Continuum versus discrete networks,graph Laplacians,and reproducing kernel Hilbert spaces

Motivated by applications to machine learning, we construct a reversible and irreducible Markov chain whose state space is a certain collection of measurable sets of a chosen l.c.h. space $\mathfrak{X}$. We study the resulting network (connected undirected graph), including transience, Royden and Riesz decompositions, and kernel factorization. We describe a construction for Hilbert spaces of signed measures which comes equipped with a new notion of reproducing kernels and there is a unique solution to a regularized optimization problem involving the approximation of $L^2$ functions by functions of finite energy. The latter has applications to machine learning (for Markov random fields, for example).     

Probe chiral magnetic effect with signed balance function

In this paper a pair of observables are proposed as alternative ways,by examining the fluctuation of net momentum-ordering of charged pairs,to study the charge separation induced by the Chiral Magnetic Effect(CME)in relativistic heavy ion collisions.They are,the out-of-plane to in-plane ratio of fluctuation of the difference between signed balance functions measured in pair’s rest frame,and the ratio of it to similar measurement made in the laboratory frame.Both observables have been studied with simulations including flow-related backgrounds,and for the first time,backgrounds that are related to resonance's global spin alignment.The two observables have similar positive responses to signal,and opposite,limited responses to identifiable backgrounds arising from resonance flow and spin alignment.Both observables have also been tested with two realistic models,namely,a multi-phase transport(AMPT)model and the anomalous-viscous fluid dynamics(AVFD)model.These two observables,when cross examined,will provide useful insights in the study of CME-induced charge separation.     

具有遗传性疾病和性状的遗传位点分析

随着信息技术的进步和发展,现代生物学越来越多地将这些技术用于大规模生物数据的收集、分析、挖掘等过程.大量计算机技术,特别是统计方法被用来进行复杂疾病的分析.大量研究表明,人体的许多表型性状差异以及对药物和疾病的易感性等都可能与某些位点相关联,或和包含有多个位点的基因相关联.因此,定位与性状或疾病相关联的位点在染色体或基因中的位置,能帮助研究人员了解性状和一些疾病的遗传机理,也能使人们对致病位点加以干预,防止一些遗传病的发生.利用随机森林方法、Bootstrap重抽样、logistic回归等大数据分析方法,意在解决优化生物学位点关联性分析中单一致病位点识别、多位点相互作用和多性状位点关联性分析等子问题.     

Permutations via linear translators

We show that many infinite classes of permutations over finite fields can be constructed via translators with a large choice of parameters. We first characterize some functions having linear translators, based on which several families of permutations are then derived. Extending the results of [9], we give in several cases the compositional inverse of these permutations. The connection with complete permutations is also utilized to provide further infinite classes of permutations. Moreover, we propose new tools to study permutations of the form $x\mapsto x+{(x^{p^m}-x+\delta )}^s$ and a few infinite classes of permutations of this form are proposed.     

符号圈拼接图的零度

令$\eta(\Gamma)$和$c(\Gamma)$是符号图$\Gamma$的零度和基本圈数. 一个符号圈拼接图是指每个块都是圈的连通符号图. 本文证明了对任意符号拼接图$\eta(\Gamma)\le c(\Gamma)+1$成立, 并且刻画了等号成立的极图, 推广了王登银等人(2022)在简单圈拼接图上的结果. 此外, 我们证明了任意的符号拼接图$\eta(\Gamma)\neq c(\Gamma)$, 给出了满足$\eta(\Gamma)=c(\Gamma)-1$的符号拼接图的一些性质并刻画处$\eta(\Gamma)=c(\Gamma)-1$的二部符号拼接图.     

Refined Composite Multi-Scale Reverse Weighted Permutation Entropy and Its Applications in Ship-Radiated Noise

Ship-radiated noise is one of the important signal types under the complex ocean background, which can well reflect physical properties of ships. As one of the valid measures to characterize the complexity of ship-radiated noise, permutation entropy (PE) has the advantages of high efficiency and simple calculation. However, PE has the problems of missing amplitude information and single scale. To address the two drawbacks, refined composite multi-scale reverse weighted PE (RCMRWPE), as a novel measurement technology of describing the signal complexity, is put forward based on refined composite multi-scale processing (RCMP) and reverse weighted PE (RWPE). RCMP is an improved method of coarse-graining, which not only solves the problem of single scale, but also improves the stability of traditional coarse-graining; RWPE has been proposed more recently, and has better inter-class separability and robustness performance to noise than PE, weighted PE (WPE), and reverse PE (RPE). Additionally, a feature extraction scheme of ship-radiated noise is proposed based on RCMRWPE, furthermore, RCMRWPE is combined with discriminant analysis classifier (DAC) to form a new classification method. After that, a large number of comparative experiments of feature extraction schemes and classification methods with two artificial random signals and six ship-radiated noise are carried out, which show that the proposed feature extraction scheme has better performance in distinguishing ability and stability than the other three similar feature extraction schemes based on multi-scale PE (MPE), multi-scale WPE (MWPE), and multi-scale RPE (MRPE), and the proposed classification method also has the highest recognition rate.