Nonlinear opinion models and other networked systems

Networks play a critical role in many physical, biological, and social systems. In this thesis, we investigate tools to model and analyze networked systems. We first examine some of the ways in which we can model social dynamics that take place on networks. We then study two recently developed data-analysis methods that employ a network framework and explore new ways in which they can be used to find meaningful signals in large data sets. In the first half of the thesis, we study opinion dynamics on networks. We begin by examining a class of opinion models, known as coevolving voter models (CVM), that couple the mechanisms of opinion formation and changing social connections. We then propose a version of CVMs that incorporates nonlinearity. In our models, we assume that individuals strive to achieve harmony and avoid disagreement, both by changing their social connections to reflect their opinions and by changing their opinions to reflect their social connections. By taking a minimalist approach to modeling social dynamics, we hope to gain a deeper understanding of how these two mechanisms can give rise to social phenomena such as the ``majority illusion''. Comparing several versions of CVMs, we find that seemingly small changes in update rules can lead to strikingly different behaviors. A particularly interesting feature of our nonlinear CVMs is that, under certain conditions, the opinion state that is held initially by a minority of the nodes can effectively spread to almost every node in a network if the minority nodes view themselves as the majority. We then discuss an ongoing project that involves another class of opinion models called bounded-confidence models. Specifically, we examine extensions of bounded-confidence models on hypergraphs and discuss some preliminary findings. In the second half of the thesis, we study problems in data analysis. We begin by considering topological structures as a tool to study integrated circuit (IC) devices. In particular, we examine a problem in the design and manufacturing of IC devices using topological data analysis (TDA), which is based on network structures called simplicial complexes. Failures in IC devices generally occur near the tolerance limits of photolithography systems, such as at the minimum separation distance between adjacent electronic components. However, for complex arrangements of electronic components, simply ensuring minimal separation is insufficient to guarantee that one can manufacture an IC design accurately and reliably. We apply tools from TDA to compare data from IC designs. Without inputting domain knowledge, we are able to infer several results about the IC design-manufacturing process. Finally, we discuss an ongoing project in the analysis of network data. Specifically, we explore applications of a recently developed algorithm called network dictionary learning (NDL) and discuss problems of network reconstruction and denoising using NDL on both synthetic and real-world networks

A modified belief-propagation decoder for the parallel decoding of product codes

In this dissertation a modification to the belief-propagation algorithm is presented. The algorithm modifies the belief-propagation algorithm to allow for the parallel decoding of product codes. The algorithm leverages the fact that each component code in the product code can be independently decoded because the codewords are encoded by independent and identically distributed (i.i.d.) processes. The algorithm maximises the parellelisation by decoding all the component codes in each dimension in parallel. In order to facilitate this process we developed new additional stages which are added to the belief-propagation algorithm: the codeword reliability estimation, the belief-aggregation and the exit test stages. The parallel product code decoder o ers a 0.2 dB worsening of the decoding BER performance when compared to the best serial decoder. However, the parallel belief-propagation decoder o ers a 7.26 time speedup on an eight-core processor, which is 0.91 of the theoretical maximum of eight for an eight-core processor

Self-Evolving Integrated Vertical Heterogeneous Networks

6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight on network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table

Role of mixed ionic and electronic transport on electrocatalytic activity of infiltrated nanoparticles in solid oxide fuel cell cermet electrodes

The infiltration of nanoparticle electrocatalysts into solid oxide fuel cell (SOFC) electrodes has been proven to produce a high density of electrochemically active sites, and reduce charge transfer polarization losses in SOFC electrodes. This is crucial for intermediate temperature operation, as these losses increase greatly at lower temperatures. Nickel-yttria stabilized zirconia (Ni-YSZ) cermets are low-cost, and exhibit excellent stability, but their main disadvantage stems from nickel coarsening and performance loss over their operational lifetimes. Infiltration of electrocatalyst nanoparticles has been shown to mitigate nickel coarsening and the consequent anode degradation. In this work, the effects of these infiltrants have been observed in a standard Ni-YSZ electrode. In addition to nickel, mixed ionic and electronic conducting (MIEC) phases have been infiltrated into Ni-YSZ scaffolds and their performance characterized using electrochemical impedance spectroscopy (EIS). Cross-sectional microscopy of fractured cells has been used to compare electrode microstructure and particle statistics. A model has been proposed to explain the origin of anode performance enhancement from nanoscale electrocatalysts