基于改进K-Means聚类的煤炭交易者信誉度划分

针对目前煤炭销售中存在的煤炭质量信息分散性与质量计价政策对其质量精确性要求的矛盾,提出了采用对初始聚类中心优化选取的K-Means聚类算法,对大型煤炭企业及其联盟客户煤炭质量检验数据进行系统分析和挖掘,获得双方质量检验行为统计规律,进行交易双方信誉度等级划分,将不确定性的质量指标转化为确定性的交易者质量检验行为评价。该研究一方面可为煤炭企业在发生交易者质量纠纷情况下合理划分质量检验责任提供参考,监督和指导矿井和客户的质检管理工作;另一方面可以为煤炭企业提供销售决策辅助支持。     

On a Mixture GARCH Time‐Series Model

Abstract. Recently, there has been a lot of interest in modelling real data with a heavy‐tailed distribution. A popular candidate is the so‐called generalized autoregressive conditional heteroscedastic (GARCH) model. Unfortunately, the tails of GARCH models are not thick enough in some applications. In this paper, we propose a mixture generalized autoregressive conditional heteroscedastic (MGARCH) model. The stationarity conditions and the tail behaviour of the MGARCH model are studied. It is shown that MGARCH models have tails thicker than those of the associated GARCH models. Therefore, the MGARCH models are more capable of capturing the heavy‐tailed features in real data. Some real examples illustrate the results.     

融合动态层次聚类和邻域区间重组的蚁群算法

针对蚁群算法搜索速度过慢以及解质量不足等问题,提出一种融合动态层次聚类和邻域区间重组的蚁群算法。在初始阶段,调整层次聚类阈值并按照类间距离最小合并的原则迭代至目标簇集,根据预合并系数进行簇间合并,通过蚁群系统得到小类路径并断开重组以加快算法整体收敛速度;接着使用蚁群系统对解空间进行优化,同时并行处理簇集与簇集邻域区间扩散重组,增加解的多样性,进一步固定迭代次数进行比较,若邻域区间重组解质量优于当前优化解则进行推荐处理,提高解的精度;当算法停滞时,引入调整因子降低各路径信息素之间差异以增强蚂蚁搜索能力,有助于算法跳出局部最优。实验结果表明,在面对大规模问题时,算法的精度在3%左右,该方法相比传统方法可以有效提高解的精度和收敛速度。     

基于数据分布的聚类联邦学习

联邦学习(federated learning)可以解决分布式机器学习中基于隐私保护的数据碎片化和数据隔离问题。在联邦学习系统中,各参与者节点合作训练模型,利用本地数据训练局部模型,并将训练好的局部模型上传到服务器节点进行聚合。在真实的应用环境中,各节点之间的数据分布往往具有很大差异,导致联邦学习模型精确度较低。为了解决非独立同分布数据对模型精确度的影响,利用不同节点之间数据分布的相似性,提出了一个聚类联邦学习框架。在Synthetic、CIFAR-10和FEMNIST标准数据集上进行了广泛实验。与其他联邦学习方法相比,基于数据分布的聚类联邦学习对模型的准确率有较大提升,且所需的计算量也更少。     

基于加权的无人机集群组网分簇算法

在无人机集群组网中,节点的高速移动会造成网络拓扑结构更新频繁,使网络管理变得更加复杂。分簇能够增大网络容量,实现空间资源的复用,是优化网络管理的有效手段之一。针对大规模、高速移动的环境进行了研究,提出了一种多参数加权分簇算法。该算法将最大速度相似度分簇算法中的分簇指标引入到加权分簇算法中,并且对链路保持率、节点度差、节点剩余能量进行改进,综合考虑这四种参数,通过加权组合的方式选举具有最大权重的网络节点作为簇头。仿真结果表明,该分簇算法不仅能够减少簇的数量和簇间切换率,提高分簇的稳定性,而且能够延长最小节点生存时间,改善网络的整体续航能力。     

Designing amino acid sequences to fold with good hydrophobic cores

We present two methods for designing amino acid sequences ofproteins that will fold to have good hydrophobic cores. Giventhe coordinates of the desired target protein or polymer structure,the methods generate sequences of hydrophobic (H) and polar(P) monomers that are intended to fold to these structures.One method designs hydrophobic inside, polar outside; the otherminimizes an energy function in a sequence evolution process.The sequences generated by these methods agree at the levelof 60–80% of the sequence positions in 20 proteins inthe Protein Data Bank. A major challenge in protein design isto create sequences that can fold uniquely, i.e. to a singleconformation rather than to many. While an earlier lattice-basedsequence evolution method was shown not to design unique folders,our method generates unique folders in lattice model tests.These methods may also be useful in designing other types offoldable polymer not based on amino acids     

Analysis of heregulin symmetry by weighted evolutionary tracing

Heregulins are members of the protein family of EGF-like growthand differentiation factors. The primary cell-surface targetsof heregulins are heterodimers of the EGF-receptor homolog HER2with either HER3 or HER4. We used a weighted evolutionary traceanalysis to identify structural features that distinguish theEGF-like domain (hrg) of heregulins from other members of theEGF family. In this analysis, each amino acid sequence is weightedaccording to its uniqueness and the variability in each positionis assigned by an amino acid substitution matrix. Conservedresidues in heregulin that are variable in other EGF-like domainsare considered possible specificity-conferring residues. Thisanalysis identifies two clusters of residues at the foot ofthe boot-shaped hrg domain. The residues in one cluster arerecruited from the N-terminus; those in the other are from the-loop region and show a weak sequence similarity to the N-terminalresidues at the opposite side of the boot. The remaining residueswith high conservation scores distribute themselves into thesetwo distinct surfaces on hrg. This pseudo-twofold symmetry andthe presence of two distinct interfaces may reflect the preferenceof hrg for heterodimeric versus homodimeric HER complexes.     

Discovering social structures of local influence by using tweetStimuli

Information diffusion in large-scale networks has been studied to identify the users influence. The influence has been targeted as a key feature either to reach large populations or influencing public opinion. Through the use of micro-blogs, such as Twitter, global influencers have been identified and ranked based on message propagation (retweets). In this paper, a new application is presented, which allows to find first and classify then the local influence on Twitter: who have influenced you and who have been influenced by you. Until now, social structures of tweets’ original authors that have been either retweeted or marked as favourites are unobservable. Throughout this application, these structures can be discovered and they reveal the existence of communities formed by users of similar profile (that are connected among them) interrelated with other similar profile users’ communities.     

Ensemble clustering by means of clustering embedding in vector spaces

Ensemble clustering is a recently evolving research direction in cluster analysis and has found several different application domains. In this work the complex ensemble clustering problem is reduced to the well-known Euclidean median problem by clustering embedding in vector spaces. The Euclidean median problem is solved by the Weiszfeld algorithm and an inverse transformation maps the Euclidean median back into the clustering domain. In the experiment study different evaluation strategies are considered. The proposed embedding strategy is compared to several state-of-art ensemble clustering algorithms and demonstrates superior performance.     

Continuous Levels‐of‐Detail and Visual Abstraction for Seamless Molecular Visualization

Molecular visualization is often challenged with rendering of large molecular structures in real time. We introduce a novel approach that enables us to show even large protein complexes. Our method is based on the level‐of‐detail concept, where we exploit three different abstractions combined in one visualization. Firstly, molecular surface abstraction exploits three different surfaces, solvent‐excluded surface (SES), Gaussian kernels and van der Waals spheres, combined as one surface by linear interpolation. Secondly, we introduce three shading abstraction levels and a method for creating seamless transitions between these representations. The SES representation with full shading and added contours stands in focus while on the other side a sphere representation of a cluster of atoms with constant shading and without contours provide the context. Thirdly, we propose a hierarchical abstraction based on a set of clusters formed on molecular atoms. All three abstraction models are driven by one importance function classifying the scene into the near‐, mid‐ and far‐field. Moreover, we introduce a methodology to render the entire molecule directly using the A‐buffer technique, which further improves the performance. The rendering performance is evaluated on series of molecules of varying atom counts.