Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

‘渝城1号’核桃矿质元素周年吸收规律研究

以‘渝城1号’核桃为研究对象,通过测定不同生育期的主要器官样品的生物量和N,P,K,Ca,Mg质量分数,总结分析核桃对5种矿质元素的吸收规律.结果表明:①开花坐果以后,核桃干物质快速增加,增加的干物质主要累积在果实、根和茎中,并在果实膨大期后,果实中的干物质累积量显著高于其他器官;②从各器官矿质元素质量分数上看,茎、营养枝和果枝中的N,P,K质量分数在展叶抽梢期最高,茎和营养枝中的Ca质量分数在果实膨大期最高,营养枝和果枝中Mg质量分数在落叶期最高.③从各器官矿质元素的累积量看,开花坐果前,N,P,K在根部的累积量最高.开花坐果后,果实中N,P,K的累积量逐渐升高.整个生育期,Ca在根部中的累积量最高,而Mg在果实膨大期前以根部累积量最高,之后在果实中的累积量最高;④5个矿质元素中,核桃对N,K,Ca吸收量大于P和Mg,其中核桃对N,P吸收量最大的时间在开花坐果期,对K和Mg吸收量最大的时间在坐果初期,对Ca吸收量最大的时间在果实膨大期.因此,开花坐果期到果实膨大期是核桃施肥的关键时期,在该时期应及时补充相应的矿质元素,以实现核桃的科学高效施肥.

基于L_(1/2)正则化的抛物线Radon变换多次波压制方法

在地震数据处理中,多次波的存在会对地震数据成像和地震资料解释带来影响,如何有效地压制多次波干扰是地震勘探中的重要问题。抛物线Radon变换因其高效的特点被广泛应用于多次波压制中,但在野外地震数据采集时,炮检距的有限性会导致变换域中的能量扩散,产生假象,使多次波压制达不到理想的效果。针对此问题,提出一种基于L_(1/2)正则化的稀疏反演高分辨抛物线Radon变换,并应用广义迭代收缩算法(generalized iterated shrinkage algorithm,GISA)进行求解。研究结果表明,L_(1/2)正则化有很强的稀疏约束能力,能提高解的稀疏度,改进信噪分离的效果。与最小二乘反演和基于L_(1)正则化的稀疏反演相比,基于L_(1/2)正则化的稀疏反演高分辨抛物线Radon变换能更有效地压制多次波,并确保了重构数据与原始数据的一致性。

Markov Chains Based on Random Generalized 1-Flipper Operations for Connected Regular Multi-digraphs

The properties of generalized flip Markov chains on connected regular digraphs are discussed.The 1-Flipper operation on Markov chains for undirected graphs is generalized to that for multi-digraphs.The generalized 1-Flipper operation preserves the regularity and weak connectivity of multi-digraphs.The generalized 1-Flipper operation is proved to be symmetric.Moreover,it is presented that a series of random generalized 1-Flipper operations eventually lead to a uniform probability distribution over all connected d-regular multi-digraphs without loops.

基于有约束L_(1/2)范数稀疏正则化的声源识别方法

基于等效源法(equivalent source method,ESM)的近场声全息(near field acoustic holography,NAH)是一种有效的声源识别技术。然而,针对空间稀疏分布的声源识别问题,传统基于L_(2)范数以及基于L_(1)范数的ESM方法分别存在声源幅值被低估与算法稳定性差等问题。因此,提出了基于有约束L_(1/2)范数稀疏正则化的声源识别方法,该方法具有强稀疏性与强抗干扰的优势,可以解决传统方法的声源识别精度低的问题。通过数值模拟试验以及普通室内的实测实验,验证了方法的有效性。

Source reconstruction for bioluminescence tomography via L_(1/2)regularization

Bioluminescence tomography(BLT)is an important noninvasive optical molecular imaging modality in preclinical research.To improve the image quality,reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem.The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm.In this paper,we present a reconstruction method based on L_(1/2) regularization to enhance sparsity of BLT solution and solve the nonconvex L_(1/2) norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights.To assess the performance of the proposed reconstruction algorithm,simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms,including the weighted interior-point,L1 homotopy,and the Stagewise Orthogonal Matching Pursuit algorithm.Simulation results show that the proposed method yield stable reconstruction results under different noise levels.Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy,multiple-source resolving and image quality.

Wavelet-based L_(1/2) regularization for CS-TomoSAR imaging of forested area

Tomographic synthetic aperture radar(TomoSAR)imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction.In these years,for the sparse elevation distribution,compressive sensing(CS)is a developed favorable technique for the high-resolution elevation reconstruction in TomoSAR by solving an L_(1) regularization problem.However,because the elevation distribution in the forested area is nonsparse,if we want to use CS in the recovery,some basis,such as wavelet,should be exploited in the sparse L_(1/2) representation of the elevation reflectivity function.This paper presents a novel wavelet-based L_(2) regularization CS-TomoSAR imaging method of the forested area.In the proposed method,we first construct a wavelet basis,which can sparsely represent the elevation reflectivity function of the forested area,and then reconstruct the elevation distribution by using the L_(1/2) regularization technique.Compared to the wavelet-based L_(1) regularization TomoSAR imaging,the proposed method can improve the elevation recovered quality efficiently.

转录因子HNF1A、HNF4A和FOXA2调节肝细胞蛋白质N-糖基化

Hepatocyte nuclear factor 1 alpha(HNF1A),hepatocyte nuclear factor 4 alpha(HNF4A),and forkhead box protein A2(FOXA2)are key transcription factors that regulate a complex gene network in the liver,cre-ating a regulatory transcriptional loop.The Encode and ChIP-Atlas databases identify the recognition sites of these transcription factors in many glycosyltransferase genes.Our in silico analysis of HNF1A,HNF4A.and FOXA2 binding to the ten candidate glyco-genes studied in this work confirms a significant enrich-ment of these transcription factors specifically in the liver.Our previous studies identified HNF1A as a master regulator of fucosylation,glycan branching,and galactosylation of plasma glycoproteins.Here,we aimed to functionally validate the role of the three transcription factors on downstream glyco-gene transcriptional expression and the possible effect on glycan phenotype.We used the state-of-the-art clus-tered regularly interspaced short palindromic repeats/dead Cas9(CRISPR/dCas9)molecular tool for the downregulation of the HNF1A,HNF4A,and FOXA2 genes in HepG2 cells-a human liver cancer cell line.The results show that the downregulation of all three genes individually and in pairs affects the transcrip-tional activity of many glyco-genes,although downregulation of glyco-genes was not always followed by an unambiguous change in the corresponding glycan structures.The effect is better seen as an overall change in the total HepG2 N-glycome,primarily due to the extension of biantennary glycans.We propose an alternative way to evaluate the N-glycome composition via estimating the overall complexity of the glycome by quantifying the number of monomers in each glycan structure.We also propose a model showing feedback loops with the mutual activation of HNF1A-FOXA2 and HNF4A-FOXA2 affecting glyco-genes and protein glycosylation in HepG2 cells.

L1/2 Regularization Based on Bayesian Empirical Likelihood

Bayesian empirical likelihood is a semiparametric method that combines parametric priors and nonparametric likelihoods, that is, replacing the parametric likelihood function in Bayes theorem with a nonparametric empirical likelihood function, which can be used without assuming the distribution of the data. It can effectively avoid the problems caused by the wrong setting of the model. In the variable selection based on Bayesian empirical likelihood, the penalty term is introduced into the model in the form of parameter prior. In this paper, we propose a novel variable selection method, L1/2 regularization based on Bayesian empirical likelihood. The L1/2 penalty is introduced into the model through a scale mixture of uniform representation of generalized Gaussian prior, and the posterior distribution is then sampled using MCMC method. Simulations demonstrate that the proposed method can have better predictive ability when the error violates the zero-mean normality assumption of the standard parameter model, and can perform variable selection.

基于长波平顺性的400 km·h^(-1)高速铁路连续梁桥徐变变形控制

基于实测轨道平顺性数据,采用车辆-轨道耦合动力仿真方法,综合考虑温度变化、列车荷载、徐变和沉降等因素的影响,进行400 km·h^(-1)高速铁路连续梁桥徐变变形控制研究。结果表明:400 km·h^(-1)高速列车车体垂向敏感波长为163 m,横向敏感波长为227 m;为保证轨道高低不平顺与车体垂向加速度的适应性,采用60 m中点弦测值控制轨道长波平顺性;车体垂向加速度与轨道高低不平顺之间呈线性相关;徐变与沉降为控制连续梁桥轨道长波平顺性的主要因素,支座位置处长波平顺性较差;400 km·h^(-1)连续梁桥桥上线路允许桥梁自身总变形引起的车体垂向加速度阈值为0.822 m·s^(-2),400 km·h^(-1)主跨不大于百米连续梁桥徐变变形阈值宜设为9.5 mm。

Experimental Data-Driven Flow Field Prediction for Compressor Cascade based on Deep Learning and l_(1)Regularization

For complex flows in compressors containing flow separations and adverse pressure gradients,the numerical simulation results based on Reynolds-averaged Navier-Stokes(RANS)models often deviate from experimental measurements more or less.To improve the prediction accuracy and reduce the difference between the RANS prediction results and experimental measurements,an experimental data-driven flow field prediction method based on deep learning and l_(1)regularization is proposed and applied to a compressor cascade flow field.The inlet boundary conditions and turbulence model parameters are calibrated to obtain the high-fidelity flow fields.The Saplart-Allmaras and SST turbulence models are used independently for mutual validation.The contributions of key modified parameters are also analyzed via sensitivity analysis.The results show that the prediction error can be reduced by nearly 70%based on the proposed algorithm.The flow fields predicted by the two calibrated turbulence models are almost the same and nearly independent of the turbulence models.The corrections of the inlet boundary conditions reduce the error in the first half of the chord.The turbulence model calibrations fix the overprediction of flow separation on the suction surface near the tail edge.

APPROXIMATION ANALYSES FOR FUZZY VALUED FUNCTIONS IN L_1(μ)-NORM BY REGULAR FUZZY NEURAL NETWORKS

By defining fuzzy valued simple functions and giving L1(μ) approximations of fuzzy valued integrably bounded functions by such simple functions, the paper analyses by L1(μ)-norm the approximation capability of four-layer feedforward regular fuzzy neural networks to the fuzzy valued integrably bounded function F : Rn → FcO(R). That is, if the transfer functionσ: R→R is non-polynomial and integrable function on each finite interval, F may be innorm approximated by fuzzy valued functions defined as to anydegree of accuracy. Finally some real examples demonstrate the conclusions.

Bernoulli-based random undersampling schemes for 2D seismic data regularization

Seismic data regularization is an important preprocessing step in seismic signal processing. Traditional seismic acquisition methods follow the Shannon–Nyquist sampling theorem, whereas compressive sensing（CS） provides a fundamentally new paradigm to overcome limitations in data acquisition. Besides the sparse representation of seismic signal in some transform domain and the 1-norm reconstruction algorithm, the seismic data regularization quality of CS-based techniques strongly depends on random undersampling schemes. For 2D seismic data, discrete uniform-based methods have been investigated, where some seismic traces are randomly sampled with an equal probability. However, in theory and practice, some seismic traces with different probability are required to be sampled for satisfying the assumptions in CS. Therefore, designing new undersampling schemes is imperative. We propose a Bernoulli-based random undersampling scheme and its jittered version to determine the regular traces that are randomly sampled with different probability, while both schemes comply with the Bernoulli process distribution. We performed experiments using the Fourier and curvelet transforms and the spectral projected gradient reconstruction algorithm for 1-norm（SPGL1）, and ten different random seeds. According to the signal-to-noise ratio（SNR） between the original and reconstructed seismic data, the detailed experimental results from 2D numerical and physical simulation data show that the proposed novel schemes perform overall better than the discrete uniform schemes.

Parameter Optimization of Regularization Variational Merging and Its Application in GNSS/MET Water Vapor

The paper discusses the core parameters of the 3 D and 4 D variational merging based on L1 norm regularization,namely optimization characteristic correlation length of background error covariance matrix and regularization parameter. Classical 3 D/4 D variational merging is based on the theory that error follows Gaussian distribution. It involves the solution of the objective functional gradient in minimization iteration,which requires the data to have continuity and differentiability. Classic 3 D/4 D-dimensional variational merging method was extended,and L1 norm was used as the constraint coupling to the classical variational merged model. Experiment was carried out by using linear advection-diffusion equation as four-dimensional prediction model,and parameter optimization of this method is discussed. Considering the strong temporal and spatial variation of water vapor,this method is further applied to the precipitable water vapor( PWV) merging by calculating reanalysis data and GNSS retrieval.Parameters were adjusted gradually to analyze the influence of background field on the merging result,and the experiment results show that the mathematical algorithm adopted in this paper is feasible.

L(d,1)-labeling of regular tilings

L(d, 1)-labeling is a kind of graph coloring problem from frequency assignment in radio networks, in which adjacent nodes must receive colors that are at least d apart while nodes at distance two from each other must receive different colors. We focus on L(d, 1)-labeling of regular tilings for d≥3 since the cases d=0, 1 or 2 have been researched by Calamoneri and Petreschi. For all three kinds of regular tilings, we give their L (d, 1)-labeling numbers for any integer d≥3. Therefore, combined with the results given by Calamoneri and Petreschi, the L(d, 1)-labeling numbers of regular tilings for any nonnegative integer d may be determined completely.

REGULARITY OF H^1 ∩L^(n((γ-1)/(2-γ))) WEAK SOLUTIONS FOR NONLINEAR ELLIPTIC SYSTEMS

Let us consider the following elliptic systems of second order-D_α(A_i~α(x, u, Du))=B_4(x, u, Du), i=1, …, N, x∈Q(?)R^n, n≥3 (1) and supposeⅰ) |A_i~α(x, u, Du)|≤L(1+|Du|);ⅱ) (1+|p|)^(-1)A_i~α(x, u, p)are H(?)lder-continuous functions with some exponent δ on (?)×R^N uniformly with respect to p, i.e.ⅲ) A_i~α(x, u, p) are differentiable function in p with bounded and continuous derivativesⅳ)ⅴ) for all u∈H_(loc)~1(Ω, R^N)∩L^(n(γ-1)/(2-γ))(Ω, R^N), B(x, u, Du)is ineasurable and |B(x, u, p)|≤a(|p|~γ+|u|~τ)+b(x), where 1+2/n<γ<2, τ≤max((n+2)/(n-2), (γ-1)/(2-γ)-ε), (?)ε>0, b(x)∈L2n/(n+2), n^2/(n+2)+e(Ω), (?)ε>0.Remarks. Only bounded open set Q will be considered in this paper; for all p≥1, λ≥0, which is clled a Morrey Space.Let assumptions ⅰ)-ⅳ) hold, Giaquinta and Modica have proved the regularity of both the H^1 weak solutions of (1) under controllable growth condition |B|≤α(|p|~γ+|u|^((n+2)/(n-2))+b, 0<γ≤1+2/n and the H^1∩L~∞ weak solutions of (1) under natural growth condition |B|≤α|p|~2+b with a smallness condition 2aM<λ(|u|≤M), which implys that the H^1∩L~∞ weak solutions have the same regularty in the case of 1+2/n<γ<2. In the case of γ=2, many counterexamples (see [2] showed that u must be in H^1L~∞, while in the case of 1+2/n<γ<2, we consider the H^1∩L^n(γ-1)/(2-γ) weak solutions of (1), weaken the instability conditions upon them (from L~∞ to L^n(γ-1)/(2-γ) and obtain the same regularity results. Finally we show that the exponent n(γ-1)/(2-γ) can not be docreased anymore for the sake of the regularity results.Delinition 1. We call u∈H^1∩L^n(γ-1)/(2-γ)(Q, R^N) be a weak solution of (1), providod that where We use the convention that repeated indices are summed. i, j go from 1 to N ann α, β from 1 to n.

具有小度数的1-正则Cayley有向图

令Γ是一个图,如果Γ的自同构群Aut(Γ)作用在其弧集上是正则的,则称图Γ为1弧正则图,简称1正则图。本文利用局部分析法和全局分析法,结合图论、群论,尤其是置换群的一些理论以及方法,给出了度数分别为3、4、5、6的1正则Cayley有向图的完全分类,这样的图如果不是正规或双正规的,那么它一定是某个商图的正规(多重)覆盖。

基于L_(1)&TV正则化的低过采样Staggered SAR成像方法

高分辨率宽测绘带(high-resolution and wide-swath,HRWS)成像是星载合成孔径雷达(synthetic aperture radar,SAR)发展的重要趋势。Staggered SAR通过改变脉冲重复频率(pulse repetition frequency,PRF)将盲区分散在整个成像带内,可以将距离向幅宽扩展为传统体制的数倍。针对变PRF模式存在的非均匀采样和回波数据丢失问题,提出了一种基于L_(1)&TV正则化的低过采样Staggered SAR成像方法。所提方法可以在不恢复丢失数据的情况下实现方位模糊抑制,并且在稀疏重构模型中引入TV正则化项,从而实现分布式目标的精确重构。仿真和实际数据实验验证了所提方法的有效性。

基于L_(1−2)时空域总变分正则项的红外弱小目标检测算法

针对红外图像序列中复杂背景干扰下容易出现的高虚警问题,提出一种基于L_(1-2)时空域总变分正则项的红外弱小目标检测算法。首先,将红外图像序列转化为时空域红外张量块,该步骤可利用张量的高维数据结构优势关联图像序列中的时空域信息。然后,利用加权Schattenp范数和L_(1-2)时空域总变分正则项对低秩背景成分进行重构,以保留背景中起伏剧烈的边缘和角点,提高稀疏目标的重构精度。最后,将目标张量恢复为图像序列,利用自适应阈值分割方法得到最终的目标图像。与另外5种检测算法进行对比实验,结果显示,该方法的虚警率较Maxemeidan算法、Tophat算法、LIRDNet算法、DNANet算法以及WSNMSTIPT算法平均分别下降了71.4%、71.1%、68.5%、74.3%和20.47%;而在检测实时性方面,该算法耗时为Maxemeidan算法、DNANet算法以及WSNMSTIPT算法的42.4%、82.9%和28.7%。实验结果验证了该方法在检测性能上的优越性,表明该算法能够显著提高复杂背景干扰下的目标检测精度和效率。

稀疏相位恢复的加权L_(1)-正则Huber回归方法

相位恢复是指从傅里叶变换或线性变换的幅值中恢复信号,广泛应用于物理科学、机器学习和工程等领域.由于相位信息的丢失导致该问题是病态的,而恢复原始信号一般需要信号的先验知识.本文已知信号稀疏性,提出了一种将Huber损失函数与加权L_(1)正则项相结合的相位恢复方法.该方法运用Majorization-Minimization(MM)优化技术对目标函数进行优化,将原始非凸相位恢复问题转化为容易求解的替代优化问题,接着利用软阈值算子求解给出不动点方程,构造算法框架并进行收敛性分析.数值实验结果表明了加权L_(1)-Huber方法的有效性和稳健性.