Kevin Winner

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Controlling particle swarm optimization is typically an unintuitive task, involving a process of adjusting low-level parameters of the system that often do not have obvious correlations with the emergent properties of the optimization process. We propose a method for controlling particle swarm optimization with non-explicit control parameters: parameters(More)
1. Migration is a common strategy used by birds that breed in seasonal environments, and multiple environmental and biological factors determine the timing of migration. How these factors operate in combination during autumn migration, which is considered to be under weaker time constraints relative to spring migration, is not clear. 2. Here, we examine the(More)
We consider the problem of inference in a probabilistic model for transient populations where we wish to learn about arrivals, departures, and population size over all time, but the only available data are periodic counts of the population size at specific observation times. The underlying model arises in queueing theory (as an Mt/G/∞ queue) and also in(More)
Graphical models with latent count variables arise in a number of fields. Standard exact inference techniques such as variable elimination and belief propagation do not apply to these models because the latent variables have countably infinite support. As a result, approximations such as truncation or MCMC are employed. We present the first exact inference(More)
Billions of birds migrate at night over North America each year. However, few studies have described the phenology of these movements, such as magnitudes, directions, and speeds, for more than one migration season and at regional scales. In this study, we characterize density, direction, and speed of nocturnally migrating birds using data from 13 weather(More)
Previous work in adaptive supersampling methods have utilized algorithmic approaches to analyze properties of the object space or image space which might benefit from increased supersampling. These techniques generally increase the computational complexity of simple supersampling, reducing the potential performance gain from employing adaptive(More)
In many scientific fields, models are used to characterize relationships and processes, as well as to predict outcomes from initial conditions and inputs. These models can support the decision-making process by allowing investigators to consider the likely effects of possible interventions and identify efficient ways to achieve desired outcomes. Machine(More)
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