Survivability through pre-configured protection in optical mesh networks

Network survivability is a very important issue, especially in optical networks that carry huge amount of traffic. Network failures which may be caused by human errors, malfunctional systems and natural disaster (eg. Earthquakes and lightening storms), have occurred quite frequently and sometimes with unpredictable consequences. Survivability is defined as the ability of the network to maintain the continuity of service against failures of network components. Pre-configuration and dynamic restoration are two schemes for network survivability. For each scheme, survivability algorithms can be applied at either Optical Channel sublayer (Och) known as link-based. Or, Optical Multiplex Section sublayer (OMS) known as path-based. The efficiency of survivability algorithms can be assessed through such criteria as capacity efficiency, restoration time and quality service. Dynamic restoration is more efficient than pre-configuration in terms of capacity resource utilization, but restoration time is longer and 100% service recovery cannot be guaranteed because sufficient spare capacity may not be available at the time of failures. Similarly, path-based survivability offers a high performance scheme for utilizing capacity resource, but restoration time is longer than link based survivability

Graph Theory for Survivability Design in Communication Networks

Design of survivable communication networks has been a complex task. Without establishing network survivability, there can be severe consequences when a physical link fails. Network failures which may be caused by dig-ups, vehicle crashes, human errors, system malfunctions, fire, rodents, sabotage, natural disasters (e.g. floods, earthquakes, lightning storms), and some other factors, have occurred quite frequently and sometimes with unpredictable consequences. To tackle these, survivability measures in a communication network can be implemented at the service layer, the logical layer, the system layer, and the physical layer

Two-view Graph Neural Networks for Knowledge Graph Completion

We present an effective GNN-based knowledge graph embedding model, named WGE, to capture entity- and relation-focused graph structures. In particular, given the knowledge graph, WGE builds a single undirected entity-focused graph that views entities as nodes. In addition, WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes. WGE then proposes a GNN-based architecture to better learn vector representations of entities and relations from these two single entity- and relation-focused graphs. WGE feeds the learned entity and relation representations into a weighted score function to return the triple scores for knowledge graph completion. Experimental results show that WGE outperforms competitive baselines, obtaining state-of-the-art performances on seven benchmark datasets for knowledge graph completion.Comment: 13 pages; 3 tables; 3 figure

Evolution of microstructure and transport properties of cement pastes due to carbonation under a CO2 pressure gradient: a modeling approach

Most carbonation models only account for diffusion as the main transport mechanism rather than advection. Nevertheless, in the case of concrete used for underground waste disposal facilities, concrete may be subjected to a high hydrostatic pressure and the surrounding environment may contain a high dissolved CO2 concentration. Therefore, a combination of diffusion and advection should be taken into account. This is also the case in accelerated carbonation where a high CO2 pressure gradient is applied in which advection in the gas phase has a significant contribution to the carbonation process. This study aims at developing a model to predict the evolution of the microstructure and transport properties of cement pastes due to carbonation under accelerated conditions in which a pressure gradient of pure CO2 is applied. The proposed model is based on a macroscopic mass balance for carbon dioxide in gaseous and aqueous phases. Besides the prediction of the changes in transport properties (diffusivity, permeability), the model also enables to predict the changes in microstructure. Data from accelerated tests were used to validate the model. Preliminary verification with experimental results shows a good agreement

Narrative structure analysis with education and training videos for e-learning

This paper deals with the problem ofstructuralizing education and training videos for high-level semantics extraction and nonlinear media presentation in e-learning applications. Drawing guidance from production knowledge in instructional media, we propose six main narrative structures employed in education and training videos for both motivation and demonstration during learning and practical training. We devise a powerful audiovisual feature set, accompanied by a hierarchical decision tree-based classification system to determine and discriminate between these structures. Based on a two-liered hierarchical model, we demonstrate that we can achieve an accuracy of 84.7% on a comprehensive set of education and training video data.<br /