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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Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their use in the lab. This means that designing and running experiments proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), a solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.
