DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic

Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics

Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application domain for biohybrid robotics, and review technological advances relevant to its future development. Construction, civil infrastructure maintenance and building occupancy in the last decades have comprised a major portion of economic production, energy consumption and carbon emissions. Integrating biological organisms into automated construction tasks and permanent building components therefore has high potential for impact. Live materials can provide several advantages over standard synthetic construction materials, including self-repair of damage, increase rather than degradation of structural performance over time, resilience to corrosive environments, support of biodiversity, and mitigation of urban heat islands. Here, we review relevant technologies, which are currently disparate. They span robotics, self-organizing systems, artificial life, construction automation, structural engineering, architecture, bioengineering, biomaterials, and molecular and cellular biology. In these disciplines, developments relevant to biohybrid construction and living buildings are in the early stages, and typically are not exchanged between disciplines. We, therefore, consider this review useful to the future development of biohybrid engineering for this highly interdisciplinary application.publishe

Efficient Content Distribution With Managed Swarms

Content distribution has become increasingly important as people have become more reliant on Internet services to provide large multimedia content. Efficiently distributing content is a complex and difficult problem: large content libraries are often distributed across many physical hosts, and each host has its own bandwidth and storage constraints. Peer-to-peer and peer-assisted download systems further complicate content distribution. By contributing their own bandwidth, end users can improve overall performance and reduce load on servers, but end users have their own motivations and incentives that are not necessarily aligned with those of content distributors. Consequently, existing content distributors either opt to serve content exclusively from hosts under their direct control, and thus neglect the large pool of resources that end users can offer, or they allow end users to contribute bandwidth at the expense of sacrificing complete control over available resources. This thesis introduces a new approach to content distribution that achieves high performance for distributing bulk content, based on managed swarms. Managed swarms efficiently allocate bandwidth from origin servers, in-network caches, and end users to achieve system-wide performance objectives. Managed swarming systems are characterized by the presence of a logically centralized coordinator that maintains a global view of the system and directs hosts toward an efficient use of bandwidth. The coordinator allocates bandwidth from each host based on empirical measurements of swarm behavior combined with a new model of swarm dynamics. The new model enables the coordinator to predict how swarms will respond to changes in bandwidth based on past measurements of their performance. In this thesis, we focus on the global objective of maximizing download bandwidth across end users in the system. To that end, we introduce two algorithms that the coordinator can use to compute efficient allocations of bandwidth for each host that result in high download speeds for clients. We have implemented a scalable coordinator that uses these algorithms to maximize system-wide aggregate bandwidth. The coordinator actively measures swarm dynamics and uses the data to calculate, for each host, a bandwidth allocation among the swarms competing for the host's bandwidth. Extensive simulations and a live deployment show that managed swarms significantly outperform centralized distribution services as well as completely decentralized peer-to-peer systems