerical methods, our method is more efficient, universal and automatic, which has a wide range of potential applications, such as speech enhancement, sound absorption and insulation.
