Code-Switching, a common phenomenon in written text and conversation, has been studied over decades by the natural language processing (NLP) research community. Initially, code-switching is intensively explored by leveraging linguistic theories and, currently, more machine-learning oriented approaches to develop models. We introduce a comprehensive systematic survey on code-switching research in natural language processing to understand the progress of the past decades and conceptualize the challenges and tasks on the code-switching topic. Finally, we summarize the trends and findings and conclude with a discussion for future direction and open questions for further investigation.

基于方面的情感分析（ABSA）是一个自然语言处理问题，需要分析用户生成的评论以确定：a）审查的目标实体，b）其所属的高级方面，c）对目标和方面表达的情绪。 ABSA的许多但分散的语料库使研究人员很难快速识别最适合特定ABSA子任务的Corpora。这项研究旨在介绍一个可用于培训和评估自动级ABSA系统的语料库数据库。此外，我们还概述了有关各种ABSA及其子任务的主要语料库，并突出了研究人员在选择语料库时应考虑的几个语料库功能。我们得出结论，需要进一步的大规模ABSA语料库。此外，由于每个语料库的构建方式都不同，因此研究人员在许多语料库上尝试一种新颖的ABSA算法，并且通常只采用一个或几个语料库，这是耗时的。该领域将从ABSA CORPORA的数据标准协议中受益。最后，我们讨论当前收集方法的优势和缺点，并为将来的ABSA数据集收集提出建议。

Nowadays, the applications of hydraulic systems are present in a wide variety of devices in both industrial and everyday environments. The implementation and usage of hydraulic systems have been well documented; however, today, this still faces a challenge, the integration of tools that allow more accurate information about the functioning and operation of these systems for proactive decision-making. In industrial applications, many sensors and methods exist to measure and determine the status of process variables (e.g., flow, pressure, force). Nevertheless, little has been done to have systems that can provide users with device-health information related to hydraulic devices integrated into the machinery. Implementing artificial intelligence (AI) technologies and machine learning (ML) models in hydraulic system components has been identified as a solution to the challenge many industries currently face: optimizing processes and carrying them out more safely and efficiently. This paper presents a solution for the characterization and estimation of anomalies in one of the most versatile and used devices in hydraulic systems, cylinders. AI and ML models were implemented to determine the current operating status of these hydraulic components and whether they are working correctly or if a failure mode or abnormal condition is present.

我们分享了我们最近的发现，以试图培训通用分割网络的各种细胞类型和成像方式。我们的方法建立在广义的U-NET体系结构上，该体系结构允许单独评估每个组件。我们修改了传统的二进制培训目标，以包括三个类以进行直接实例细分。进行了有关培训方案，培训设置，网络骨架和各个模块的详细实验。我们提出的培训方案依次从每个数据集中吸取小匹配，并且在优化步骤之前积累了梯度。我们发现，培训通用网络的关键是所有数据集上的历史监督，并且有必要以公正的方式对每个数据集进行采样。我们的实验还表明，可能存在共同的特征来定义细胞类型和成像方式的细胞边界，这可以允许应用训练有素的模型完全看不见的数据集。一些培训技巧可以进一步提高细分性能，包括交叉渗透损失功能中的班级权重，精心设计的学习率调度程序，较大的图像作物以进行上下文信息以及不平衡类别的其他损失条款。我们还发现，由于它们更可靠的统计估计和更高的语义理解，分割性能可以受益于组规范化层和缺陷的空间金字塔池模块。我们参与了在IEEE国际生物医学成像研讨会（ISBI）2021举行的第六个细胞跟踪挑战（CTC）。我们的方法被评估为在主要曲目的初始提交期间，作为最佳亚军，并在额外的竞争中获得了第三名，以准备摘要出版物。