13,932 research outputs found
Unified Analysis of Collapsible and Ordered Pushdown Automata via Term Rewriting
We model collapsible and ordered pushdown systems with term rewriting, by
encoding higher-order stacks and multiple stacks into trees. We show a uniform
inverse preservation of recognizability result for the resulting class of term
rewriting systems, which is obtained by extending the classic saturation-based
approach. This result subsumes and unifies similar analyses on collapsible and
ordered pushdown systems. Despite the rich literature on inverse preservation
of recognizability for term rewrite systems, our result does not seem to follow
from any previous study.Comment: in Proc. of FRE
Efficient chaining of seeds in ordered trees
We consider here the problem of chaining seeds in ordered trees. Seeds are
mappings between two trees Q and T and a chain is a subset of non overlapping
seeds that is consistent with respect to postfix order and ancestrality. This
problem is a natural extension of a similar problem for sequences, and has
applications in computational biology, such as mining a database of RNA
secondary structures. For the chaining problem with a set of m constant size
seeds, we describe an algorithm with complexity O(m2 log(m)) in time and O(m2)
in space
Cross-lingual RST Discourse Parsing
Discourse parsing is an integral part of understanding information flow and
argumentative structure in documents. Most previous research has focused on
inducing and evaluating models from the English RST Discourse Treebank.
However, discourse treebanks for other languages exist, including Spanish,
German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same
underlying linguistic theory, but differ slightly in the way documents are
annotated. In this paper, we present (a) a new discourse parser which is
simpler, yet competitive (significantly better on 2/3 metrics) to state of the
art for English, (b) a harmonization of discourse treebanks across languages,
enabling us to present (c) what to the best of our knowledge are the first
experiments on cross-lingual discourse parsing.Comment: To be published in EACL 2017, 13 page
Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze Classification
Accurate, robust, inexpensive gaze tracking in the car can help keep a driver
safe by facilitating the more effective study of how to improve (1) vehicle
interfaces and (2) the design of future Advanced Driver Assistance Systems. In
this paper, we estimate head pose and eye pose from monocular video using
methods developed extensively in prior work and ask two new interesting
questions. First, how much better can we classify driver gaze using head and
eye pose versus just using head pose? Second, are there individual-specific
gaze strategies that strongly correlate with how much gaze classification
improves with the addition of eye pose information? We answer these questions
by evaluating data drawn from an on-road study of 40 drivers. The main insight
of the paper is conveyed through the analogy of an "owl" and "lizard" which
describes the degree to which the eyes and the head move when shifting gaze.
When the head moves a lot ("owl"), not much classification improvement is
attained by estimating eye pose on top of head pose. On the other hand, when
the head stays still and only the eyes move ("lizard"), classification accuracy
increases significantly from adding in eye pose. We characterize how that
accuracy varies between people, gaze strategies, and gaze regions.Comment: Accepted for Publication in IET Computer Vision. arXiv admin note:
text overlap with arXiv:1507.0476
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