14,154 research outputs found
Radiance and Doppler shift distributions across the network of the quiet Sun
The radiance and Doppler-shift distributions across the solar network provide
observational constraints of two-dimensional modeling of transition-region
emission and flows in coronal funnels. Two different methods, dispersion plots
and average-profile studies, were applied to investigate these distributions.
In the dispersion plots, we divided the entire scanned region into a bright and
a dark part according to an image of Fe xii; we plotted intensities and Doppler
shifts in each bin as determined according to a filtered intensity of Si ii. We
also studied the difference in height variations of the magnetic field as
extrapolated from the MDI magnetogram, in and outside network. For the
average-profile study, we selected 74 individual cases and derived the average
profiles of intensities and Doppler shifts across the network. The dispersion
plots reveal that the intensities of Si ii and C iv increase from network
boundary to network center in both parts. However, the intensity of Ne viii
shows different trends, namely increasing in the bright part and decreasing in
the dark part. In both parts, the Doppler shift of C iv increases steadily from
internetwork to network center. The average-profile study reveals that the
intensities of the three lines all decline from the network center to
internetwork region. The binned intensities of Si ii and Ne viii have a good
correlation. We also find that the large blue shift of Ne viii does not
coincide with large red shift of C iv. Our results suggest that the network
structure is still prominent at the layer where Ne viii is formed in the quiet
Sun, and that the magnetic structures expand more strongly in the dark part
than in the bright part of this quiet Sun region.Comment: 10 pages,9 figure
Upflows in the upper transition region of the quiet Sun
We investigate the physical meaning of the prominent blue shifts of Ne VIII,
which is observed to be associated with quiet-Sun network junctions (boundary
intersections), through data analyses combining force-free-field extrapolations
with EUV spectroscopic observations. For a middle-latitude region, we
reconstruct the magnetic funnel structure in a sub-region showing faint
emission in EIT-Fe 195. This funnel appears to consist of several smaller
funnels that originate from network lanes, expand with height and finally merge
into a single wide open-field region. However, the large blue shifts of Ne VIII
are generally not associated with open fields, but seem to be associated with
the legs of closed magnetic loops. Moreover, in most cases significant upflows
are found in both of the funnel-shaped loop legs. These quasi-steady upflows
are regarded as signatures of mass supply to the coronal loops rather than the
solar wind. Our observational result also reveals that in many cases the
upflows in the upper transition region (TR) and the downflows in the middle TR
are not fully cospatial. Based on these new observational results, we suggest
different TR structures in coronal holes and in the quiet Sun.Comment: 4 pages, 4 figures, will appear in the Proceedings of the Solar wind
12 conferenc
Bounds of Efficiency at Maximum Power for Normal-, Sub- and Super-Dissipative Carnot-Like Heat Engines
The Carnot-like heat engines are classified into three types (normal-, sub-
and super-dissipative) according to relations between the minimum irreversible
entropy production in the "isothermal" processes and the time for completing
those processes. The efficiencies at maximum power of normal-, sub- and
super-dissipative Carnot-like heat engines are proved to be bounded between
and , and , 0 and
, respectively. These bounds are also shared by linear, sub-
and super-linear irreversible Carnot-like engines [Tu and Wang, Europhys. Lett.
98, 40001 (2012)] although the dissipative engines and the irreversible ones
are inequivalent to each other.Comment: 1 figur
Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images
The automatic segmentation of human knee cartilage from 3D MR images is a
useful yet challenging task due to the thin sheet structure of the cartilage
with diffuse boundaries and inhomogeneous intensities. In this paper, we
present an iterative multi-class learning method to segment the femoral, tibial
and patellar cartilage simultaneously, which effectively exploits the spatial
contextual constraints between bone and cartilage, and also between different
cartilages. First, based on the fact that the cartilage grows in only certain
area of the corresponding bone surface, we extract the distance features of not
only to the surface of the bone, but more informatively, to the densely
registered anatomical landmarks on the bone surface. Second, we introduce a set
of iterative discriminative classifiers that at each iteration, probability
comparison features are constructed from the class confidence maps derived by
previously learned classifiers. These features automatically embed the semantic
context information between different cartilages of interest. Validated on a
total of 176 volumes from the Osteoarthritis Initiative (OAI) dataset, the
proposed approach demonstrates high robustness and accuracy of segmentation in
comparison with existing state-of-the-art MR cartilage segmentation methods.Comment: MICCAI 2013: Workshop on Medical Computer Visio
PCA-SIR: a new nonlinear supervised dimension reduction method with application to pain prediction from EEG
Dimension reduction is critical in identifying a small set of discriminative features that are predictive of behavior or cognition from high-dimensional neuroimaging data, such as EEG and fMRI. In the present study, we proposed a novel nonlinear supervised dimension reduction technique, named PCA-SIR (Principal Component Analysis and Sliced Inverse Regression), for analyzing high-dimensional EEG time-course data. Compared with conventional dimension reduction methods used for EEG, such as PCA and partial least-squares (PLS), the PCA-SIR method can make use of nonlinear relationship between class labels (i.e., behavioral or cognitive parameters) and predictors (i.e., EEG samples) to achieve the effective dimension reduction (e.d.r.) directions. We applied the new PCA-SIR method to predict the subjective pain perception (at a level ranging from 0 to 10) from single-trial laser-evoked EEG time courses. Experimental results on 96 subjects showed that reduced features by PCA-SIR can lead to significantly higher prediction accuracy than those by PCA and PLS. Therefore, PCA-SIR could be a promising supervised dimension reduction technique for multivariate pattern analysis of high-dimensional neuroimaging data. © 2015 IEEE.published_or_final_versio
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