2,804 research outputs found
Viterbi Training for PCFGs: Hardness Results and Competitiveness of Uniform Initialization
We consider the search for a maximum likelihood assignment of hidden derivations and grammar weights for a probabilistic context-free grammar, the problem approximately solved by “Viterbi training.” We show that solving and even approximating Viterbi training for PCFGs is NP-hard. We motivate the use of uniformat-random initialization for Viterbi EM as an optimal initializer in absence of further information about the correct model parameters, providing an approximate bound on the log-likelihood.
Developing and applying heterogeneous phylogenetic models with XRate
Modeling sequence evolution on phylogenetic trees is a useful technique in
computational biology. Especially powerful are models which take account of the
heterogeneous nature of sequence evolution according to the "grammar" of the
encoded gene features. However, beyond a modest level of model complexity,
manual coding of models becomes prohibitively labor-intensive. We demonstrate,
via a set of case studies, the new built-in model-prototyping capabilities of
XRate (macros and Scheme extensions). These features allow rapid implementation
of phylogenetic models which would have previously been far more
labor-intensive. XRate's new capabilities for lineage-specific models,
ancestral sequence reconstruction, and improved annotation output are also
discussed. XRate's flexible model-specification capabilities and computational
efficiency make it well-suited to developing and prototyping phylogenetic
grammar models. XRate is available as part of the DART software package:
http://biowiki.org/DART .Comment: 34 pages, 3 figures, glossary of XRate model terminolog
TransParsCit: A Transformer-Based Citation Parser Trained on Large-Scale Synthesized Data
Accurately parsing citation strings is key to automatically building large-scale citation graphs, so a robust citation parser is an essential module in academic search engines. One limitation of the state-of-the-art models (such as ParsCit and Neural-ParsCit) is the lack of a large-scale training corpus. Manually annotating hundreds of thousands of citation strings is laborious and time-consuming. This thesis presents a novel transformer-based citation parser by leveraging the GIANT dataset, consisting of 1 billion synthesized citation strings covering over 1500 citation styles. As opposed to handcrafted features, our model benefits from word embeddings and character-based embeddings by combining the bidirectional long shortterm memory (BiLSTM) with the Transformer and Conditional Random Forest (CRF). We varied the training data size from 500 to 1M and investigated the impact of training size on the performance. We evaluated our models on standard CORA benchmark and observed an increase in F1-score as the training size increased. The best performance happened when the training size was around 220K, achieving an F1-score of up to 100% on key citation fields. To our best knowledge, this is the first citation parser trained on a largescale synthesized dataset. Project codes and documentation can be found on this GitHub repository: https://github.com/lamps-lab/Citation-Parser
XRate: a fast prototyping, training and annotation tool for phylo-grammars
BACKGROUND: Recent years have seen the emergence of genome annotation methods based on the phylo-grammar, a probabilistic model combining continuous-time Markov chains and stochastic grammars. Previously, phylo-grammars have required considerable effort to implement, limiting their adoption by computational biologists. RESULTS: We have developed an open source software tool, xrate, for working with reversible, irreversible or parametric substitution models combined with stochastic context-free grammars. xrate efficiently estimates maximum-likelihood parameters and phylogenetic trees using a novel "phylo-EM" algorithm that we describe. The grammar is specified in an external configuration file, allowing users to design new grammars, estimate rate parameters from training data and annotate multiple sequence alignments without the need to recompile code from source. We have used xrate to measure codon substitution rates and predict protein and RNA secondary structures. CONCLUSION: Our results demonstrate that xrate estimates biologically meaningful rates and makes predictions whose accuracy is comparable to that of more specialized tools
On Prediction Using Variable Order Markov Models
This paper is concerned with algorithms for prediction of discrete sequences
over a finite alphabet, using variable order Markov models. The class of such
algorithms is large and in principle includes any lossless compression
algorithm. We focus on six prominent prediction algorithms, including Context
Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic
Suffix Trees (PSTs). We discuss the properties of these algorithms and compare
their performance using real life sequences from three domains: proteins,
English text and music pieces. The comparison is made with respect to
prediction quality as measured by the average log-loss. We also compare
classification algorithms based on these predictors with respect to a number of
large protein classification tasks. Our results indicate that a "decomposed"
CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in
sequence prediction tasks. Somewhat surprisingly, a different algorithm, which
is a modification of the Lempel-Ziv compression algorithm, significantly
outperforms all algorithms on the protein classification problems
Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope
In this work we present the approach taken to address the problems anomalous fault detection and system delays experienced by the MeerLICHT telescope. We make use of the abundantly available console logs, that record all aspects of the telescope's function, to obtain information. The MeerLICHT operational team must devote time to manually inspecting the logs during system downtime to discover faults. This task is laborious, time inefficient given the large size of the logs, and does not suit the time-sensitive nature of many of the surveys the telescope partakes in. We used the novel approach of the Hidden Markov model, to address the problems of fault detection and system delays experienced by the MeerLICHT. We were able to train the model in three separate ways, showing some success at fault detection and none at the addressing the system delays
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