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Exponential Sums over Primes Formed with Coefficients of Primitive Cusp Forms

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Acta Mathematica Sinica,English Series 2009年第4期25卷 687-692页

作者： hui xue LAO Department of Mathematics Shandong Normal University Ji'nan 250014 P. R. China School of Mathematics Shandong University Ji'nan 250100 P. R. China

Let Af（n） be the coefficient of the logarithmic derivative for the Hecke L-function. In this paper we study the cancellation of the function Ay（n） twisted with an additive character e（α√n）, α 〉0, i.e. Ef（x... 详细信息

关键词： Fourier coefficient of cusp forms exponential sums Hecke L-function

Bromate removal by gamma irradiation in aqueous solutions

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Nuclear Science and Techniques 2016年第1期27卷 44-49页

作者： Zhao-Lian Ye xue-hui Fang hui-Qi Hou College of Chemistry and Environmental Engineering Jiangsu University of Technology Department of Environmental Science and Engineering Fudan University

Bromate(BrO_3) is a disinfection by-product in drinking water, and its removal is very difficult especially at low levels.60 Co gamma rays were used to remove BrO_3in aqueous solution in this study. The effects of absorbed doses, BrO_3initial concentration, gas saturation, p H value and coexisting anions(Cl, NO 3, SO2 4and HCO 3=CO_3^(2 -))on BrO_3reduction were evaluated. After 4.0-k Gy irradiation of air-equilibrated solution of 30.7 lg/L BrO_3, the residual BrO_3was 8.3 lg/L, which is below the maximum contaminant level of drinking water. The BrO_3reduction rate increased with the dose, in the order of N2[ air [ O_2[ N_2O atmosphere under similar conditions. The results also show that high p H favored the BrO_3removal. According to the experimental results, it can be concluded that the efficiency of decomposing BrO_3by reactive species followed the order of e aq[ H [ HO_2 [ O_2. Coexisting Cl, HCO 3=CO_3^(2 -)and SO2 4ions have little effect on BrO_3removal, whereas NO_3can inhibit its removal as a result of competition with BrO_3for e aq.

关键词： γ射线照射水溶液溴酸盐吸收剂量二氧化碳高pH值消毒副产物含气饱和度

Characteristics of alumina particles in dispersion-strengthened copper alloys

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International Journal of Minerals,Metallurgy and Materials 2014年第11期21卷 1115-1119页

作者： xue-hui Zhang Xiao-xian Li School of Materials Science and Engineering Jiangxi University of Science and Technology School of Metallurgical and Chemical Engineering Jiangxi University of Science and Technology

Two types of alumina dispersion-strengthened copper（ADSC） alloys were fabricated by a novel in-situ reactive synthesis（IRS） and a traditional internal oxidation（IO） process. The features of alumina dispersoids in these ADSC alloys were investigated by X-ray diffraction, scanning electron microscopy, and high-resolution transmission electron microscopy. It is found that nano-sized γ-Al2O3 particles of approximately 10 nm in diameter are homogeneously distributed in the IRS-ADSC composites. Meanwhile, larger-sized, mixed crystal structure alumina with rod-shaped morphology is embedded in the IO-ADSC alloy. The IRS-ADSC composites can obtain better mechanical and physical properties than the IO-ADSC composites; the tensile strength of the IRS-ADSC alloy can reach 570 MPa at room temperature, its electrical conductivity is 85% IACS, and the Rockwell hardness can reach 86 HRB.

关键词： metallic matrix composites alumina copper in-situ reactive synthesis internal oxidation

Two-dimensional assembly of giant molecules

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Science China Chemistry 2018年第1期61卷 17-24页

作者： Zhongguo Liu Deyu Kong xue-hui Dong South China Advanced Institute for Soft Matter Science and Technology South China University of Technology State Key Laboratory of Luminescent Materials and Devices South China University of Technology

This mini review summarizes recent progress on two-dimensional(2 D) self-assembly of giant molecules. Two critical factors with significant impact on the formation of nanostructure are highlighted, i.e., the dimensional constraint of 2 D geometry, and the conformational constraint imposed the rigid molecular nanoparticles(MNPs). Diverse 2 D nanostructures have been fabricated in condensed state and solution by rational molecular design. The collective secondary interactions between functional groups on the periphery of the MNPs and their persistent shape, together with the dimensional limit, change the free energy landscape and lead to unconventional nanostructures. The unique molecular properties of giant molecules endow these 2 D structures with promising technological applications.

关键词： two-dimensional assembly giant molecules packing constraint

High-throughput screening for biomedical applications in a Ti-Zr-Nb alloy system through masking co-sputtering

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Science China(Physics,Mechanics & Astronomy) 2019年第9期62卷 86-94页

作者： xue-hui Yan Jiang Ma Yong Zhang Beijing Advanced Innovation Center of Materials Genome Engineering State Key Laboratory for Advanced Metals and MaterialsUniversity of Science and Technology BeijingBeijing 100083China College of Mechatronic and Control Engineering Shenzhen UniversityShenzhen 518060China

A method of co-sputtering deposition combined with physical masking was applied to the parallel preparation of a ternary Ti-NbZr system alloy. Sixteen independent specimens with varying compositions were obtained. Their microstructure, phase structure,Young’s modulus, nanoindentation hardness, and electrochemical behavior in a phosphate buffer solution(PBS) were studied in detail. It was revealed that the Ti-Zr-Nb alloys possess a single BCC structure. As confirmed via nanoindentation tests, the Young’s modulus of the specimens ranged from 80.3 to 94.8 GPa and the nanoindentation hardness ranged from 3.6 to 5.0 *** optimizing the composition of the specimens, the Ti34Zr52Nb14 alloy was made to possess the lowest modulus in this work(76.5 GPa). Moreover, the Ti34Zr52Nb14 alloy showed excellent corrosion resistance in PBS without any tendency for pitting at anodic potentials up to 1 Vsce. These preliminary advantages offer the opportunity to explore new orthopedic implant alloys based on Ti-Zr-Nb alloys. Moreover, this work provides an effective method for the parallel preparation of biomedical alloys.

关键词： high-throughput biomedical materials co-sputtering physical mask Young’s modulus

Self-corrected unsupervised domain adaptation

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Frontiers of Computer Science 2022年第5期16卷 35-43页

作者： Yunyun WANG Chao WANG hui xue Songcan CHEN School of Computer Science and Engineering Nanjing University of Posts&TelecommunicationsNanjing 210046China School of Computer Science and Engineering Southeast UniversityNanjing 210096China School of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210023China

Unsupervised domain adaptation(UDA),which aims to use knowledge from a label-rich source domain to help learn unlabeled target domain,has recently attracted much *** methods mainly concentrate on source classification and distribution alignment between domains to expect the correct target *** in this paper,we attempt to learn the target prediction end to end directly,and develop a Self-corrected unsupervised domain adaptation(SCUDA)method with probabilistic label *** adopts a probabilistic label corrector to learn and correct the target labels ***,besides model parameters,those target pseudo-labels are also updated in learning and corrected by the anchor-variable,which preserves the class candidates for *** on real datasets show the competitiveness of SCUDA.

关键词： unsupervised domain adaptation adversarial Learning deep neural network pseudo-labels label corrector

Pointwise manifold regularization for semi-supervised learning

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Frontiers of Computer Science 2021年第1期15卷 91-98页

作者： Yunyun WANG Jiao HAN Yating SHEN hui xue Department of Computer Science and Engineering Nanjing University of Posts&TelecommunicationsNanjing 210046China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

Manifold regularization(MR)provides a powerful framework for semi-supervised classification using both the labeled and unlabeled *** constrains that similar instances over the manifold graph should share similar classification out-puts according to the manifold *** is easily noted that MR is built on the pairwise smoothness over the manifold graph,i.e.,the smoothness constraint is implemented over all instance pairs and actually considers each instance pair as a single ***,the smoothness can be pointwise in nature,that is,the smoothness shall inherently occur“everywhere"to relate the behavior of each point or instance to that of its close *** in this paper,we attempt to de-velop a pointwise MR(PW_MR for short)for semi-supervised learning through constraining on individual local *** this way,the pointwise nature of smoothness is preserved,and moreover,by considering individual instances rather than instance pairs,the importance or contribution of individual instances can be *** importance can be described by the confidence for correct prediction,or the local density,for *** provides a different way for implementing manifold smoothness Finally,empirical results show the competitiveness of PW_MR compared to pairwise MR.

关键词： semi-supervised classification manifold regularization pairwise smoothness pointwise smoothness local density

A primal perspective for indefinite kernel SVM problem

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Frontiers of Computer Science 2020年第2期14卷 349-363页

作者： hui xue Haiming XU Xiaohong CHEN Yunyun WANG School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) MOENanjing 210096China College of Science Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210046China

Indefinite kernel support vector machine(IKSVM)has recently attracted increasing attentions in machine *** IKSVM essentially is a non-convex problem,existing algorithms either change the spectrum of indefinite kernel directly but risking losing some valuable information or solve the dual form of IKSVM whereas suffering from a dual gap *** this paper,we propose a primal perspective for solving the *** is,we directly focus on the primal form of IKSVM and present a novel algorithm termed as IKSVM-DC for binary and multi-class ***,according to the characteristics of the spectrum for the indefinite kernel matrix,IKSVM-DC decomposes the primal function into the subtraction of two convex functions as a difference of convex functions(DC)*** accelerate convergence rate,IKSVM-DC combines the classical DC algorithm with a line search step along the descent direction at each ***,we construct a multi-class IKSVM model which can classify multiple classes in a unified form.A theoretical analysis is then presented to validate that IKSVM-DC can converge to a local ***,we conduct experiments on both binary and multi-class datasets and the experimental results show that IKSVM-DC is superior to other state-of-the-art IKSVM algorithms.

关键词： indefinite kernel support vector machine multi-class classification non-convex optimization

A maximum margin clustering algorithm based on indefinite kernels

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Frontiers of Computer Science 2019年第4期13卷 813-827页

作者： hui xue Sen LI Xiaohong CHEN Yunyun WANG School of Computer Science and Engineering Southeast University Nanjing 210096 Chin Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China College of Science Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Department of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046 China

Indefinite kernels have attracted more and more attentions in machine learning due to its wider application scope than usual positive definite kernels. However, the research about indefinite kernel clustering is relatively scarce. Furthermore, existing clustering methods are mainly designed based on positive definite kernels which are incapable in indefinite kernel scenarios. In this paper, we propose a novel indefinite kernel clustering algorithm termed as indefinite kernel maximum margin clustering (IKMMC) based on the state-of-the-art maximum margin clustering (MMC) model. IKMMC tries to find a proxy positive definite kernel to approximate the original indefinite one and thus embeds a new F-norm regularizer in the objective function to measure the diversity of the two kernels, which can be further optimized by an iterative approach. Concretely, at each iteration, given a set of initial class labels, IKMMC firstly transforms the clustering problem into a classification one solved by indefinite kernel support vector machine (IKSVM) with an extra class balance constraint and then the obtained prediction labels will be used as the new input class labels at next iteration until the error rate of prediction is smaller than a prespecified tolerance. Finally, IKMMC utilizes the prediction labels at the last iteration as the expected indices of ***, we further extend IKMMC from binary clustering problems to more complex multi-class scenarios. Experimental results have shown the superiority of our algorithms.

关键词： indefinite kernel maximum margin clustering support vector machine kernel method