Boosted leave-many-out cross-validation: the effect of training and test set diversity on PLS statistics

It is becoming increasingly common in quantitative structure/activity relationship (QSAR) analyses to use external test sets to evaluate the likely stability and predictivity of the models obtained. In some cases, such as those involving variable selection, an internal test set – i.e., a cross-validation set – is also used. Care is sometimes taken to ensure that the subsets used exhibit response and/or property distributions similar to those of the data set as a whole, but more often the individual observations are simply assigned `at random.' In the special case of MLR without variable selection, it can be analytically demonstrated that this strategy is inferior to others. Most particularly, D-optimal design performs better if the form of the regression equation is known and the variables involved are well behaved. This report introduces an alternative, non-parametric approach termed `boosted leave-many-out' (boosted LMO) cross-validation. In this method, relatively small training sets are chosen by applying optimizable k-dissimilarity selection (OptiSim) using a small subsample size (k = 4, in this case), with the unselected observations being reserved as a test set for the corresponding reduced model. Predictive errors for the full model are then estimated by aggregating results over several such analyses. The countervailing effects of training and test set size, diversity, and representativeness on PLS model statistics are described for CoMFA analysis of a large data set of COX2 inhibitors.     

Disentangling the Information in Species Interaction Networks

Shannon’s entropy measure is a popular means for quantifying ecological diversity. We explore how one can use information-theoretic measures (that are often called indices in ecology) on joint ensembles to study the diversity of species interaction networks. We leverage the little-known balance equation to decompose the network information into three components describing the species abundance, specificity, and redundancy. This balance reveals that there exists a fundamental trade-off between these components. The decomposition can be straightforwardly extended to analyse networks through time as well as space, leading to the corresponding notions for alpha, beta, and gamma diversity. Our work aims to provide an accessible introduction for ecologists. To this end, we illustrate the interpretation of the components on numerous real networks. The corresponding code is made available to the community in the specialised Julia package EcologicalNetworks.jl.     

Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the processing of complex signals, enhance the control of complex systems, increase the performance of optimization, and even extend the enabling of the potential for creativity. In this article, the authors discuss the fractional dynamics, FOT and rich fractional stochastic models. First, the use of fractional dynamics in big data analytics for quantifying big data variability stemming from the generation of complex systems is justified. Second, we show why fractional dynamics is needed in machine learning and optimal randomness when asking: “is there a more optimal way to optimize?”. Third, an optimal randomness case study for a stochastic configuration network (SCN) machine-learning method with heavy-tailed distributions is discussed. Finally, views on big data and (physics-informed) machine learning with fractional dynamics for future research are presented with concluding remarks.     

On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius

We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by considering a variational definition with respect to a generic mean, thereby extending the notion of Sibson’s information radius. The variational definition applies to any arbitrary distance and yields a new way to define a Jensen-Shannon symmetrization of distances. When the variational optimization is further constrained to belong to prescribed families of probability measures, we get relative Jensen-Shannon divergences and their equivalent Jensen-Shannon symmetrizations of distances that generalize the concept of information projections. Finally, we touch upon applications of these variational Jensen-Shannon divergences and diversity indices to clustering and quantization tasks of probability measures, including statistical mixtures.     

跨层级性别多样性变量在工作满意度和离职意图之间的调节作用

本研究在国外研究的基础上,将不同职务层级的性别多样性变量引入离职意图的前因变量模型中,并检验了多样性变量对于工作满意度和离职意图之间的调节效应。在对298个样本做回归分析后,发现了高层管理者和中层管理者性别多样性变量对离职意图的显著影响作用,以及基层管理者和上级性别多样性变量对于工作满意度和离职意图的调节作用。     

呼图壁种牛场草原生态站鼠类群落初步研究

本文根据１９９５年７￣８月的调查资料并采用聚类分析方法对呼图壁种牛场草原生态站鼠类群落及其演替进行了分析研究。结果表明：该地区的鼠类可划分为２种群落，灰仓鼠＋小家鼠为主的农田居民点鼠类群落，五趾跳鼠＋根田鼠为主的荒漠草原鼠类群落，种的多样性和生态环境的差异有关，随着人类经济活动的增加，经营时间的不同，鼠类群落受到的影响程度也不一样，在荒漠向人工环境转化过程中存在于其中的鼠类也呈现有规律的演替现象。     

短波FH／MFSK编码系统中部分频带单音阻塞的性能

研究了带反馈链路的选择式频率分集的短波信道ＦＨ／ＭＦＳＫ编码系统，在具有加性白高斯噪声（ＡＷＧＮ）的瑞利衰落信道中，存在部分频带单音阻塞时的性能，并给出了采用ＲＳ码、双－ｋ码、重发码时，无编码与有编码系统的码符差错率与比特能量噪声比的关系曲线．     

Highly Stereoselective Synthesis of Natural‐Product‐Like Hybrids by an Organocatalytic/Multicomponent Reaction Sequence

In an endeavor to provide an efficient route to natural product hybrids, described herein is an efficient, highly stereoselective, one‐pot process comprising an organocatalytic conjugate addition of 1,3‐dicarbonyls to α,β‐unsaturated aldehydes followed by an intramolecular isocyanide‐based multicomponent reaction. This approach enables the rapid assembly of complex natural product hybrids including up to four different molecular fragments, such as hydroquinolinone, chromene, piperidine, peptide, lipid, and glycoside moieties. The strategy combines the stereocontrol of organocatalysis with the diversity‐generating character of multicomponent reactions, thus leading to structurally unique peptidomimetics integrating heterocyclic, lipidic, and sugar moieties.