Investigation of Edge Plasma Features in the HL-IM Tokamak

Edge plasma features in typical HL- 1M discharges were presented. Particle confinement and plasma rotation have been investigated in the discharges with lower hybrid current drive (LHCD), molecular beam injection (MBI) and pellet fuelling. LHCD can make particle confinement increase a factor of 2-3 for low-density discharge. Particle confinement time and poloidal rotation can be at least doubled after pellet injection, while MBI can make confinement time increase about one order of magnitude with higher performance.

Zircon U-Pb dating,Geochemical Features of the Halunwusu Composite Granites at the Northern Edge of Southern Qilian,Qinghai

1 Introduction Halunwusu Composite Granites,locates in the northern margin of Belt,between the North Qaidam block and the Central Qilian block,with two phases of magmatic activities.The granites mainly consisits of the Early

Shallow Feature-driven Dual-edges Localization Network for Weakly Supervised Localization

Weakly supervised object localization mines the pixel-level location information based on image-level annotations.The traditional weakly supervised object localization approaches exploit the last convolutional feature map to locate the discriminative regions with abundant semantics.Although it shows the localization ability of classification network,the process lacks the use of shallow edge and texture features,which cannot meet the requirement of object integrity in the localization task.Thus,we propose a novel shallow feature-driven dual-edges localization(DEL)network,in which dual kinds of shallow edges are utilized to mine entire target object regions.Specifically,we design an edge feature mining(EFM)module to extract the shallow edge details through the similarity measurement between the original class activation map and shallow features.We exploit the EFM module to extract two kinds of edges,named the edge of the shallow feature map and the edge of shallow gradients,for enhancing the edge details of the target object in the last convolutional feature map.The total process is proposed during the inference stage,which does not bring extra training costs.Extensive experiments on both the ILSVRC and CUB-200-2011 datasets show that the DEL method obtains consistency and substantial performance improvements compared with the existing methods.

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications.Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension.The classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral features.For this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral bands.The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel.The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system.In this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in conjunction.The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI.In the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information.The resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost.The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF.The classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa factor.The proposed scheme achieved greater than 90%results for all the performance evaluation metrics.The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range.The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.

Progressive edge-sensing dynamic scene deblurring

Deblurring images of dynamic scenes is a challenging task because blurring occurs due to a combination of many factors.In recent years,the use of multi-scale pyramid methods to recover high-resolution sharp images has been extensively studied.We have made improvements to the lack of detail recovery in the cascade structure through a network using progressive integration of data streams.Our new multi-scale structure and edge feature perception design deals with changes in blurring at different spatial scales and enhances the sensitivity of the network to blurred edges.The coarse-to-fine architecture restores the image structure,first performing global adjustments,and then performing local refinement.In this way,not only is global correlation considered,but also residual information is used to significantly improve image restoration and enhance texture details.Experimental results show quantitative and qualitative improvements over existing methods.

Diagnosing phenotypes of single-sample individuals by edge biomarkers

Network or edge biomarkers area reliable form to characterize phenotypes or diseases.However,obtaining edges orcorrelations between molecules for an individual requires measurement ofmultiple samples of that individual,which are generally unavailable in clinical practice.Thus,it is strongly demanded to diagnose a disease by edge or network biomarkers in one-sample-for-one-individual context.Here,we developed a new computational framework,EdgeBiomarker,to integrate edge and node biomarkers to diagnose phenotype of each single test sample.By applying the method to datasets of lung and breast cancer,it reveals new marker genes/gene-pairs and related sub-networks for distinguishing earlier and advanced cancer stages.Our method shows advantages over traditional methods:(i)edge biomarkers extracted from non-differentially expressed genes achieve better cross-validation accuracy of diagnosis than molecule or node biomarkers from differentially expressed genes,suggesting that certain pathogenic information is only present at the level of network and under-estimated by traditional methods;(ii)edge biomarkers categorize patients into low/high survival rate in a more reliablemanner;(iii)edge biomarkers are significantly enriched in relevant biological functions or pathways,implying that the association changes ina network,rather than expression changes in individual molecules,tendtobe causally related to cancer development.The new frameworkof edgebiomarkers paves theway for diagnosing diseases and analyzing the irmolecular mechanisms by edges or networks in one-sample-for-one-individual basis.This also provides a powerful tool for precision medicine or big-data medicine.