16,319 research outputs found
Efficient Analysis of Complex Diagrams using Constraint-Based Parsing
This paper describes substantial advances in the analysis (parsing) of
diagrams using constraint grammars. The addition of set types to the grammar
and spatial indexing of the data make it possible to efficiently parse real
diagrams of substantial complexity. The system is probably the first to
demonstrate efficient diagram parsing using grammars that easily be retargeted
to other domains. The work assumes that the diagrams are available as a flat
collection of graphics primitives: lines, polygons, circles, Bezier curves and
text. This is appropriate for future electronic documents or for vectorized
diagrams converted from scanned images. The classes of diagrams that we have
analyzed include x,y data graphs and genetic diagrams drawn from the biological
literature, as well as finite state automata diagrams (states and arcs). As an
example, parsing a four-part data graph composed of 133 primitives required 35
sec using Macintosh Common Lisp on a Macintosh Quadra 700.Comment: 9 pages, Postscript, no fonts, compressed, uuencoded. Composed in
MSWord 5.1a for the Mac. To appear in ICDAR '95. Other versions at
ftp://ftp.ccs.neu.edu/pub/people/futrell
A Penrose polynomial for embedded graphs
We extend the Penrose polynomial, originally defined only for plane graphs,
to graphs embedded in arbitrary surfaces. Considering this Penrose polynomial
of embedded graphs leads to new identities and relations for the Penrose
polynomial which can not be realized within the class of plane graphs. In
particular, by exploiting connections with the transition polynomial and the
ribbon group action, we find a deletion-contraction-type relation for the
Penrose polynomial. We relate the Penrose polynomial of an orientable
checkerboard colourable graph to the circuit partition polynomial of its medial
graph and use this to find new combinatorial interpretations of the Penrose
polynomial. We also show that the Penrose polynomial of a plane graph G can be
expressed as a sum of chromatic polynomials of twisted duals of G. This allows
us to obtain a new reformulation of the Four Colour Theorem
Non-commutative flux representation for loop quantum gravity
The Hilbert space of loop quantum gravity is usually described in terms of
cylindrical functionals of the gauge connection, the electric fluxes acting as
non-commuting derivation operators. It has long been believed that this
non-commutativity prevents a dual flux (or triad) representation of loop
quantum gravity to exist. We show here, instead, that such a representation can
be explicitly defined, by means of a non-commutative Fourier transform defined
on the loop gravity state space. In this dual representation, flux operators
act by *-multiplication and holonomy operators act by translation. We describe
the gauge invariant dual states and discuss their geometrical meaning. Finally,
we apply the construction to the simpler case of a U(1) gauge group and compare
the resulting flux representation with the triad representation used in loop
quantum cosmology.Comment: 12 pages, matches published versio
(3+1)-dimensional topological phases and self-dual quantum geometries encoded on Heegard surfaces
We apply the recently suggested strategy to lift state spaces and operators
for (2+1)-dimensional topological quantum field theories to state spaces and
operators for a (3+1)-dimensional TQFT with defects. We start from the
(2+1)-dimensional Turaev-Viro theory and obtain a state space, consistent with
the state space expected from the Crane-Yetter model with line defects. This
work has important applications for quantum gravity as well as the theory of
topological phases in (3+1) dimensions. It provides a self-dual quantum
geometry realization based on a vacuum state peaked on a homogeneously curved
geometry. The state spaces and operators we construct here provide also an
improved version of the Walker-Wang model, and simplify its analysis
considerably. We in particular show that the fusion bases of the
(2+1)-dimensional theory lead to a rich set of bases for the (3+1)-dimensional
theory. This includes a quantum deformed spin network basis, which in a loop
quantum gravity context diagonalizes spatial geometry operators. We also obtain
a dual curvature basis, that diagonalizes the Walker-Wang Hamiltonian.
Furthermore, the construction presented here can be generalized to provide
state spaces for the recently introduced dichromatic four-dimensional manifold
invariants.Comment: 27 pages, many figures, v2: minor correction
Associahedra via spines
An associahedron is a polytope whose vertices correspond to triangulations of
a convex polygon and whose edges correspond to flips between them. Using
labeled polygons, C. Hohlweg and C. Lange constructed various realizations of
the associahedron with relevant properties related to the symmetric group and
the classical permutahedron. We introduce the spine of a triangulation as its
dual tree together with a labeling and an orientation. This notion extends the
classical understanding of the associahedron via binary trees, introduces a new
perspective on C. Hohlweg and C. Lange's construction closer to J.-L. Loday's
original approach, and sheds light upon the combinatorial and geometric
properties of the resulting realizations of the associahedron. It also leads to
noteworthy proofs which shorten and simplify previous approaches.Comment: 27 pages, 11 figures. Version 5: minor correction
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference
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