16,067 research outputs found
From Regular Expression Matching to Parsing
Given a regular expression and a string , the regular expression
parsing problem is to determine if matches and if so, determine how it
matches, e.g., by a mapping of the characters of to the characters in .
Regular expression parsing makes finding matches of a regular expression even
more useful by allowing us to directly extract subpatterns of the match, e.g.,
for extracting IP-addresses from internet traffic analysis or extracting
subparts of genomes from genetic data bases. We present a new general
techniques for efficiently converting a large class of algorithms that
determine if a string matches regular expression into algorithms that
can construct a corresponding mapping. As a consequence, we obtain the first
efficient linear space solutions for regular expression parsing
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Attentional capture by meaning: A multi-level modelling study
We present a computational study of attentional capture by meaning, based on Barnard et al's key-distractor attentional blink task. We highlight a sequence of models, from an abstract black-box to a structurally detailed white-box model. Each of these models reproduces the major findings from the key-distractor blink task. We argue that such multi-level modelling gives greater confidence in the theoretical position encapsulated by these models
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Development of a method for screening short-lived proteins using green fluorescent protein.
We have developed a screening technology for the identification of short-lived proteins. A green fluorescent protein (GFP)-fusion cDNA library was generated for monitoring degradation kinetics. Cells expressing a subset of the GFP-cDNA expression library were screened to recover those in which the fluorescence signal diminished rapidly when protein synthesis was inhibited. Thirty clones that met the screening criteria were characterized individually. Twenty-three (73%) proved to have a half-life of 4 hours or less
Learning a Pose Lexicon for Semantic Action Recognition
This paper presents a novel method for learning a pose lexicon comprising
semantic poses defined by textual instructions and their associated visual
poses defined by visual features. The proposed method simultaneously takes two
input streams, semantic poses and visual pose candidates, and statistically
learns a mapping between them to construct the lexicon. With the learned
lexicon, action recognition can be cast as the problem of finding the maximum
translation probability of a sequence of semantic poses given a stream of
visual pose candidates. Experiments evaluating pre-trained and zero-shot action
recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets
were used to verify the efficacy of the proposed method.Comment: Accepted by the 2016 IEEE International Conference on Multimedia and
Expo (ICME 2016). 6 pages paper and 4 pages supplementary materia
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
This paper presents a novel unsupervised domain adaptation method for
cross-domain visual recognition. We propose a unified framework that reduces
the shift between domains both statistically and geometrically, referred to as
Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two
coupled projections that project the source domain and target domain data into
low dimensional subspaces where the geometrical shift and distribution shift
are reduced simultaneously. The objective function can be solved efficiently in
a closed form. Extensive experiments have verified that the proposed method
significantly outperforms several state-of-the-art domain adaptation methods on
a synthetic dataset and three different real world cross-domain visual
recognition tasks
Unsupervised Domain Adaptation: A Multi-task Learning-based Method
This paper presents a novel multi-task learning-based method for unsupervised
domain adaptation. Specifically, the source and target domain classifiers are
jointly learned by considering the geometry of target domain and the divergence
between the source and target domains based on the concept of multi-task
learning. Two novel algorithms are proposed upon the method using Regularized
Least Squares and Support Vector Machines respectively. Experiments on both
synthetic and real world cross domain recognition tasks have shown that the
proposed methods outperform several state-of-the-art domain adaptation methods
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