Besides regular paper presentations, the program of SUM 2013 will feature invited talks by Christos Faloutsos (Carnegie Mellon University) and Steve Eubank (Virginia Tech), and an invited tutorial by Rama Chellappa (University of Maryland).
Given the specifics of a virus (or product, or hashtag) how quickly will it propagate on a contact network? Will it create an epidemic, or will it quickly die out? The way a virus/product/meme propagates on a graph is important, because it can help us design immunization policies (if we want to stop it) or marketing policies (if we want it to succeed). We present some surprising results on the so-called 'epidemic threshold', we discuss the effects of time-varying contact networks, and we present fast algorithms to achieve near-optimal immunization.
It is well-known that the structure of host-host contact networks can play an important role in determining the spread of infectious disease. Especially over the past decade, there have been many attempts to infer human contact networks at scales from urban regions to continents, and to simulate epidemics on the resulting networks. Moreover, since both pharmaceutical and non-pharmaceutical interventions can be represented as changes in network structure, simulated epidemics can be used to evaluate hypothetical combinations of interventions. Unfortunately, it is difficult to understand the simulated epidemics' sensitivity to details in the network structure. Results, for example those relating degree distribution to outbreak dynamics, typically make unwarranted assumptions about independence or symmetries in the network that introduce hard-to-control errors. Understanding this sensitivity to network structure is crucial for answering several related questions:
During the past three decades, probabilistic methods and uncertainty analysis have been slowly but steadily integrated into computer vision research. During the early years, as more emphasis was given to geometry and probabilistic inference over geometric representations was challenging, the role of probabilistic inference was minimal. Since the introduction of Markov random fields, robust methods and error bounds, many computer vision problems are lending themselves for more rigorous analysis. In this talk, I will illustrate these ideas by highlighting the role played by MRFs in image analysis, error bounds for the structure from motion problem and some recent works on probabilistic inference on manifolds for activity recognition.