633,124 research outputs found
Sporadicity and synchronization in one-dimensional asymmetrically coupled maps
A one-dimensional chain of sporadic maps with asymmetric nearest neighbour
couplings is numerically studied. It is shown that in the region of strong
asymmetry the system becomes spatially fully synchronized, even in the
thermodinamic limit, while the Lyapunov exponent is zero. For weak asymmetry
the synchronization is no more complete, and the Lyapunov exponent becomes
positive. In addition one has a clear relation between temporal and spatial
chaos, {\it i.e.}: a positive effective Lyapunov exponent corresponds to a lack
of synchronization and {\it vice versa}Comment: 9 pages + 3 figures (postscript appended uuencoded tar), IOP style
(appended uuencoded compress
Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction
Temporal relation prediction in incomplete temporal knowledge graphs (TKGs)
is a popular temporal knowledge graph completion (TKGC) problem in both
transductive and inductive settings. Traditional embedding-based TKGC models
(TKGE) rely on structured connections and can only handle a fixed set of
entities, i.e., the transductive setting. In the inductive setting where test
TKGs contain emerging entities, the latest methods are based on symbolic rules
or pre-trained language models (PLMs). However, they suffer from being
inflexible and not time-specific, respectively. In this work, we extend the
fully-inductive setting, where entities in the training and test sets are
totally disjoint, into TKGs and take a further step towards a more flexible and
time-sensitive temporal relation prediction approach SST-BERT, incorporating
Structured Sentences with Time-enhanced BERT. Our model can obtain the entity
history and implicitly learn rules in the semantic space by encoding structured
sentences, solving the problem of inflexibility. We propose to use a time
masking MLM task to pre-train BERT in a corpus rich in temporal tokens
specially generated for TKGs, enhancing the time sensitivity of SST-BERT. To
compute the probability of occurrence of a target quadruple, we aggregate all
its structured sentences from both temporal and semantic perspectives into a
score. Experiments on the transductive datasets and newly generated
fully-inductive benchmarks show that SST-BERT successfully improves over
state-of-the-art baselines
Occurrence and persistence of magnetic elements in the quiet Sun
Turbulent convection efficiently transports energy up to the solar
photosphere, but its multi-scale nature and dynamic properties are still not
fully understood. Several works in the literature have investigated the
emergence of patterns of convective and magnetic nature in the quiet Sun at
spatial and temporal scales from granular to global. Aims. To shed light on the
scales of organisation at which turbulent convection operates, and its
relationship with the magnetic flux therein, we studied characteristic spatial
and temporal scales of magnetic features in the quiet Sun. Methods. Thanks to
an unprecedented data set entirely enclosing a supergranule, occurrence and
persistence analysis of magnetogram time series were used to detect spatial and
long-lived temporal correlations in the quiet Sun and to investigate their
nature. Results. A relation between occurrence and persistence representative
for the quiet Sun was found. In particular, highly recurrent and persistent
patterns were detected especially in the boundary of the supergranular cell.
These are due to moving magnetic elements undergoing motion that behaves like a
random walk together with longer decorrelations ( h) with respect to
regions inside the supergranule. In the vertices of the supegranular cell the
maximum observed occurrence is not associated with the maximum persistence,
suggesting that there are different dynamic regimes affecting the magnetic
elements
Markov Properties of Electrical Discharge Current Fluctuations in Plasma
Using the Markovian method, we study the stochastic nature of electrical
discharge current fluctuations in the Helium plasma. Sinusoidal trends are
extracted from the data set by the Fourier-Detrended Fluctuation analysis and
consequently cleaned data is retrieved. We determine the Markov time scale of
the detrended data set by using likelihood analysis. We also estimate the
Kramers-Moyal's coefficients of the discharge current fluctuations and derive
the corresponding Fokker-Planck equation. In addition, the obtained Langevin
equation enables us to reconstruct discharge time series with similar
statistical properties compared with the observed in the experiment. We also
provide an exact decomposition of temporal correlation function by using
Kramers-Moyal's coefficients. We show that for the stationary time series, the
two point temporal correlation function has an exponential decaying behavior
with a characteristic correlation time scale. Our results confirm that, there
is no definite relation between correlation and Markov time scales. However
both of them behave as monotonic increasing function of discharge current
intensity. Finally to complete our analysis, the multifractal behavior of
reconstructed time series using its Keramers-Moyal's coefficients and original
data set are investigated. Extended self similarity analysis demonstrates that
fluctuations in our experimental setup deviates from Kolmogorov (K41) theory
for fully developed turbulence regime.Comment: 25 pages, 9 figures and 4 tables. V3: Added comments, references,
figures and major correction
Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields
Automated Facial Expression Recognition (FER) has been a challenging task for
decades. Many of the existing works use hand-crafted features such as LBP, HOG,
LPQ, and Histogram of Optical Flow (HOF) combined with classifiers such as
Support Vector Machines for expression recognition. These methods often require
rigorous hyperparameter tuning to achieve good results. Recently Deep Neural
Networks (DNN) have shown to outperform traditional methods in visual object
recognition. In this paper, we propose a two-part network consisting of a
DNN-based architecture followed by a Conditional Random Field (CRF) module for
facial expression recognition in videos. The first part captures the spatial
relation within facial images using convolutional layers followed by three
Inception-ResNet modules and two fully-connected layers. To capture the
temporal relation between the image frames, we use linear chain CRF in the
second part of our network. We evaluate our proposed network on three publicly
available databases, viz. CK+, MMI, and FERA. Experiments are performed in
subject-independent and cross-database manners. Our experimental results show
that cascading the deep network architecture with the CRF module considerably
increases the recognition of facial expressions in videos and in particular it
outperforms the state-of-the-art methods in the cross-database experiments and
yields comparable results in the subject-independent experiments.Comment: To appear in 12th IEEE Conference on Automatic Face and Gesture
Recognition Worksho
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