@conference {17904,
	title = {MDMap: A system for data-driven layout and exploration of molecular dynamics simulations},
	booktitle = {Biological Data Visualization (BioVis), 2011 IEEE Symposium on},
	year = {2011},
	month = {2011/10//},
	pages = {111 - 118},
	abstract = {Contemporary molecular dynamics simulations result in a glut of simulation data, making analysis and discovery a difficult and burdensome task. We present MDMap, a system designed to summarize long-running molecular dynamics (MD) simulations. We represent a molecular dynamics simulation as a state transition graph over a set of intermediate (stable and semi-stable) states. The transitions amongst the states together with their frequencies represent the flow of a biomolecule through the trajectory space. MDMap automatically determines potential intermediate conformations and the transitions amongst them by analyzing the conformational space explored by the MD simulation. MDMap is an automated system to visualize MD simulations as state-transition diagrams, and can replace the current tedious manual layouts of biomolecular folding landscapes with an automated tool. The layout of the representative states and the corresponding transitions among them is presented to the user as a visual synopsis of the long-running MD simulation. We compare and contrast multiple presentations of the state transition diagrams, such as conformational embedding, and spectral, hierarchical, and force-directed graph layouts. We believe this system could provide a road-map for the visualization of other stochastic time-varying simulations in a variety of different domains.},
	keywords = {computing;digital, driven, DYNAMICS, exploration;data, folding, graph;stochastic, landscapes;data, layout;molecular, MDMap;biomolecular, method;stochastic, processes;, simulation;graph, simulations;state, simulations;trajectory, space;biology, theory;molecular, time-varying, transition},
	doi = {10.1109/BioVis.2011.6094055},
	author = {Patro,R. and Ip, Cheuk Yiu and Bista,S. and Cho,S.S. and Thirumalai,D. and Varshney, Amitabh}
}
@conference {13142,
	title = {Learning Higher-order Transition Models in Medium-scale Camera Networks},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We present a Bayesian framework for learning higher- order transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the network and have a greater predictive power for multi-camera tracking than camera adjacency alone. These models also provide inherent resilience to camera failure, filling in gaps left by single or even multiple non-adjacent camera failures. Our approach to estimating higher-order transition models relies on the accurate assignment of camera observations to the underlying trajectories of objects moving through the network. We addresses this data association problem by gathering the observations and evaluating alternative partitions of the observation set into individual object trajectories. Searching the complete partition space is intractable, so an incremental approach is taken, iteratively adding observations and pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model. When the algorithm has considered all observations, the most likely (MAP) partition is taken as the true object trajectories. From these recovered trajectories, the higher-order statistics we seek can be derived and employed for tracking. The partitioning algorithm we present is parallel in nature and can be readily extended to distributed computation in medium-scale smart camera networks.},
	keywords = {(artificial, approach;medium-scale, association, Bayesian, camera, cameras;video, framework;data, fusion;iterative, graphical, intelligence);optical, learning;incremental, likelihood;multicamera, likely, methods;higher, methods;learning, model, model;video, movement;probabilistic, network;Bayes, network;most, order, partition, problem;higher-order, statistics;higher-order, statistics;image, Surveillance, surveillance;, tracking;object, tracking;probability;video, transition},
	doi = {10.1109/ICCV.2007.4409203},
	author = {Farrell,R. and David Doermann and Davis, Larry S.}
}
