325 research outputs found
Linearly Supporting Feature Extraction For Automated Estimation Of Stellar Atmospheric Parameters
We describe a scheme to extract linearly supporting (LSU) features from
stellar spectra to automatically estimate the atmospheric parameters ,
log, and [Fe/H]. "Linearly supporting" means that the atmospheric
parameters can be accurately estimated from the extracted features through a
linear model. The successive steps of the process are as follow: first,
decompose the spectrum using a wavelet packet (WP) and represent it by the
derived decomposition coefficients; second, detect representative spectral
features from the decomposition coefficients using the proposed method Least
Absolute Shrinkage and Selection Operator (LARS); third, estimate the
atmospheric parameters , log, and [Fe/H] from the detected
features using a linear regression method. One prominent characteristic of this
scheme is its ability to evaluate quantitatively the contribution of each
detected feature to the atmospheric parameter estimate and also to trace back
the physical significance of that feature. This work also shows that the
usefulness of a component depends on both wavelength and frequency. The
proposed scheme has been evaluated on both real spectra from the Sloan Digital
Sky Survey (SDSS)/SEGUE and synthetic spectra calculated from Kurucz's NEWODF
models. On real spectra, we extracted 23 features to estimate , 62
features for log, and 68 features for [Fe/H]. Test consistencies between
our estimates and those provided by the Spectroscopic Sarameter Pipeline of
SDSS show that the mean absolute errors (MAEs) are 0.0062 dex for log
(83 K for ), 0.2345 dex for log, and 0.1564 dex for [Fe/H]. For
the synthetic spectra, the MAE test accuracies are 0.0022 dex for log
(32 K for ), 0.0337 dex for log, and 0.0268 dex for [Fe/H].Comment: 21 pages, 7 figures, 8 tables, The Astrophysical Journal Supplement
Series (accepted for publication
Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery
Bridge detection in remote sensing images (RSIs) plays a crucial role in
various applications, but it poses unique challenges compared to the detection
of other objects. In RSIs, bridges exhibit considerable variations in terms of
their spatial scales and aspect ratios. Therefore, to ensure the visibility and
integrity of bridges, it is essential to perform holistic bridge detection in
large-size very-high-resolution (VHR) RSIs. However, the lack of datasets with
large-size VHR RSIs limits the deep learning algorithms' performance on bridge
detection. Due to the limitation of GPU memory in tackling large-size images,
deep learning-based object detection methods commonly adopt the cropping
strategy, which inevitably results in label fragmentation and discontinuous
prediction. To ameliorate the scarcity of datasets, this paper proposes a
large-scale dataset named GLH-Bridge comprising 6,000 VHR RSIs sampled from
diverse geographic locations across the globe. These images encompass a wide
range of sizes, varying from 2,048*2,048 to 16,38*16,384 pixels, and
collectively feature 59,737 bridges. Furthermore, we present an efficient
network for holistic bridge detection (HBD-Net) in large-size RSIs. The HBD-Net
presents a separate detector-based feature fusion (SDFF) architecture and is
optimized via a shape-sensitive sample re-weighting (SSRW) strategy. Based on
the proposed GLH-Bridge dataset, we establish a bridge detection benchmark
including the OBB and HBB tasks, and validate the effectiveness of the proposed
HBD-Net. Additionally, cross-dataset generalization experiments on two publicly
available datasets illustrate the strong generalization capability of the
GLH-Bridge dataset.Comment: 16 pages, 11 figures, 6 tables; due to the limitation "The abstract
field cannot be longer than 1,920 characters", the abstract appearing here is
slightly shorter than that in the PDF fil
Preparation and Characterization of Polyurethane/Nanocopper Composites and Their Application in Intrauterine Devices
A novel intrauterine devices material, polyurethane/nano-copper (PU/NC) nanocomposite, was prepared. The structure, morphology, copper ion (Cu2+) release rate, and water absorption of PU/NC nanocomposites were investigated. The results indicated that the nanocoppers were uniformly dispersed in the matrix. The release rates of Cu2+ of PU/NC nanocomposites remained stable during the experimentation time. These results indicated that the PU/NC nanocomposites have a great potential to replace current commercial intrauterine devices materials
Combined Y-configured stents for revising occluded transjugular intrahepatic portosystemic shunt
PURPOSEWe aimed to determine the technical feasibility, safety and prognosis of the transjugular intrahepatic portosystemic shunt (TIPS) revision by combined Y-configured stents placement.METHODSWe retrospectively evaluated 12 patients who received TIPS revision using Y-stenting technique between June 2015 and January 2019. The rates of technical success, complication, shunt patency, hepatic encephalopathy and mortality were described and analyzed.RESULTSThe combined Y-configured stents were successfully placed in 11 of 12 patients (92%) without major complications. The median portosystemic pressure gradient (PPG) decreased from 23 mmHg (interquartile range, IQR, 18.5–27.5 mmHg) to 10 mmHg (IQR, 9–14 mmHg). The left internal jugular vein approach was used in 5 patients. Four patients required a shunt extension with an extra stent to resolve the stenosis at the portal venous terminus. Two patients developed hepatic encephalopathy, which was medically controlled within 3 months after the procedure. The TIPS patency and survival rates were both 100% during a median follow-up period of 10 months (IQR, 5.5–14 months).CONCLUSIONTIPS revision by combined Y-configured stents placement was technically feasible and safe with favorable clinical outcomes
Preparation and Characterization of Polyurethane/Nanocopper Composites and Their Application in Intrauterine Devices
A novel intrauterine devices material, polyurethane/nano-copper (PU/NC) nanocomposite, was prepared. The structure, morphology, copper ion (Cu 2+ ) release rate, and water absorption of PU/NC nanocomposites were investigated. The results indicated that the nanocoppers were uniformly dispersed in the matrix. The release rates of Cu 2+ of PU/NC nanocomposites remained stable during the experimentation time. These results indicated that the PU/NC nanocomposites have a great potential to replace current commercial intrauterine devices materials
Direction dependent switching of carrier-type enabled by Fermi surface geometry
While charge carriers can typically be designated as either electron- or
hole- type, depending on the sign of the Hall coefficient, some materials defy
this straightforward classification. Here we find that LaRhGe goes
beyond this dichotomy, where the Hall resistivity is electron-like for magnetic
fields along the -axis but hole-like in the basal plane. Together with
first-principles calculations, we show that this direction-dependent switching
of the carrier type arises within a single band, where the special geometry
leads to charge carriers on the same Fermi surface orbiting as electrons along
some directions, but holes along others. The relationship between the Fermi
surface geometry and occurrence of a Hall sign reversal is further generalized
by considering tight-binding model calculations, which show that this type of
Fermi surface corresponds to a more robust means of realizing this phenomenon,
suggesting an important route for tailoring direction dependent properties for
advanced electronic device applications.Comment: 7 pages, 5 figure
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