Mixtures of granular media often exhibit size segregation along the axis of a partially-filled, horizontal, rotating cylinder. Previous experiments have observed axial bands of segregation that grow from concentration fluctuations and merge in a manner analogous to spinodal decomposition. We have observed that a new dynamical state precedes this effect in certain mixtures: bi-directional traveling waves. By preparing initial conditions, we found that the wave speed decreased with wavelength. Such waves appear to be inconsistent with simple PDE models which are first order in time.Comment: 11 page

We have studied the early time evolution of granular segregation patterns in a horizontal rotating cylinder partially filled with a sand/salt mixture. The growth of concentration fluctuations starting from premixed initial conditions was analyzed using Fourier techniques. In one mixture, we observed generally merging dynamics in the segregated bands up to the onset of saturation. At the threshold of saturation, we found a spectrum of wavelengths with a broad peak. The peak position was nearly independent of the tube rotation rate.The overall growth rate of Fourier modes had a maximum at a particular value of the angular rotation frequency. In a slightly different mixture, we observed transient traveling waves when the larger grains were in the majority. We measured the wave speed as a function of several parameters using presegregated initial conditions to launch waves of various wavelengths.

In the course of researching a subject, it is often necessary to submit the same search request to multiple heterogeneous information sources in order to (a) aggregate as much information as possible, and (b) integrate different aspects of the subject into a coherent report. While it is clear that there is value in providing a federated search solution to make dealing with multiple sources less time-consuming, not all organizations aggregate from the same sources, and once the information has been retrieved, not all organizations want them to be integrated in the same way.The Verity Federated Infrastructure addresses this problem by providing a flexible framework for adding new sources and customizing the way in which results are integrated, postprocessed and presented. A new source is made available by writing a Java module called a worker that abides by the search interface of the source.Sources can range from simple information feeds to more complex applications, e.g., CRM systems, relational databases, etc. Workers also perform postprocessing on the results returned by other workers, e.g., to provide uniform scores for results from different sources, filtering, etc. This post-processing enables different results to be integrated into a coherent report. Post-processing is triggered by events that propagate between workers and is done asynchronously in the background while results are being viewed. This ability to do background post-processing allows execution of time-consuming operations that provide substantial value without adversely affecting user experience. Finally, search results are returned and viewed incrementally, which enables searching of peer-to-peer networks via peer workers that we have developed.

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Particle filters are used for hidden state estimation with nonlinear dynamical systems. The inference of 3-d human motion is a natural application, given the nonlinear dynamics of the body and the nonlinear relation between states and image observations. Howevel; the application of particle filters has been limited to cases where the number of state variables is relatively small, because the number of samples needed with high dimensional problems can be prohibitive. We describe a filter that uses hybrid Monte Carlo (HMC) to obtain samples in high dimensional spaces. It uses multiple Markov chains that use posterior gradients to rapidly explore the state space, yielding fair samples from the posterior We find that the HMC filter is several thousand times faster than a conventional particle filter on a 28D people trucking problem.