Distribution-free uncertainty quantification for kernel methods by gradient perturbations

Machine Learning - We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributional assumptions, hence,...     

Ultrafast Electron–Phonon Coupling at Metal-Dielectric Interface

Energy transfer from photo-excited electrons in a metal thin film to the dielectric substrate is important for understanding the ultrafast heat transfer process across the two materials. Substantial research has been conducted to investigate heat transfer in a metal-dielectric structure. In this work, a two-temperature model in metal was used to analyze the interface electron and dielectric substrate coupling. An improved temperature and wavelength-dependent Drude–Lorentz model was implemented to interpret the signals obtained in optical measurements. Ultrafast pump-and-probe measurements on Au-Si samples were carried out, where the probe photon energy was chosen to be close to the interband transition threshold of gold to minimize the influence of non-equilibrium electrons on the optical response and maximize the thermal modulation to the optical reflectance. Electron-substrate interface thermal conductance at different pump laser fluences was obtained, and was found to increase with the interface temperature.     

Continuation methods for approximate large scale object sequencing

Machine Learning - We propose a set of highly scalable algorithms for the combinatorial data analysis problem of seriating similarity matrices. Seriation consists of finding a permutation of data...     

Universities through the eyes of bibliographic databases: a retroactive growth comparison of Google Scholar,Scopus and Web of Science

Scientometrics - The purpose of this study is to ascertain the suitability of GS’s url-based method as a valid approximation of universities’ academic output measures, taking into...     

Evaluation of Intellectual Property Objects in the Nanoindustry Field

Russian Microelectronics - The recently developed nanomaterials and their production technologies as intellectual property objects (IPOs) are considered. The role of the informational-analytical...     

Threshold privacy-preserving cloud auditing with multiple uploaders

International Journal of Information Security - Data integrity is a critical security issue in cloud storage. The data integrity checking schemes by a third-party auditor (TPA) have attracted a lot...     

Super-Resolution Reconstruction of Images Based on Microarray Camera

In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array images. The array images are interpolated to obtain a HR image initially followed by a convolution neural network (CNN) procedure for enhancement. The convolution layers of our convolution neural network are 3×3 or 1×1 layers, of which the 1×1 layers are used to improve the network performance particularly. A bottleneck structure is applied to reduce the parameter numbers of the nonlinear mapping and to improve the nonlinear capability of the whole network. Finally, we use a 3×3 deconvolution layer to significantly reduce the number of parameters compared to the deconvolution layer of FSRCNN-s. The experiments show that the proposed method can not only ameliorate effectively the texture quality of the target image based on the array images information, but also further enhance the quality of the initial high resolution image by the improved CNN.     

Decorative near-infrared transmission filters featuring high-efficiency and angular-insensitivity employing 1D photonic crystals

We present a new scheme for visibly-opaque but near-infrared-transmitting filters involving 7 layers based on one-dimensional ternary photonic crystals, with capabilities in reaching nearly 100% transmission efficiency in the near-infrared region. Different decorative reflection colors can be created by adding additional three layers while maintaining the near-infrared transmission performance. In addition, our proposed structural colors show great angular insensitivity up to ±60° for both transverse electric and transverse magnetic polarizations, which are highly desired in various fields. The facile strategy described here involves a simple deposition method for the fabrication, thereby having great potential in diverse applications such as image sensors, anti-counterfeit tag, and optical measurement systems.

     

Separation of hematite from banded hematite jasper (BHJ) by magnetic coating

The separation of iron oxide from banded hematite jasper(BHJ) assaying 47.8% Fe, 25.6% Si O2 and 2.30%Al2O3 using selective magnetic coating was studied. Characterization studies of the low grade ore indicate that besides hematite and goethite,jasper, a microcrystalline form of quartzite, is the major impurity associated with this ore. Beneficiation by conventional magnetic separation technique could yield a magnetic concentrate containing 60.8% Fe with 51% Fe recovery. In order to enhance the recovery of the iron oxide minerals, fine magnetite, colloidal magnetite and oleate colloidal magnetite were used as the coating material. When subjected to magnetic separation, the coated ore produces an iron concentrate containing 60.2% Fe with an enhanced recovery of56%. The AFM studies indicate that the coagulation of hematite particles with the oleate colloidal magnetite facilitates the higher recovery of iron particles from the low grade BHJ iron ore under appropriate conditions.