1,500,819 research outputs found
Algorithms for Analysing the Temporal Structure of Discourse
We describe a method for analysing the temporal structure of a discourse
which takes into account the effects of tense, aspect, temporal adverbials and
rhetorical structure and which minimises unnecessary ambiguity in the temporal
structure. It is part of a discourse grammar implemented in Carpenter's ALE
formalism. The method for building up the temporal structure of the discourse
combines constraints and preferences: we use constraints to reduce the number
of possible structures, exploiting the HPSG type hierarchy and unification for
this purpose; and we apply preferences to choose between the remaining options
using a temporal centering mechanism. We end by recommending that an
underspecified representation of the structure using these techniques be used
to avoid generating the temporal/rhetorical structure until higher-level
information can be used to disambiguate.Comment: EACL '95, 8 pages, 1 eps picture, tar-ed, compressed, uuencoded, uses
eaclap.sty, a4wide.sty, epsf.te
Pauses and the temporal structure of speech
Natural-sounding speech synthesis requires close control over the temporal structure of the speech flow. This includes a full predictive scheme for the durational structure and in particuliar the prolongation of final syllables of lexemes as well as for the pausal structure in the utterance. In this chapter, a description of the temporal structure and the summary of the numerous factors that modify it are presented. In the second part, predictive schemes for the temporal structure of speech ("performance structures") are introduced, and their potential for characterising the overall prosodic structure of speech is demonstrated
Exploring Temporal Networks with Greedy Walks
Temporal networks come with a wide variety of heterogeneities, from
burstiness of event sequences to correlations between timings of node and link
activations. In this paper, we set to explore the latter by using greedy walks
as probes of temporal network structure. Given a temporal network (a sequence
of contacts), greedy walks proceed from node to node by always following the
first available contact. Because of this, their structure is particularly
sensitive to temporal-topological patterns involving repeated contacts between
sets of nodes. This becomes evident in their small coverage per step as
compared to a temporal reference model -- in empirical temporal networks,
greedy walks often get stuck within small sets of nodes because of correlated
contact patterns. While this may also happen in static networks that have
pronounced community structure, the use of the temporal reference model takes
the underlying static network structure out of the equation and indicates that
there is a purely temporal reason for the observations. Further analysis of the
structure of greedy walks indicates that burst trains, sequences of repeated
contacts between node pairs, are the dominant factor. However, there are larger
patterns too, as shown with non-backtracking greedy walks. We proceed further
to study the entropy rates of greedy walks, and show that the sequences of
visited nodes are more structured and predictable in original data as compared
to temporally uncorrelated references. Taken together, these results indicate a
richness of correlated temporal-topological patterns in temporal networks
Mathematical structure of the temporal gauge
The mathematical structure of the temporal gauge of QED is critically
examined in both the alternative formulations characterized by either
positivity or regularity of the Weyl algebra. The conflict between time
translation invariance and Gauss law constraint is shown to lead to peculiar
features. In the positive case only the correlations of exponentials of fields
exist (non regularity), the space translations are not strongly continuous, so
that their generators do not exist, a theta vacuum degeneracy occurs,
associated to a spontaneous symmetry breaking. In the indefinite case the
spectral condition only holds in terms of positivity of the energy, gauge
invariant theta-vacua exist on the observables, with no extension to time
translation invariant states on the field algebra, the vacuum is faithful on
the longitudinal algebra and a KMS structure emerges. Functional integral
representations are derived in both cases, with the alternative between ergodic
measures on real random fields or complex Gaussian random fields.Comment: Late
Describing Videos by Exploiting Temporal Structure
Recent progress in using recurrent neural networks (RNNs) for image
description has motivated the exploration of their application for video
description. However, while images are static, working with videos requires
modeling their dynamic temporal structure and then properly integrating that
information into a natural language description. In this context, we propose an
approach that successfully takes into account both the local and global
temporal structure of videos to produce descriptions. First, our approach
incorporates a spatial temporal 3-D convolutional neural network (3-D CNN)
representation of the short temporal dynamics. The 3-D CNN representation is
trained on video action recognition tasks, so as to produce a representation
that is tuned to human motion and behavior. Second we propose a temporal
attention mechanism that allows to go beyond local temporal modeling and learns
to automatically select the most relevant temporal segments given the
text-generating RNN. Our approach exceeds the current state-of-art for both
BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on
a new, larger and more challenging dataset of paired video and natural language
descriptions.Comment: Accepted to ICCV15. This version comes with code release and
supplementary materia
Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach
The increasing availability of temporal network data is calling for more
research on extracting and characterizing mesoscopic structures in temporal
networks and on relating such structure to specific functions or properties of
the system. An outstanding challenge is the extension of the results achieved
for static networks to time-varying networks, where the topological structure
of the system and the temporal activity patterns of its components are
intertwined. Here we investigate the use of a latent factor decomposition
technique, non-negative tensor factorization, to extract the community-activity
structure of temporal networks. The method is intrinsically temporal and allows
to simultaneously identify communities and to track their activity over time.
We represent the time-varying adjacency matrix of a temporal network as a
three-way tensor and approximate this tensor as a sum of terms that can be
interpreted as communities of nodes with an associated activity time series. We
summarize known computational techniques for tensor decomposition and discuss
some quality metrics that can be used to tune the complexity of the factorized
representation. We subsequently apply tensor factorization to a temporal
network for which a ground truth is available for both the community structure
and the temporal activity patterns. The data we use describe the social
interactions of students in a school, the associations between students and
school classes, and the spatio-temporal trajectories of students over time. We
show that non-negative tensor factorization is capable of recovering the class
structure with high accuracy. In particular, the extracted tensor components
can be validated either as known school classes, or in terms of correlated
activity patterns, i.e., of spatial and temporal coincidences that are
determined by the known school activity schedule
VideoGraph: Recognizing Minutes-Long Human Activities in Videos
Many human activities take minutes to unfold. To represent them, related
works opt for statistical pooling, which neglects the temporal structure.
Others opt for convolutional methods, as CNN and Non-Local. While successful in
learning temporal concepts, they are short of modeling minutes-long temporal
dependencies. We propose VideoGraph, a method to achieve the best of two
worlds: represent minutes-long human activities and learn their underlying
temporal structure. VideoGraph learns a graph-based representation for human
activities. The graph, its nodes and edges are learned entirely from video
datasets, making VideoGraph applicable to problems without node-level
annotation. The result is improvements over related works on benchmarks:
Epic-Kitchen and Breakfast. Besides, we demonstrate that VideoGraph is able to
learn the temporal structure of human activities in minutes-long videos
Time as a guide to cause
How do people learn causal structure? In two studies we investigated
the interplay between temporal order, intervention and covariational cues. In
Study 1 temporal order overrode covariation information, leading to spurious
causal inferences when the temporal cues were misleading. In Study 2 both
temporal order and intervention contributed to accurate causal inference, well
beyond that achievable through covariational data alone. Together the studies
show that people use both temporal order and interventional cues to infer
causal structure, and that these cues dominate the available statistical
information. We endorse a hypothesis-driven account of learning, whereby
people use cues such as temporal order to generate initial models, and then
test these models against the incoming covariational data
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