10,659 research outputs found
Spontaneous Subtle Expression Detection and Recognition based on Facial Strain
Optical strain is an extension of optical flow that is capable of quantifying
subtle changes on faces and representing the minute facial motion intensities
at the pixel level. This is computationally essential for the relatively new
field of spontaneous micro-expression, where subtle expressions can be
technically challenging to pinpoint. In this paper, we present a novel method
for detecting and recognizing micro-expressions by utilizing facial optical
strain magnitudes to construct optical strain features and optical strain
weighted features. The two sets of features are then concatenated to form the
resultant feature histogram. Experiments were performed on the CASME II and
SMIC databases. We demonstrate on both databases, the usefulness of optical
strain information and more importantly, that our best approaches are able to
outperform the original baseline results for both detection and recognition
tasks. A comparison of the proposed method with other existing spatio-temporal
feature extraction approaches is also presented.Comment: 21 pages (including references), single column format, accepted to
Signal Processing: Image Communication journa
Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks
We investigate the relationship of resting-state fMRI functional connectivity
estimated over long periods of time with time-varying functional connectivity
estimated over shorter time intervals. We show that using Pearson's correlation
to estimate functional connectivity implies that the range of fluctuations of
functional connections over short time scales is subject to statistical
constraints imposed by their connectivity strength over longer scales. We
present a method for estimating time-varying functional connectivity that is
designed to mitigate this issue and allows us to identify episodes where
functional connections are unexpectedly strong or weak. We apply this method to
data recorded from participants, and show that the number of
unexpectedly strong/weak connections fluctuates over time, and that these
variations coincide with intermittent periods of high and low modularity in
time-varying functional connectivity. We also find that during periods of
relative quiescence regions associated with default mode network tend to join
communities with attentional, control, and primary sensory systems. In
contrast, during periods where many connections are unexpectedly strong/weak,
default mode regions dissociate and form distinct modules. Finally, we go on to
show that, while all functional connections can at times manifest stronger
(more positively correlated) or weaker (more negatively correlated) than
expected, a small number of connections, mostly within the visual and
somatomotor networks, do so a disproportional number of times. Our statistical
approach allows the detection of functional connections that fluctuate more or
less than expected based on their long-time averages and may be of use in
future studies characterizing the spatio-temporal patterns of time-varying
functional connectivityComment: 47 Pages, 8 Figures, 4 Supplementary Figure
Predictability and hierarchy in Drosophila behavior
Even the simplest of animals exhibit behavioral sequences with complex
temporal dynamics. Prominent amongst the proposed organizing principles for
these dynamics has been the idea of a hierarchy, wherein the movements an
animal makes can be understood as a set of nested sub-clusters. Although this
type of organization holds potential advantages in terms of motion control and
neural circuitry, measurements demonstrating this for an animal's entire
behavioral repertoire have been limited in scope and temporal complexity. Here,
we use a recently developed unsupervised technique to discover and track the
occurrence of all stereotyped behaviors performed by fruit flies moving in a
shallow arena. Calculating the optimally predictive representation of the fly's
future behaviors, we show that fly behavior exhibits multiple time scales and
is organized into a hierarchical structure that is indicative of its underlying
behavioral programs and its changing internal states
Internal Robustness: systematic search for systematic bias in SN Ia data
A great deal of effort is currently being devoted to understanding,
estimating and removing systematic errors in cosmological data. In the
particular case of type Ia supernovae, systematics are starting to dominate the
error budget. Here we propose a Bayesian tool for carrying out a systematic
search for systematic contamination. This serves as an extension to the
standard goodness-of-fit tests and allows not only to cross-check raw or
processed data for the presence of systematics but also to pin-point the data
that are most likely contaminated. We successfully test our tool with mock
catalogues and conclude that the Union2.1 data do not possess a significant
amount of systematics. Finally, we show that if one includes in Union2.1 the
supernovae that originally failed the quality cuts, our tool signals the
presence of systematics at over 3.8-sigma confidence level.Comment: 14 pages, 15 figures; matches version accepted for publication in
MNRA
- …