12,053 research outputs found
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
Program Synthesis using Natural Language
Interacting with computers is a ubiquitous activity for millions of people.
Repetitive or specialized tasks often require creation of small, often one-off,
programs. End-users struggle with learning and using the myriad of
domain-specific languages (DSLs) to effectively accomplish these tasks.
We present a general framework for constructing program synthesizers that
take natural language (NL) inputs and produce expressions in a target DSL. The
framework takes as input a DSL definition and training data consisting of
NL/DSL pairs. From these it constructs a synthesizer by learning optimal
weights and classifiers (using NLP features) that rank the outputs of a
keyword-programming based translation. We applied our framework to three
domains: repetitive text editing, an intelligent tutoring system, and flight
information queries. On 1200+ English descriptions, the respective synthesizers
rank the desired program as the top-1 and top-3 for 80% and 90% descriptions
respectively
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
A simple online competitive adaptation of Lempel-Ziv compression with efficient random access support
We present a simple adaptation of the Lempel Ziv 78' (LZ78) compression
scheme ({\em IEEE Transactions on Information Theory, 1978}) that supports
efficient random access to the input string. Namely, given query access to the
compressed string, it is possible to efficiently recover any symbol of the
input string. The compression algorithm is given as input a parameter \eps
>0, and with very high probability increases the length of the compressed
string by at most a factor of (1+\eps). The access time is O(\log n +
1/\eps^2) in expectation, and O(\log n/\eps^2) with high probability. The
scheme relies on sparse transitive-closure spanners. Any (consecutive)
substring of the input string can be retrieved at an additional additive cost
in the running time of the length of the substring. We also formally establish
the necessity of modifying LZ78 so as to allow efficient random access.
Specifically, we construct a family of strings for which
queries to the LZ78-compressed string are required in order to recover a single
symbol in the input string. The main benefit of the proposed scheme is that it
preserves the online nature and simplicity of LZ78, and that for {\em every}
input string, the length of the compressed string is only a small factor larger
than that obtained by running LZ78
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