been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete

be applied to solve several important problems in data streams such as finding quantiles, frequent items, etc. The time and space bounds we show for using the CM sketch to solve these problems significantly improve those previously known — typically from 1/ε 2 to 1/ε in factor. 1

given point in time. Unfortunately, the state spaces of complex processes are very large, making an explicit representation of a belief state intractable. Even in dynamic Bayesian networks (DBNs), where the process itself can be represented compactly, the representation of the belief state

This paper studies the network-level behavior of spammers, including: IP address ranges that send the most spam, common spamming modes (e.g., BGP route hijacking, bots), how persistent across time each spamming host is, and characteristics of spamming botnets. We try to answer these questions

Decentralized decision making under uncertainty has been shown to be intractable when each agent has different partial information about the domain. Thus, improving the applicability and scalability of planning algorithms is an important challenge. We present the first memory-bounded dynamic

RTDP is a recent heuristic-search DP algorithm for solving non-deterministic planning problems with full observability. In relation to other dynamic programming methods, RTDP has two benefits: first, it does not have to evaluate the entire state space in order to deliver an optimal policy

major flaws: either they were not provably real-time, or they imposed large space overheads to meet the real-time bounds. We present a mostly non-moving, dynamically defragmenting collector that overcomes both of these limitations: by avoiding copying in most cases, space requirements are kept low

structure for efficient answering of queries of the form, &quot;Given a 7 ~ F, count (or report) the points of P lying in 7.&quot; Generalizing the simplex range searching techniques, we give a solution with nearly linear space and preprocessing time and with O(n 1- x/b+~) query time, where d < b

structure that maintains a dynamic set of n points in the plane with real coordinates, and supports range selection queries in O((lgn / lg lgn)²) time, as well as insertions and deletions in O((lgn / lg lgn)²) amortized time. The space usage of this data structure is an Θ(lgn / lg lgn) factor improvement

configurations and k-space sampling patterns. Special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density. For this case the feasibility of the proposed methods was verified both in vitro and in vivo. Scan time was reduced to one-half using a two