Titles and Abstracts of Talks - 4th Annual MoCSSy Symposium
Hamid Arabnia (Computer Science, University of Georgia)
TITLE:
ABSTRACT: Inherent limitations on the computational power of sequential uniprocessor systems have lead to the development of parallel multiprocessor systems. The two major issues in the formulation and design of parallel multiprocessor systems are algorithm design and architecture design. The parallel multiprocessor systems should be so designed so as to facilitate the design and implementation of the efficient parallel algorithms that exploit optimally the capabilities of the system. From an architectural point of view, the system should have low hardware complexity, be capable of being built of components that can be easily replicated, should exhibit desirable cost-performance characteristics, be cost effective and exhibit good scalability in terms of hardware complexity and cost with increasing problem size. In distributed memory multiprocessor systems, the processing elements can be considered to be nodes that are connected together via an interconnection network. In order to facilitate algorithm and architecture design, we require that the interconnection network have a low diameter, the system be symmetric and each node in the system have low degree of connectivity. Further, it is also desirable that the system configuration and behavior be amenable to a suitable and tractable mathematical description. The requirement of network symmetry ensures that each node in the network is identical to any other, thereby greatly reducing the architecture and algorithm design effort. For most symmetric network topologies, however, the requirements of low degree of connectivity for each node and low network diameter are often conflicting. Low network diameter often entails that each node in the network have a high degree of connectivity resulting in a drastic increase in the number of inter-processor connection links. A low degree of connectivity on the other hand, results in a high network diameter which in turn results in high inter-processor communication overhead and reduced efficiency of parallelism. Reconfigurable networks attempt to address this tradeoff. In a reconfigurable network each node has a fixed degree of connectivity irrespective of the network size. The network diameter is restricted by allowing the network to reconfigure itself into different configurations. In general, a reconfigurable system needs to satisfy the following criteria in order to be considered practically viable: (a) In each configuration the nodes in the network should have a fixed degree of active connectivity irrespective of network size, (b) The network diameter should be kept low via the reconfiguration mechanism and (c) The hardware for the reconfiguration mechanism (i.e. switch) should be of reasonable complexity. In this presentation, we discuss our design of a reconfigurable network topology that is targeted at medical applications; however, others have found a number of interesting properties about the network that makes it ideal for applications in computational biology as well as information engineering. We present some results and discuss our ongoing work in this area; we will also present a particular variation to our original reconfigurable network which is nature/biology inspired.
Jeffrey Brantingham (Department of Anthropology, University of California, Los Angeles)
TITLE: A Neutral Model of Gang Territory Formation
ABSTRACT: Many criminal street gangs are territorial in nature claiming compact geographic regions as their home turf. Gangs defend their territories both with the threat and use of violence. A neutral model based on spatial Lotka-Volterra competition equations shows that territories are strictly stable only where gangs are exactly symmetrical in all characteristics, which we interpret as a neutral condition. We test the model against data on violent crime involving thirteen street gangs in a region of Los Angeles. We find excellent agreement between model predictions and the observed spatial distribution of crime.
Steven Shechter (Sauder School of Business, University of British Columbia)
TITLE: Optimizing testing and treatment guidelines for patients with chronic kidney disease
ABSTRACT: Periodic laboratory tests help clinicians measure the progress of a disease and forecast when a treatment should start. Forecast accuracy is particularly important when there is a lead time to prepare patients for treatment. For example, chronic kidney disease patients who will need dialysis should ideally have a type of vascular access (called an arteriovenous fistula) created months in advance so that it matures by the time dialysis is needed. It is undesirable to have the fistula ready too early or too late, which may occur due to the uncertainty in fistula maturation and dialysis start times. We present a data-driven decision model that seeks to balance these tradeoffs under uncertainty in recommending testing and fistula preparation times.
Les Vertesi (Complex Systems Modelling Group, The IRMACS Centre, Simon Fraser University)
TITLE: Shifting the Paradigm: Using Complex Systems Frameworks to Address the Challenge of Obesity
ABSTRACT:
Raman Paranjape (Centre for Sustainable Communities, University of Regina)
TITLE: Agent-based Modelling in Healthcare: A Practical Approach
ABSTRACT: This presentation will summarize a set of agent-based simulations which have been developed over a number of years to examine the characteristics and dynamics of various aspects of the Canadian Healthcare System. The simulations show that the system dynamics of the health care system can have unique and less than obvious behaviours. In order to develop accurate and useful models, complex and sufficiently detailed components have to be developed. This presentation will focus on both microscopic and macroscopic interactions of components and the relationships between them. Results will include an examination of agent-activated Electronic Health Records, modelling of Patient Flows in a Neuro-Surgery Ward, and modelling of Diabetic Patients and the Healthcare System Response. All these examples show that there is significant benefit to healthcare modelling and as we move forward into a future with greater and greater demands on the Healthcare System; benefits can be garnered by Individuals, Healthcare Units and Policy Makers.
Brian Fisher (Interactive Arts & Technology, Simon Fraser University)
TITLE: Complexity in Visual Analytics
ABSTRACT: This talk explores the implications of the new science of visual analytics for cognitive science and informatics. Visual analytics was defined in a US National Research Agenda as “the science of analytical reasoning facilitated by interactive visual interfaces”. Visual analytics researchers seek to better inform decision-making in a variety of areas including public safety, aerospace engineering and maintenance, health care, and finance. They do this by building an understanding of the interaction of human decision makers and visual information systems, using that understanding to design better systems and work processes. Thus, visual analytics can be thought of as a translational cognitive science. It begins with new field study methods that characterize human and computational cognitive capabilities as they are used for decision-making in a range of situations. Because findings from field methods do not generalize well, we must then generate research questions for laboratory investigation. The results of those studies will enable us to build mathematical and computational theories that predict the impact of changes in technology on cognitive processes in technology-rich environments. Finally, we must design, implement, and evaluate new information technologies and work practices based on our research.
Visual analytics might interact with modelling in three ways: first, the information presented to the user might be generated by a model of the system in question, and interaction with that process may be ongoing (e.g. a mixed-initiative system); second, the need for a high degree of accuracy in prediction of human performance capabilities will require improved cognitive models (e.g. SOAR, SNIF-ACT); finally as the lag between an analyst's query and a graphical response becomes comparable to the pace of cognitive operations that the analyst performs as they reason, human and computational processes become "close coupled" and models of human-information systems may become necessary.
