An Agent-Based Framework for Complex Networks

Part 11: Multi Agent - IoTInternational audienceA large number of research and industrial projects could benefit from a module-based development. However, these modules and the communication between them may vary from project to project. Therefore, a general middleware instead of several specialized middlewares for each domain is desired. This paper presents the ACONA Framework (Agent-based Complex Network Architecture). It is an agent-based middleware with a lightweight and flexible infrastructure. Also, it offers the possibility of evolutionary programming. Its performance is demonstrated in three applications: (i) A cognitive architecture with around 40 interconnected modules; (ii) a stock market simulator with elements of evolutionary programming; and (iii) an industry 4.0 application of a conveyor belt

Potential for low fracture toughness and lamellar tearing on PWR steam generator and reactor coolant pump supports. Resolution of generic technical activity A-12

This report summarizes work performed by the Nuclear Regulatory Commission staff and its contractor, Sandia Laboratories, in the resolution of Generic Technical Activity A-12, ''Potential for Low Fracture Toughness and Lamellar Tearing in PWR Steam Generator and Reactor Coolant Pump Supports.'' The report describes the technical issues, the technical studies performed by Sandia describes the technical issues, the technical studies performed by Sandia Laboratories, the NRC staff's technical positions based on these studies, and the staff's plan for implementing its technical positions. It also provides recommendations for further work. The complete technical input from Sandia Laboratories is appended to the report

Potential for low fracture toughness and lamellar tearing on PWR steam generator and reactor coolant pump supports : resolution of generic technical activity A-12 /

"Manuscript completed: August 1979; Date published: October 1979.""NUREG-0577, For comment."Includes bibliographical references.Mode of access: Internet

Towards encoding time in text-based entity embeddings

Knowledge Graphs (KG) are widely used abstractions to represent entity-centric knowledge. Approaches to embed entities, entity types and relations represented in the graph into vector spaces - often referred to as KG embeddings - have become increasingly popular for their ability to capture the similarity between entities and support other reasoning tasks. However, representation of time has received little attention in these approaches. In this work, we make a first step to encode time into vector-based entity representations using a text-based KG embedding model named Typed Entity Embeddings (TEEs). In TEEs, each entity is represented by a vector that represents the entity and its type, which is learned from entity mentions found in a text corpus. Inspired by evidence from cognitive sciences and application-oriented concerns, we propose an approach to encode representations of years into TEEs by aggregating the representations of the entities that occur in event-based descriptions of the years. These representations are used to define two time-aware similarity measures to control the implicit effect of time on entity similarity. Experimental results show that the linear order of years obtained using our model is highly correlated with natural time flow and the effectiveness of the time-aware similarity measure proposed to flatten the time effect on entity similarity