to a compact representation of objects so that similarity of objects can be estimated from their compact sketches, and also leads to efficient algorithms for approximate nearest neighbor search and clustering. Min-wise independent permutations provide an elegant construction of such a locality

heuristics from Strips encodings. They differ from specialized problem solvers such as those developed for the 24-Puzzle and Rubik's Cube in that they use a general declarative language for stating problems and a general mechanism for extracting heuristics from these representations. In this paper, we

redundantly encode structured representations as content vectors, whose dot product yields an estimate of how well the corresponding structural representations will match. The second stage (FAC) uses SME to compute a true structural match between the probe and output from the first stage. MAC/FAC has been

We formalize weighted dependency parsing as searching for maximum spanning trees (MSTs) in directed graphs. Using this representation, the parsing algorithm of Eisner (1996) is sufficient for searching over all projective trees in O(n 3) time. More surprisingly, the representation is extended

is an effective treatment. Physiological activation and habituation within and across exposure ession are cited as indicators ofemotional processing, and variables that influence activation and habituation of fear responses are examined. These variables and theindicators are analyzed to yield an account of what

of pixel-based representations is an important task for image-based rendering. Our example-based super-resolution algorithm yields Fig. 1 (h, i). Previous Work: Researchers have long studied image interpolation, although only recently using machine learning or sampling approaches, which offer much power

We first exhibit in the commutative case the simple algebraic relations between the algebra of functions on a manifold and its infinitesimal length element ds. Its unitary representations correspond to Riemannian metrics and Spin structure while ds is the Dirac propagator ds = ×— × = D −1 where D

criteria" to any situation where it appears that media or attributes of media have been instrumental in fostering learning gains: We need to ask whether there are other media or another set of media attributes that would yield similar learning gains. The question is critical because if different media

We present a formulation for recursive recovery of motion, pointwise structure, and focal length from feature corre-spondences tracked through an image sequence. In addition to adding focal length to the state vector, several representational improvements are made over earlier structure from

with accompanying displacement maps. This choice of representation yields a coarse but efficient model suitable for animation and a fine but more expensive model suitable for rendering. The first step in our process consists of interactively painting patch boundaries over a rendering of the mesh. In many