Abstract. Optical circuit-switched networks such as National LambdaRail (NLR) offer dedicated bandwidth to support large-scale bulk data transfer. Though a dedicated circuit-switched network eliminates congestion from the network itself, it effectively "pushes" the congestion to the end hosts, resulting in lower-than-expected throughput. Previous approaches either use an ad-hoc proactive approach that does not generalize well or a sluggish reactive approach where the sending rate is only adapted based on synchronous feedback from the receiver. We address the shortcomings of such approaches using a two-step process. First, we improve the adaptivity of the reactive approach by proposing an asynchronous, fine-grained, rate-based approach. While this approach enhances performance, its limitation is that it is still reactive. Consequently, we then analyze the predictive patterns of load on the receiver and provide strong evidence that a proactive approach is not only possible, but also necessary, to achieve the best performance in dynamically varying end-host conditions.

Motion capture can produce high quality data for motion generation.However, that professional motion capture is expensive, and imposes restrictions on the capturing environment. We propose a motion estimation framework that utilizes a small set of low-cost, 3D acceleration sensors. We use a data-driven approach to synthesize realistic human motion comparable in quality to the motion captured by the professional motion capture systems. We employ eight 3D accelerometers -four Nintendo c Wii controllers -attached to a performer's body to capture motion data. The collected data is used to synthesize high quality motion from a statistical model learned from a high quality motion capture database. The proposed system is inexpensive and is easy to setup.

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