. Using both a real-world deployment and simulation, we show that Deluge can reliably disseminate data to all nodes and characterize its overall performance. On Mica2dot nodes, Deluge can push nearly 90 bytes/second, oneninth the maximum transmission rate of the radio supported under TinyOS. Control

learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.

the ability to use high-dimensional feature spaces, and from their strong theoretical guarantees. However, many real-world tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernel-based methods ignore structure in the problem, assigning labels

, and a practical design for location-hidden services via rendezvous points. Tor works on the real-world Internet, requires no special privileges or kernel modifications, requires little synchronization or coordination between nodes, and provides a reasonable tradeoff between anonymity, usability

with before the theory can be fully applied to real-world job design problems. First, means must be developed for measuring current levels of activation of individuals in actual work settings (cf. Thayer, 1967), and for assessing the "optimal level" of activation for different individuals. Until

with the results of applying CUTE to real-world examples of C code.

to call real-time a program or system that receives external interrupts or reads sensors connected to the physical world and outputs commands to it. Real-time programming is an essential industrial activ-

This paper examines whether temporal difference methods for training connectionist networks, such as Suttons's TD(lambda) algorithm can be successfully applied to complex real-world problems. A number of important practical issues are identified and discussed from a general theoretical

present in real-world programs, and describe how to effectively capture and exploit it in order to perform load value prediction. Temporal and spatial locality are attributes of storage locations, and describe the future likelihood of references to those locations or their close neighbors. In a similar

of the observations. Prioritized Sweeping aims for the best of both worlds. It uses all previous experiences both to prioritize important dynamic programming sweeps and to guide the exploration of state-space. We compare Prioritized Sweeping with other reinforcement learning schemes for a number of di erent