models: a decentralized label model, label polymorphism, run-time label checking, and automatic label inference. JFlow also supports many language features that have never been integrated successfully with static information flow control, including objects, subclassing, dynamic type tests, access control

and run-time mechanisms that support mobility, and techniques for implementing mobility that do not degrade the performance of local operations. Performance measurements of the current implementation are included.

Cilk (pronounced "silk") is a C-based runtime system for multithreaded parallel programming. In this paper, we document the efficiency of the Cilk work-stealing scheduler, both empirically and analytically. We show that on real and synthetic applications, the "work" and "

Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake

-rst " principle has led to a portable Cilk-5 im-plementation in which the typical cost of spawning a parallel thread is only between 2 and 6 times the cost of a C function call on a variety of contemporary machines. Many Cilk pro-grams run on one processor with virtually no degradation compared

repeatedly in an atomic block), (3) we use runtime filtering to detect duplicate log entries that are missed statically, and (4) we present a series of GC-time techniques to compact the logs generated by long-running atomic blocks. Our implementation supports short-running scalable concurrent benchmarks

quickly to changes in the scene which enables very sensitive detection of moving targets. We also show how the model can use color information to suppress detec-tion of shadows. The implementation of the model runs in real-time for both gray level and color imagery. Evaluation shows that this approach

The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have: (1) the advantage that they do not require a

enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced

, or the simplest, or both. A randomized algorithm is an algorithm that uses random numbers to influence the choices it makes in the course of its computation. Thus its behavior (typically quantified as running time or quality of output) varies from