Operads for complex system design specification, analysis and synthesis.

As the complexity and heterogeneity of a system grows, the challenge of specifying, documenting and synthesizing correct, machine-readable designs increases dramatically. Separation of the system into manageable parts is needed to support analysis at various levels of granularity so that the system is maintainable and adaptable over its life cycle. In this paper, we argue that operads provide an effective knowledge representation to address these challenges. Formal documentation of a syntactically correct design is built up during design synthesis, guided by semantic reasoning about design effectiveness. Throughout, the ability to decompose the system into parts and reconstitute the whole is maintained. We describe recent progress in effective modelling under this paradigm and directions for future work to systematically address scalability challenges for complex system design.

Transforming Health Care through Digital Revolutions.

The Internet, which has spanned several networks in a broad range of domains, is having a significant impact on every aspect of our lives. The next generation of networks will utilize a wide variety of resources with significant sensing capabilities. Such networks will extend beyond physically linked computers to include multimodal-information from biological, cognitive, semantic, and social networks. This paradigm shift will involve symbiotic networks of smart medical devices, and smart phones or mobile personal computing and communication devices. These devices-and the network-will be constantly sensing, monitoring, and interpreting the environment; this is sometimes referred to as the Internet of Things (IoT). We are also witnessing considerable interest in the "Omics" paradigm, which can be viewed as the study of a domain in a massive scale, at different levels of abstraction, in an integrative manner. The IoT revolution, combined with the Omics revolution (genomics and socio-omics or social networks) and artificial intelligence resurgence, will have significant implications for the way health care is delivered in the United States. After discussing a vision for health care in the future, we introduce the P9 health care concept, followed by a discussion of a framework for smart health care. Then, we present a case study and research directions, followed by examples of ongoing work at the National Institute of Standards and Technology (NIST).

A Web Resource for Exploring the CORD-19 Dataset Using Root- and Rule-Based Phrases.

This short paper describes a web resource-the NIST CORD-19 Web Resource-for community explorations of the COVID-19 Open Research Dataset (CORD-19). The tools for exploration in the web resource make use of the NIST-developed Root- and Rule-based method, which exploits underlying linguistic structures to create terms that represent phrases in a corpus. The method allows for auto-suggesting-related terms to discover terms to refine the search of a COVID-19 heterogenous document base. The method also produces taxonomic structures in the target domain as well as providing semantic information about the relationships between terms. This term structure can serve as a basis for creating topic modeling and trend analysis tools. In this paper, we describe use of a novel search engine to demonstrate some of the capabilities above.

Categorical models for process planning.

Process plans provide a structure for 1) identifying the tasks involved in a given process, 2) the resources needed to accomplish them, and 3) a variety of relationships and constraints between these. This information guides important operational decisions across various organizational levels, from the factory floor to the global supply chain. Efficient use of this information requires a concrete analytical model that can be easily represented in digital form. In this paper we present a modeling framework for process plans based on a branch of mathematics called category theory (CT). Specifically, string diagrams provide an intuitive yet precise graphical syntax for describing symmetric monoidal categories (SMCs), mathematical structures which support serial and parallel composition. Ideal for process representation, these structures also support a powerful mathematical toolkit. Here we use these tools to analyze the relationship between different levels of abstraction in process planning hierarchy. Our goal in this paper is to provide a precise mathematical account of similarities across levels of process hierarchy; to relate high-level axiomatization of the relationships across levels and to provide sound theoretical foundations for manipulations across levels.

Generating Domain Terminologies using Root- and Rule-Based Terms.

Motivated by the need for flexible, intuitive, reusable, and normalized terminology for guiding search and building ontologies, we present a general approach for generating sets of such terminologies from natural language documents. The terms that this approach generates are root- and rule-based terms, generated by a series of rules designed to be flexible, to evolve, and, perhaps most important, to protect against ambiguity and standardize semantically similar but syntactically distinct phrases to a normal form. This approach combines several linguistic and computational methods that can be automated with the help of training sets to quickly and consistently extract normalized terms. We discuss how this can be extended as natural language technologies improve and how the strategy applies to common use-cases such as search, document entry and archiving, and identifying, tracking, and predicting scientific and technological trends.

Workflow and electronic health records in small medical practices.

This paper analyzes the workflow and implementation of electronic health record (EHR) systems across different functions in small physician offices. We characterize the differences in the offices based on the levels of computerization in terms of workflow, sources of time delay, and barriers to using EHR systems to support the entire workflow. The study was based on a combination of questionnaires, interviews, in situ observations, and data collection efforts. This study was not intended to be a full-scale time-and-motion study with precise measurements but was intended to provide an overview of the potential sources of delays while performing office tasks. The study follows an interpretive model of case studies rather than a large-sample statistical survey of practices. To identify time-consuming tasks, workflow maps were created based on the aggregated data from the offices. The results from the study show that specialty physicians are more favorable toward adopting EHR systems than primary care physicians are. The barriers to adoption of EHR systems by primary care physicians can be attributed to the complex workflows that exist in primary care physician offices, leading to nonstandardized workflow structures and practices. Also, primary care physicians would benefit more from EHR systems if the systems could interact with external entities.