This chapter initially explains how dependencies are established when at least a part of an infrastructure system requires the provision of the service to function. Although the focus is on functional dependencies, this chapter also explores physical and conditional dependencies. Resilience metrics presented in previous chapters are broadened in order to represent the effect of dependencies on resilience levels. Dependencies established within an infrastructure system are also explained. The concept of buffer as a local storage of the resources related to the depending service is defined as part of these expanded metrics, and then it is exemplified by examining a practical application of such buffers: power plants for information and communication network (ICN) sites. After introducing the main concepts and ideas related to dependencies, this chapter takes a broader view by discussing interdependencies when those are established both directly and indirectly. The study of interdependencies for electric power grids and ICN also explores the relationship with other infrastructures, such as transportation networks and water distribution systems, and with community social systems.

In this chapter, we describe a tensor network (TN) based common language established between machine learning and many-body physics, which allows for bidirectional contributions. By showing that many-body wave functions are structurally equivalent to mappings of convolutional and recurrent networks, we bring forth quantum entanglement measures as natural quantifiers of dependencies modeled by such networks. Accordingly, we propose a novel entanglement-based deep learning design scheme that sheds light on the success of popular architectural choices made by deep learning practitioners and suggests new practical prescriptions. In the other direction, we construct TNs corresponding to deep recurrent and convolutional networks. This allows us to theoretically demonstrate that these architectures are powerful enough to represent highly entangled quantum systems polynomially more efficiently than previously employed architectures. We thus provide theoretical motivation to shift neural-network-based wave function representations closer to state-of-the-art deep learning architectures.

Africa has seen progress and setbacks with regard to the economic and socio-economic development after decolonization until ca. 2000. These are linked to historical and structural challenges, including the economic infrastructure the colonial powers left behind and the unfavourable geography of vast parts of the continent. In the post-colonial phase there has been much economic and trade dependence on the former colonial powers – giving rise to the dependency theory and the notion of neo-colonialism. There was often an unwillingness of the post-colonial leadership to set the course for the economies of their countries. And rentier states developed. Several initiatives – from Africa and beyond – have been proposed to deal with the economic misery, with those of the World Bank and the International Monetary Fund being the dominant ones, pushing African initiatives aside.

This chapter introduces the separationist approach to bilingualism and shows that its foundations are shaky. More generally, Creole continua argue that thinking of languages as discrete entities is a mistake. Code-switching involves integrating different types of linguistic material and establishing dependencies among them. The chapter also discusses methodological issues and argues that acceptability judgments of deep bilinguals are a good method to obtain data.