First record of \u3ci\u3eOrsilochides scurrilis\u3c/i\u3e (Stål) (Hemiptera: Heteroptera: Scutelleridae: Pachycorinae) in the United States, with notes on the biology and distribution of U.S. species of \u3ci\u3eOrsilochides\u3c/i\u3e Kirkaldy

Orsilochides scurrilis (Stål) (Hemiptera: Heteroptera: Scutelleridae) is reported from the United States for the first time based on a specimen collected in Santa Cruz County, Arizona. A key to separate the U.S. species of Orsilochides Kirkaldy is provided. In addition, host plant records and distribution of the other two species of Orsilochides that occur in the U.S., Orsilochides guttata (Herrich-Schäffer) and Orsilochides stictica (Dallas), are analyzed through a combination of digital photo records and museum specimens

A Bayesian Nonparametric Approach to Modeling Motion Patterns

The most difficult—and often most essential— aspect of many interception and tracking tasks is constructing motion models of the targets to be found. Experts can often provide only partial information, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. In contrast, nonparametric models are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides a flexible representation for each individual motion pattern, while the DP assigns observed trajectories to particular motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area

Learning stick-figure models using nonparametric Bayesian priors over trees

We present a probabilistic stick-figure model that uses a nonparametric Bayesian distribution over trees for its structure prior. Sticks are represented by nodes in a tree in such a way that their parameter distributions are probabilistically centered around their parent node. This prior enables the inference procedures to learn multiple explanations for motion-capture data, each of which could be trees of different depth and path lengths. Thus, the algorithm can automatically determine a reasonable distribution over the number of sticks in a given dataset and their hierarchical relationships. We provide experimental results on several motion-capture datasets, demonstrating the model’s ability to recover plausible stick-figure structure, and also the model’s robust behavior when faced with occlusion. Figure 1. Two frames of a stick-figure model learned automatically on a giraffe motion-capture dataset. Marker data with the same color and symbol belong to the same stick. We use a probabilistic stick-figure model that can automatically learn the number of sticks and their relationships in a tree. 1