Connectivity, Complexity and Catastrophe in Large-scale Systems

This book represents an approach to large-scale system modeling that is a challenging synthesis for the systems analyst, the operations research worker, the system theorist, the policy analyst, and the student of social systems. After pointing out that the mathematical form of a system description dictates the types of questions that can be asked and answered by the model, the author declares that "there is no such thing as a model system: there are many models, each with its own characteristic mathematical features and each capable of addressing a certain subset of important questions about the system and its operation". The book supports this point with examples from a wide spectrum of contexts (such as physics, economic activity, water-resource management, ecology, transportation, and physiology) viewed from the points of view of various models and theories (such as general system theory, control theory, graph theory, linear and nonlinear system theory, and catastrophy theory). Against this broad background, the book then considers in depth the relations to large-scale systems of the theories of connectivity, complexity, stability, catastrophy, and resilience

Social network support for data delivery infrastructures

Network infrastructures often need to stage content so that it is accessible to consumers. The standard solution, deploying the content on a centralised server, can be inadequate in several situations. Our thesis is that information encoded in social networks can be used to tailor content staging decisions to the user base and thereby build better data delivery infrastructures. This claim is supported by two case studies, which apply social information in challenging situations where traditional content staging is infeasible. Our approach works by examining empirical traces to identify relevant social properties, and then exploits them. The first study looks at cost-effectively serving the ``Long Tail'' of rich-media user-generated content, which need to be staged close to viewers to control latency and jitter. Our traces show that a preference for the unpopular tail items often spreads virally and is localised to some part of the social network. Exploiting this, we propose Buzztraq, which decreases replication costs by selectively copying items to locations favoured by viral spread. We also design SpinThrift, which separates popular and unpopular content based on the relative proportion of viral accesses, and opportunistically spins down disks containing unpopular content, thereby saving energy. The second study examines whether human face-to-face contacts can efficiently create paths over time between arbitrary users. Here, content is staged by spreading it through intermediate users until the destination is reached. Flooding every node minimises delivery times but is not scalable. We show that the human contact network is resilient to individual path failures, and for unicast paths, can efficiently approximate flooding in delivery time distribution simply by randomly sampling a handful of paths found by it. Multicast by contained flooding within a community is also efficient. However, connectivity relies on rare contacts and frequent contacts are often not useful for data delivery. Also, periods of similar duration could achieve different levels of connectivity; we devise a test to identify good periods. We finish by discussing how these properties influence routing algorithms.This work was supported by a St. John's College Benefactor's Scholarship and a Research Studentship from the Cambridge Philosophical Society

Bayesian networks for the multi-risk assessment of road infrastructure

The purpose of this study is to develop a methodological framework for the multi-risk assessment of road infrastructure systems. Since the network performance is directly linked to the functional states of its physical elements, most efforts are devoted to the derivation of fragility functions for bridges exposed to potential earthquake, flood and ground failure events. Thus, a harmonization effort is required in order to reconcile fragility models and damage scales from different hazard types. The proposed framework starts with the inventory of the various hazard-specific damaging mechanisms or failure modes that may affect each bridge component (e.g. piers, deck, bearings). Component fragility curves are then derived for each of these component failure modes, while corresponding functional consequences are proposed in a component-level damage-functionality matrix, thanks to an expert-based survey. Functionality-consistent failure modes at the bridge level are then assembled for specific configurations of component damage states. Finally, the development of a Bayesian Network approach enables the robust and efficient derivation of system fragility functions that (i) directly provide probabilities of reaching functionality losses and (ii) account for multiple types of hazard loadings and multi-risk interactions. At the network scale, a fully probabilistic approach is adopted in order to integrate multi-risk interactions at both hazard and fragility levels. A temporal dimension is integrated to account for joint independent hazard events, while the hazard-harmonized fragility models are able to capture cascading failures. The quantification of extreme events cannot be achieved by conventional sampling methods, and therefore the inference ability of Bayesian Networks is investigated as an alternative. Elaborate Bayesian Network formulations based on the identification of link sets are benchmarked, thus demonstrating the current computational difficulties to treat large and complex systems

A Hybrid Visual Control Scheme to Assist the Visually Impaired with Guided Reaching Tasks

In recent years, numerous researchers have been working towards adapting technology developed for robotic control to use in the creation of high-technology assistive devices for the visually impaired. These types of devices have been proven to help visually impaired people live with a greater degree of confidence and independence. However, most prior work has focused primarily on a single problem from mobile robotics, namely navigation in an unknown environment. In this work we address the issue of the design and performance of an assistive device application to aid the visually-impaired with a guided reaching task. The device follows an eye-in-hand, IBLM visual servoing configuration with a single camera and vibrotactile feedback to the user to direct guided tracking during the reaching task. We present a model for the system that employs a hybrid control scheme based on a Discrete Event System (DES) approach. This approach avoids significant problems inherent in the competing classical control or conventional visual servoing models for upper limb movement found in the literature. The proposed hybrid model parameterizes the partitioning of the image state-space that produces a variable size targeting window for compensatory tracking in the reaching task. The partitioning is created through the positioning of hypersurface boundaries within the state space, which when crossed trigger events that cause DES-controller state transition that enable differing control laws. A set of metrics encompassing, accuracy ($D$), precision ($\theta_{e}$), and overall tracking performance ($\psi$) are also proposed to quantity system performance so that the effect of parameter variations and alternate controller configurations can be compared. To this end, a prototype called \texttt{aiReach} was constructed and experiments were conducted testing the functional use of the system and other supporting aspects of the system behaviour using participant volunteers. Results are presented validating the system design and demonstrating effective use of a two parameter partitioning scheme that utilizes a targeting window with additional hysteresis region to filtering perturbations due to natural proprioceptive limitations for precise control of upper limb movement. Results from the experiments show that accuracy performance increased with the use of the dual parameter hysteresis target window model ($0.91 \leq D \leq 1$, $\mu(D)=0.9644$, $\sigma(D)=0.0172$) over the single parameter fixed window model ($0.82 \leq D \leq 0.98$, $\mu(D)=0.9205$, $\sigma(D)=0.0297$) while the precision metric, $\theta_{e}$, remained relatively unchanged. In addition, the overall tracking performance metric produces scores which correctly rank the performance of the guided reaching tasks form most difficult to easiest

Notes on Theory of Distributed Systems

Notes for the Yale course CPSC 465/565 Theory of Distributed Systems