1,626 research outputs found
Inferring an Indeterminate String from a Prefix Graph
An \itbf{indeterminate string} (or, more simply, just a \itbf{string}) \s{x}
= \s{x}[1..n] on an alphabet is a sequence of nonempty subsets of
. We say that \s{x}[i_1] and \s{x}[i_2] \itbf{match} (written
\s{x}[i_1] \match \s{x}[i_2]) if and only if \s{x}[i_1] \cap \s{x}[i_2] \ne
\emptyset. A \itbf{feasible array} is an array \s{y} = \s{y}[1..n] of
integers such that \s{y}[1] = n and for every , \s{y}[i] \in
0..n\- i\+ 1. A \itbf{prefix table} of a string \s{x} is an array \s{\pi} =
\s{\pi}[1..n] of integers such that, for every , \s{\pi}[i] = j
if and only if \s{x}[i..i\+ j\- 1] is the longest substring at position
of \s{x} that matches a prefix of \s{x}. It is known from \cite{CRSW13} that
every feasible array is a prefix table of some indetermintate string. A
\itbf{prefix graph} \mathcal{P} = \mathcal{P}_{\s{y}} is a labelled simple
graph whose structure is determined by a feasible array \s{y}. In this paper we
show, given a feasible array \s{y}, how to use \mathcal{P}_{\s{y}} to
construct a lexicographically least indeterminate string on a minimum alphabet
whose prefix table \s{\pi} = \s{y}.Comment: 13 pages, 1 figur
New Algorithms and Lower Bounds for Sequential-Access Data Compression
This thesis concerns sequential-access data compression, i.e., by algorithms
that read the input one or more times from beginning to end. In one chapter we
consider adaptive prefix coding, for which we must read the input character by
character, outputting each character's self-delimiting codeword before reading
the next one. We show how to encode and decode each character in constant
worst-case time while producing an encoding whose length is worst-case optimal.
In another chapter we consider one-pass compression with memory bounded in
terms of the alphabet size and context length, and prove a nearly tight
tradeoff between the amount of memory we can use and the quality of the
compression we can achieve. In a third chapter we consider compression in the
read/write streams model, which allows us passes and memory both
polylogarithmic in the size of the input. We first show how to achieve
universal compression using only one pass over one stream. We then show that
one stream is not sufficient for achieving good grammar-based compression.
Finally, we show that two streams are necessary and sufficient for achieving
entropy-only bounds.Comment: draft of PhD thesi
Lossless quantum data compression and variable-length coding
In order to compress quantum messages without loss of information it is
necessary to allow the length of the encoded messages to vary. We develop a
general framework for variable-length quantum messages in close analogy to the
classical case and show that lossless compression is only possible if the
message to be compressed is known to the sender. The lossless compression of an
ensemble of messages is bounded from below by its von-Neumann entropy. We show
that it is possible to reduce the number of qbits passing through a quantum
channel even below the von-Neumann entropy by adding a classical side-channel.
We give an explicit communication protocol that realizes lossless and
instantaneous quantum data compression and apply it to a simple example. This
protocol can be used for both online quantum communication and storage of
quantum data.Comment: 16 pages, 5 figure
Omnibus Sequences, Coupon Collection, and Missing Word Counts
An {\it Omnibus Sequence} of length is one that has each possible
"message" of length embedded in it as a subsequence. We study various
properties of Omnibus Sequences in this paper, making connections, whenever
possible, to the classical coupon collector problem.Comment: 26 page
Image Understanding by Hierarchical Symbolic Representation and Inexact Matching of Attributed Graphs
We study the symbolic representation of imagery information by a powerful global representation scheme in the form of Attributed Relational Graph (ARG), and propose new techniques for the extraction of such representation from spatial-domain images, and for performing the task of image understanding through the analysis of the extracted ARG representation. To achieve practical image understanding tasks, the system needs to comprehend the imagery information in a global form. Therefore, we propose a multi-layer hierarchical scheme for the extraction of global symbolic representation from spatial-domain images. The proposed scheme produces a symbolic mapping of the input data in terms of an output alphabet, whose elements are defined over global subimages. The proposed scheme uses a combination of model-driven and data-driven concepts. The model- driven principle is represented by a graph transducer, which is used to specify the alphabet at each layer in the scheme. A symbolic mapping is driven by the input data to map the input local alphabet into the output global alphabet. Through the iterative application of the symbolic transformational mapping at different levels of hierarchy, the system extracts a global representation from the image in the form of attributed relational graphs. Further processing and interpretation of the imagery information can, then, be performed on their ARG representation. We also propose an efficient approach for calculating a distance measure and finding the best inexact matching configuration between attributed relational graphs. For two ARGs, we define sequences of weighted error-transformations which when performed on one ARG (or a subgraph of it), will produce the other ARG. A distance measure between two ARGs is defined as the weight of the sequence which possesses minimum total-weight. Moreover, this minimum-total weight sequence defines the best inexact matching configuration between the two ARGs. The global minimization over the possible sequences is performed by a dynamic programming technique, the approach shows good results for ARGs of practical sizes. The proposed system possesses the capability to inference the alphabets of the ARG representation which it uses. In the inference phase, the hierarchical scheme is usually driven by the input data only, which normally consist of images of model objects. It extracts the global alphabet of the ARG representation of the models. The extracted model representation is then used in the operation phase of the system to: perform the mapping in the multi-layer scheme. We present our experimental results for utilizing the proposed system for locating objects in complex scenes
- …