scholarly journals Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature

Author(s):  
Yu Wang ◽  
Jinchao Li ◽  
Tristan Naumann ◽  
Chenyan Xiong ◽  
Hao Cheng ◽  
...  
2021 ◽  
Vol 7 (12) ◽  
pp. eabc9800
Author(s):  
Ryan J. Gallagher ◽  
Jean-Gabriel Young ◽  
Brooke Foucault Welles

Core-periphery structure, the arrangement of a network into a dense core and sparse periphery, is a versatile descriptor of various social, biological, and technological networks. In practice, different core-periphery algorithms are often applied interchangeably despite the fact that they can yield inconsistent descriptions of core-periphery structure. For example, two of the most widely used algorithms, the k-cores decomposition and the classic two-block model of Borgatti and Everett, extract fundamentally different structures: The latter partitions a network into a binary hub-and-spoke layout, while the former divides it into a layered hierarchy. We introduce a core-periphery typology to clarify these differences, along with Bayesian stochastic block modeling techniques to classify networks in accordance with this typology. Empirically, we find a rich diversity of core-periphery structure among networks. Through a detailed case study, we demonstrate the importance of acknowledging this diversity and situating networks within the core-periphery typology when conducting domain-specific analyses.


Author(s):  
Janice E. Cuny ◽  
Robert A. Dunn ◽  
Steven T. Hackstadt ◽  
Christopher W. Harrop ◽  
Harold H. Hersey ◽  
...  

Author(s):  
Shaw C. Feng ◽  
William Z. Bernstein ◽  
Thomas Hedberg ◽  
Allison Barnard Feeney

The need for capturing knowledge in the digital form in design, process planning, production, and inspection has increasingly become an issue in manufacturing industries as the variety and complexity of product lifecycle applications increase. Both knowledge and data need to be well managed for quality assurance, lifecycle impact assessment, and design improvement. Some technical barriers exist today that inhibit industry from fully utilizing design, planning, processing, and inspection knowledge. The primary barrier is a lack of a well-accepted mechanism that enables users to integrate data and knowledge. This paper prescribes knowledge management to address a lack of mechanisms for integrating, sharing, and updating domain-specific knowledge in smart manufacturing (SM). Aspects of the knowledge constructs include conceptual design, detailed design, process planning, material property, production, and inspection. The main contribution of this paper is to provide a methodology on what knowledge manufacturing organizations access, update, and archive in the context of SM. The case study in this paper provides some example knowledge objects to enable SM.


Author(s):  
Kyoungho An ◽  
Adam Trewyn ◽  
Aniruddha Gokhale ◽  
Shivakumar Sastry

Much of the existing literature on domain-specific modeling languages (DSMLs) focuses on either the DSML design and their use in developing complex software systems (e.g., in enterprise and web applications), or their use in physical systems (e.g., process control). With increasing focus on research and development of cyber-physical systems (CPS) such as autonomous automotive systems and process control systems, which are systems that tightly integrate cyber and physical artifacts, it becomes important to understand the need for and the roles played by DSMLs for such systems. One use of DSMLs for CPS systems is in the analysis and verification of different properties of the system. Many questions arise in this context: How are the cyber and physical artifacts represented in DSMLs? How can these DSMLs be used in analysis? This book chapter addresses these questions through a case study of reconfigurable conveyor systems used as a representative example.


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