<div>Ensuring the robustness and resilience of safety-critical systems from civil aviation to military surveillance technologies requires improvements to target tracking capabilities. Implementing target tracking as a distributed function can improve the quality and availability of information for end users. Any errors in the model of a target's dynamics or a sensor network's measurement process will result in estimates with degraded accuracy or even filter divergence. This dissertation solves a distributed estimation problem for estimating the state of a dynamical system and the parameters defining a model of that system.</div><div>The novelty of this work lies in the ability of a sensor network to maintain consensus on state and parameter estimates through local communications between sensor platforms.</div>

This thesis puts forward a novel way of control for robotic morphologies. Taking inspiration from Behaviour Based robotics and self-organisation principles, we present an interfacing mechanism, capable of adapting both to the user and the robot, while enabling a paradigm of intuitive control for the user. A transparent mechanism is presented, allowing for a seamless integration of control signals and robot behaviours. Instead of the user adapting to the interface and control paradigm, the proposed architecture allows the user to shape the control motifs in their way of preference, moving away from the cases where the user has to read and understand operation manuals or has to learn to operate a specific device. The seminal idea behind the work presented is the coupling of intuitive human behaviours with the dynamics of a machine in order to control and direct the machine dynamics. Starting from a tabula rasa basis, the architectures presented are able to identify control patterns (behaviours) for any given robotic morphology and successfully merge them with control signals from the user, regardless of the input device used. We provide a deep insight in the advantages of behaviour coupling, investigating the proposed system in detail, providing evidence for and quantifying emergent properties of the models proposed. The structural components of the interface are presented and assessed both individually and as a whole, as are inherent properties of the architectures. The proposed system is examined and tested both in vitro and in vivo, and is shown to work even in cases of complicated environments, as well as, complicated robotic morphologies. As a whole, this paradigm of control is found to highlight the potential for a change in the paradigm of robotic control, and a new level in the taxonomy of human in the loop systems.

O custo de serviços na Internet tem encorajado o uso de cache a nível de aplicação para suprir as demandas dos usuários e melhorar a escalabilidade e disponibilidade de aplicações. Cache a nível de aplicação, onde desenvolvedores manualmente controlam o conteúdo cacheado, tem sido adotada quando soluções tradicionais de cache não são capazes de atender aos requisitos de desempenho desejados. Apesar de sua crescente popularidade, este tipo de cache é tipicamente endereçado de maneira ad-hoc, uma vez que depende de detalhes específicos da aplicação para ser desenvolvida. Dessa forma, tal cache consiste em uma tarefa que requer tempo e esforço, além de ser altamente suscetível a erros. Esta dissertação avança o trabalho relacionado a cache a nível de aplicação provendo uma compreensão de seu estado de prática e automatizando a identificação de conteúdo cacheável, fornecendo assim suporte substancial aos desenvolvedores para o projeto, implementação e manutenção de soluções de caching. Mais especificamente, este trabalho apresenta três contribuições: a estruturação de conhecimento sobre caching derivado de um estudo qualitativo, um levantamento do estado da arte em abordagens de cache estáticas e adaptativas, e uma técnica que automatiza a difícil tarefa de identificar oportunidades de cache O estudo qualitativo, que envolveu a investigação de dez aplicações web (código aberto e comercial) com características diferentes, permitiu-nos determinar o estado de prática de cache a nível de aplicação, juntamente com orientações práticas aos desenvolvedores na forma de padrões e diretrizes. Com base nesses padrões e diretrizes derivados, também propomos uma abordagem para automatizar a identificação de métodos cacheáveis, que é geralmente realizado manualmente por desenvolvedores. Tal abordagem foi implementada como um framework, que pode ser integrado em aplicações web para identificar automaticamente oportunidades de cache em tempo de execução, com base na monitoração da execução do sistema e gerenciamento adaptativo das decisões de cache. Nós avaliamos a abordagem empiricamente com três aplicações web de código aberto, e os resultados indicam que a abordagem é capaz de identificar oportunidades de cache adequadas, melhorando o desempenho das aplicações em até 12,16%. / Latency and cost of Internet-based services are encouraging the use of application-level caching to continue satisfying users’ demands, and improve the scalability and availability of origin servers. Application-level caching, in which developers manually control cached content, has been adopted when traditional forms of caching are insufficient to meet such requirements. Despite its popularity, this level of caching is typically addressed in an adhoc way, given that it depends on specific details of the application. Furthermore, it forces application developers to reason about a crosscutting concern, which is unrelated to the application business logic. As a result, application-level caching is a time-consuming and error-prone task, becoming a common source of bugs. This dissertation advances work on application-level caching by providing an understanding of its state-of-practice and automating the decision regarding cacheable content, thus providing developers with substantial support to design, implement and maintain application-level caching solutions. More specifically, we provide three key contributions: structured knowledge derived from a qualitative study, a survey of the state-of-the-art on static and adaptive caching approaches, and a technique and framework that automate the challenging task of identifying cache opportunities The qualitative study, which involved the investigation of ten web applications (open-source and commercial) with different characteristics, allowed us to determine the state-of-practice of application-level caching, along with practical guidance to developers as patterns and guidelines to be followed. Based on such patterns and guidelines derived, we also propose an approach to automate the identification of cacheable methods, which is often manually done and is not supported by existing approaches to implement application-level caching. We implemented a caching framework that can be seamlessly integrated into web applications to automatically identify and cache opportunities at runtime, by monitoring system execution and adaptively managing caching decisions. We evaluated our approach empirically with three open-source web applications, and results indicate that we can identify adequate caching opportunities by improving application throughput up to 12.16%. Furthermore, our approach can prevent code tangling and raise the abstraction level of caching.

abstract: This thesis consists of three projects employing complexity economics methods to explore firm dynamics. The first is the Firm Ecosystem Model, which addresses the institutional conditions of capital access and entrenched competitive advantage. Larger firms will be more competitive than smaller firms due to efficiencies of scale, but the persistence of larger firms is also supported institutionally through mechanisms such as tax policy, capital access mechanisms and industry-favorable legislation. At the same time, evidence suggests that small firms innovate more than larger firms, and an aggressive firm-as-value perspective incentivizes early investment in new firms in an attempt to capture that value. The Ecological Firm Model explores the effects of the differences in innovation and investment patterns and persistence rates between large and small firms. The second project is the Structural Inertia Model, which is intended to build theory around why larger firms may be less successful in capturing new marketshare than smaller firms, as well as to advance fitness landscape methods. The model explores the possibility that firms with larger scopes may be less effective in mitigating the costs of cooperation because conditions may arise that cause intrafirm conflicts. The model is implemented on structured fitness landscapes derived using the maximal order of interaction (NM) formulation and described using local optima networks (LONs), thus integrating these novel techniques. Finally, firm dynamics can serve as a proxy for the ease at which people can voluntarily enter into the legal cooperative agreements that constitute firms. The third project, the Emergent Firm model, is an exploration of how this dynamic of voluntary association may be affected by differing capital institutions, and explores the macroeconomic implications of the economies that emerge out of the various resulting firm populations. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics for the Life and Social Sciences 2018

abstract: Design is a fundamental human activity through which we attempt to navigate and manipulate the world around us for our survival, pleasure, and benefit. As human society has evolved, so too has the complexity and impact of our design activities on the environment. Now clearly intertwined as a complex social-ecological system at the global scale, we struggle in our ability to understand, design, implement, and manage solutions to complex global issues such as climate change, water scarcity, food security, and natural disasters. Some have asserted that this is because complex adaptive systems, like these, are moving targets that are only partially designed and partially emergent and self-organizing. Furthermore, these types of systems are difficult to understand and control due to the inherent dynamics of "wicked problems", such as: uncertainty, social dilemmas, inequities, and trade-offs involving multiple feedback loops that sometimes cause both the problems and their potential solutions to shift and evolve together. These problems do not, however, negate our collective need to effectively design, produce, and implement strategies that allow us to appropriate, distribute, manage and sustain the resources on which we depend. Design, however, is not well understood in the context of complex adaptive systems involving common-pool resources. In addition, the relationship between our attempts at control and performance at the system-level over time is not well understood either. This research contributes to our understanding of design in common-pool resource systems by using a multi-methods approach to investigate longitudinal data on an innovative participatory design intervention implemented in nineteen small-scale, farmer-managed irrigation systems in the Indrawati River Basin of Nepal over the last three decades. The intervention was intended as an experiment in using participatory planning, design and construction processes to increase food security and strengthen the self-sufficiency and self-governing capacity of resource user groups within the poorest district in Nepal. This work is the first time that theories of participatory design-processes have been empirically tested against longitudinal data on a number of small-scale, locally managed common-pool resource systems. It clarifies and helps to develop a theory of design in this setting for both scientific and practical purposes. / Dissertation/Thesis / Doctoral Dissertation Environmental Social Science 2018

This thesis encompasses research on Artificial Intelligence in support of automating scientific discovery in the fields of biology and medicine. At the core of this research is the ongoing development of a general-purpose artificial intelligence framework emulating various facets of human-level intelligence necessary for building cross-domain knowledge that may lead to new insights and discoveries. To learn and build models in a data-driven manner, we develop a general-purpose learning framework called Syntactic Nonparametric Analysis of Complex Systems (SYNACX), which uses tools from Bayesian nonparametric inference to learn the statistical and syntactic properties of biological phenomena from sequence data. We show that the models learned by SYNACX offer performance comparable to that of standard neural network architectures. For complex biological systems or processes consisting of several heterogeneous components with spatio-temporal interdependencies across multiple scales, learning frameworks like SYNACX can become unwieldy due to the the resultant combinatorial complexity. Thus we also investigate ways to robustly reduce data dimensionality by introducing a new data abstraction. In particular, we extend traditional string and graph grammars in a new modeling formalism which we call Simplicial Grammar. This formalism integrates the topological properties of the simplicial complex with the expressive power of stochastic grammars in a computation abstraction with which we can decompose complex system behavior, into a finite set of modular grammar rules which parsimoniously describe the spatial/temporal structure and dynamics of patterns inferred from sequence data.

Organizational adaptability is critical to organizational survival, and executive leadership's inability to adapt to extreme disruptive complex events threatens survival. Scenario planning is one means of adapting to extreme disruptive complex events. In this qualitative interpretive phenomenological study, 20 executives who had lived experience with extreme disruptive complex events and applied scenario planning to help adapt participated in phenomenological interviews to share their experiences related to the application of scenario planning as a means adaptation to extreme disruptive complex events. Participants were from a single large organization with executives distributed throughout the United States and executives from 10 state agencies located within a single state. Using the thematic analysis process, 14 themes emerged. The themes included knowing the difference between adaptation and response, not being afraid to tackle difficult questions, scenario planning is never over because the environment constantly changes, the true measures of scenario planning value are the benefits achieved via the planning exercise versus the business application, and participation should be individuals who can or could have a direct influence on adaptation and do not get bogged down in structured and/or rigid processes, methods, or tools because while useful, they are not required to be successful. The implications for positive social change include the ability for organizations to reduce economic injury and the compound effects of disruption including the social impacts of business injury, disruption, recovery, job loss, and reduced revenue on communities and local economies.

This study is the exploration of the emergence of DataONE, a multidisciplinary, multinational, and multi-institutional virtual scientific collaboration to develop a cyberinfrastructure for earth sciences data, from the complex adaptive systems perspective. Data is generated through conducting 15 semi-structured interviews, observing three 3-day meetings, and 51 online surveys. The main contribution of this study is the development of a complexity framework and its application to a project such as DataONE. The findings reveal that DataONE behaves like a complex adaptive system: various individuals and institutions interacting, adapting, and coevolving to achieve their own and common goals; during the process new structures, relationships, and products emerge that harmonize with DataONE’s goals. DataONE is quite resilient to threats and adaptive to its environment, which are important strengths. The strength comes from its diversified structure and balanced management style that allows for frequent interaction among members. The study also offers insights to PI(s), managers, and funding institutions on how to treat complex systems. Additional results regarding multidisiplinarity, library and information sciences, and communication studies are presented as well.

Generative Multimedia (GM) applications are an increasingly popular way to implement interactive media performances. Our contributions include creating a metric for evaluating Generative Multimedia performances, designing a model for accepting perceiver preferences, and using those preferences to adapt GM performances. The metric used is imprecision, which is the ratio of the actual computation time of a GM element to the computation time of a complete version of that GM element. By taking a perceiver's preferences into account when making adaptation decisions, applications can produce GM performances that meet soft real-time and resource constraints while allocating imprecision to the GM elements the perceiver least cares about. Compared to other approaches, perceiver-directed imprecision best allocates impreciseness while minimizing delay.

Semiotic dynamics and computational evolutionary musicology literature investigate emergence and evolution of linguistic and musical conventions by using computational multi-agent complex adaptive system models. This thesis proposes a new computational evolutionary musicology model, by altering previous models of familiarity based musical interactions that try to capture evolution of songs as a co-evolutionary process through mate selection. The proposed modified familiarity game models a closed community of agents, where individuals of the society interact with each other just by using their musical expectations. With this novel methodology, it is found that constituent agents can form a musical agreement by agreeing on a shared bi-gram musical expectation scheme. This convergence is attained in a self-organizing fashion and throughout this process significant usage of n-gram melodic lines become observable. Furthermore, modified familiarity game dynamics are investigated and it is concluded that convergence trends are dependent on simulation parameters.