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 $T_{eff}$, log$~g$, 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)$_{bs}$; third, estimate the atmospheric parameters $T_{eff}$, log$~g$, 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 $T_{eff}$, 62 features for log$~g$, 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$~T_{eff}$ (83 K for $T_{eff}$), 0.2345 dex for log$~g$, and 0.1564 dex for [Fe/H]. For the synthetic spectra, the MAE test accuracies are 0.0022 dex for log$~T_{eff}$ (32 K for $T_{eff}$), 0.0337 dex for log$~g$, 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 LaRh$_6$Ge$_4$ goes beyond this dichotomy, where the Hall resistivity is electron-like for magnetic fields along the $c$-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