2,880 research outputs found
Optimal-Time Text Indexing in BWT-runs Bounded Space
Indexing highly repetitive texts --- such as genomic databases, software
repositories and versioned text collections --- has become an important problem
since the turn of the millennium. A relevant compressibility measure for
repetitive texts is , the number of runs in their Burrows-Wheeler Transform
(BWT). One of the earliest indexes for repetitive collections, the Run-Length
FM-index, used space and was able to efficiently count the number of
occurrences of a pattern of length in the text (in loglogarithmic time per
pattern symbol, with current techniques). However, it was unable to locate the
positions of those occurrences efficiently within a space bounded in terms of
. Since then, a number of other indexes with space bounded by other measures
of repetitiveness --- the number of phrases in the Lempel-Ziv parse, the size
of the smallest grammar generating the text, the size of the smallest automaton
recognizing the text factors --- have been proposed for efficiently locating,
but not directly counting, the occurrences of a pattern. In this paper we close
this long-standing problem, showing how to extend the Run-Length FM-index so
that it can locate the occurrences efficiently within space (in
loglogarithmic time each), and reaching optimal time within
space, on a RAM machine of bits. Within
space, our index can also count in optimal time .
Raising the space to , we support count and locate in
and time, which is optimal in the
packed setting and had not been obtained before in compressed space. We also
describe a structure using space that replaces the text and
extracts any text substring of length in almost-optimal time
. (...continues...
Indexing Highly Repetitive String Collections
Two decades ago, a breakthrough in indexing string collections made it
possible to represent them within their compressed space while at the same time
offering indexed search functionalities. As this new technology permeated
through applications like bioinformatics, the string collections experienced a
growth that outperforms Moore's Law and challenges our ability of handling them
even in compressed form. It turns out, fortunately, that many of these rapidly
growing string collections are highly repetitive, so that their information
content is orders of magnitude lower than their plain size. The statistical
compression methods used for classical collections, however, are blind to this
repetitiveness, and therefore a new set of techniques has been developed in
order to properly exploit it. The resulting indexes form a new generation of
data structures able to handle the huge repetitive string collections that we
are facing.
In this survey we cover the algorithmic developments that have led to these
data structures. We describe the distinct compression paradigms that have been
used to exploit repetitiveness, the fundamental algorithmic ideas that form the
base of all the existing indexes, and the various structures that have been
proposed, comparing them both in theoretical and practical aspects. We conclude
with the current challenges in this fascinating field
IDENTIFICATION OF COVER SONGS USING INFORMATION THEORETIC MEASURES OF SIMILARITY
13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted versio
Program Similarity Detection with Checksims
In response to growing academic dishonesty in low- level computer science and electrical and computer engineering courses, we present extit{checksims}, a similarity detector designed to highlight suspicious assignments for instructor review. We report the design rationale for the software, and describe our detection of dozens of previously undetected cases of academic dishonesty in previous classes
Fully-Functional Suffix Trees and Optimal Text Searching in BWT-runs Bounded Space
Indexing highly repetitive texts - such as genomic databases, software
repositories and versioned text collections - has become an important problem
since the turn of the millennium. A relevant compressibility measure for
repetitive texts is r, the number of runs in their Burrows-Wheeler Transforms
(BWTs). One of the earliest indexes for repetitive collections, the Run-Length
FM-index, used O(r) space and was able to efficiently count the number of
occurrences of a pattern of length m in the text (in loglogarithmic time per
pattern symbol, with current techniques). However, it was unable to locate the
positions of those occurrences efficiently within a space bounded in terms of
r. In this paper we close this long-standing problem, showing how to extend the
Run-Length FM-index so that it can locate the occ occurrences efficiently
within O(r) space (in loglogarithmic time each), and reaching optimal time, O(m
+ occ), within O(r log log w ({\sigma} + n/r)) space, for a text of length n
over an alphabet of size {\sigma} on a RAM machine with words of w =
{\Omega}(log n) bits. Within that space, our index can also count in optimal
time, O(m). Multiplying the space by O(w/ log {\sigma}), we support count and
locate in O(dm log({\sigma})/we) and O(dm log({\sigma})/we + occ) time, which
is optimal in the packed setting and had not been obtained before in compressed
space. We also describe a structure using O(r log(n/r)) space that replaces the
text and extracts any text substring of length ` in almost-optimal time
O(log(n/r) + ` log({\sigma})/w). Within that space, we similarly provide direct
access to suffix array, inverse suffix array, and longest common prefix array
cells, and extend these capabilities to full suffix tree functionality,
typically in O(log(n/r)) time per operation.Comment: submitted version; optimal count and locate in smaller space: O(r log
log_w(n/r + sigma)
Towards a machine-learning architecture for lexical functional grammar parsing
Data-driven grammar induction aims at producing wide-coverage grammars of human languages. Initial efforts in this field produced relatively shallow linguistic representations such as phrase-structure trees, which only encode constituent structure. Recent work on inducing deep grammars from treebanks addresses this shortcoming by also
recovering non-local dependencies and grammatical relations. My aim is to investigate the issues arising when adapting an existing Lexical Functional Grammar (LFG) induction method to a new language and treebank, and find solutions which will generalize robustly across multiple languages.
The research hypothesis is that by exploiting machine-learning algorithms to learn morphological features, lemmatization classes and grammatical functions from treebanks we can reduce the amount of manual specification and improve robustness, accuracy and domain- and language -independence for LFG parsing systems. Function labels can often be relatively straightforwardly mapped to LFG grammatical functions. Learning them reliably permits grammar induction to depend less on language-specific LFG annotation rules. I therefore propose ways to improve acquisition of function labels from treebanks and translate those improvements into better-quality f-structure parsing.
In a lexicalized grammatical formalism such as LFG a large amount of syntactically relevant information comes from lexical entries. It is, therefore, important to be able
to perform morphological analysis in an accurate and robust way for morphologically rich languages. I propose a fully data-driven supervised method to simultaneously
lemmatize and morphologically analyze text and obtain competitive or improved results on a range of typologically diverse languages
Learning discrete Hidden Markov Models from state distribution vectors
Hidden Markov Models (HMMs) are probabilistic models that have been widely applied to a number of fields since their inception in the late 1960’s. Computational Biology, Image Processing, and Signal Processing, are but a few of the application areas of HMMs. In this dissertation, we develop several new efficient learning algorithms for learning HMM parameters. First, we propose a new polynomial-time algorithm for supervised learning of the parameters of a first order HMM from a state probability distribution (SD) oracle. The SD oracle provides the learner with the state distribution vector corresponding to a query string. We prove the correctness of the algorithm and establish the conditions under which it is guaranteed to construct a model that exactly matches the oracle’s target HMM. We also conduct a simulation experiment to test the viability of the algorithm. Furthermore, the SD oracle is proven to be necessary for polynomial-time learning in the sense that the consistency problem for HMMs, where a training set of state distribution vectors such as those provided by the SD oracle is used but without the ability to query on arbitrary strings, is NP-complete. Next, we define helpful distributions on an instance set of strings for which polynomial-time HMM learning from state distribution vectors is feasible in the absence of an SD oracle and propose a new PAC-learning algorithm under helpful distribution for HMM parameters. The PAC-learning algorithm ensures with high probability that HMM parameters can be learned from training examples without asking queries. Furthermore, we propose a hybrid learning algorithm for approximating HMM parameters from a dataset composed of strings and their corresponding state distribution vectors, and provide supporting experimental data, which indicates our hybrid algorithm produces more accurate approximations than the existing method
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