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Current solutions for data quality control (QC) in the environmental sciences are locked within propriety platforms or reliant on specialized software. This can pose a problem for data users when attempting to integrate QC into their existing workflows. To address this limitation, we developed an embedded domain specific language (EDSL), Materia, that provides functions, data structures, and a fluent syntax for defining and executing quality control tests on data. Materia enables developers to more easily integrate QC into complex data pipelines and makes QC more accessible for students and citizen scientists. We evaluate Materia via two metrics: productivity and a quantitative performance analysis. Our productivity examples show how Materia can simplify complex descriptions of tests in Pandas and mirror natural language descriptions of common QC tests. We also demonstrate that Materia achieves satisfactory performance with over 200,000 floating-point values processed in under three seconds.
Performance analysis is critical for pinpointing bottlenecks in parallel applications. Several profilers exist to instrument parallel programs on HPC systems and gather performance data. Hatchet is an open-source Python library that can read profiling output of several tools, and enables the user to perform a variety of programmatic analyses on hierarchical performance profiles. In this paper, we augment Hatchet to support new features: a query language for representing call path patterns that can be used to filter a calling context tree, visualization support for displaying and interacting with performance profiles, and new operations for performing analyses on multiple datasets. Additionally, we present performance optimizations in Hatchet’s HPCToolkit reader and the unify operation to enable scalable analysis of large datasets.
Nonverbal interactions are a key component of human communication. Since robots have become significant by trying to get close to human beings, it is important that they follow social rules governing the use of space. Prior research has conceptualized personal space as physical zones which are based on static distances. This work examined how preferred interaction distance can change given different interaction scenarios. We conducted a user study using three different robot heights. We also examined the difference in preferred interaction distance when a robot approaches a human and, conversely, when a human approaches a robot. Factors included in quantitative analysis are the participants' gender, robot's height, and method of approach. Subjective measures included human comfort and perceived safety. The results obtained through this study shows that robot height, participant gender and method of approach were significant factors influencing measured proxemic zones and accordingly participant comfort. Subjective data showed that experiment respondents regarded robots in a more favorable light following their participation in this study. Furthermore, the NAO was perceived most positively by respondents according to various metrics and the PR2 Tall, most negatively.