An evolving model of online bipartite networks

Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike  , show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p$p$, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p$p$. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.     

Kernel spectral clustering with memory effect

Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, metabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowledge. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness. The latter allows the model to cluster the current data well and to be consistent with the recent history. We also propose new model selection criteria in order to carefully choose the hyper-parameters of our model, which is a crucial issue to achieve good performances. We successfully test the model on four toy problems and on a real world network. We also compare our model with Evolutionary Spectral Clustering, which is a state-of-the-art algorithm for community detection of evolving networks, illustrating that the kernel spectral clustering with memory effect can achieve better or equal performances.     

Comprehensive data analysis of femtosecond transient absorption spectra: A review

Nowadays, time-resolved spectroscopy data can be routinely and accurately collected in UV-vis femtosecond transient absorption spectroscopy. However, the data analysis strategy and the postulation of a physically valid model for this kind of measurements may be tackled with many different approaches ranging from pure soft-modeling (model-free) to hard-modeling, where the elaboration of a parametric spectro-temporal model may be required. This paper reviews methods that are used in practice for the analysis of femtosecond transient absorption spectroscopy data. Model-based methods, common in photochemistry, are revisited, and soft-modeling methods, which originate from the chemometrics field and that recently disseminated in the photo(bio)chemistry literature, are presented. These soft-modeling methods are designed to suit the intrinsic nature of the multivariate (or multi-way) measurement. Soft-modeling tools do not require a priori physical or mechanistic models to provide a decomposition of the data on the time and wavelength dimensions, the only requirement being that these two (or more) dimensions are separable. Additionally, Bayesian data analysis, which provides a probabilistic framework for data analysis, is considered in detail, since it allows uncertainty quantification and validation of the model selection step.     

Evolving chemometric models for predicting dynamic process parameters in viscose production

In viscose production, it is important to monitor three process parameters in order to assure a high quality of the final product: the concentrations of H₂SO₄, Na₂SO₄ and Z_nSO₄. During on-line production these process parameters usually show a quite high dynamics depending on the fiber type that is produced. Thus, conventional chemometric models, which are trained based on collected calibration spectra from Fourier transform near infrared (FT-NIR) measurements and kept fixed during the whole life-time of the on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of new on-line data. In this paper, we are demonstrating evolving chemometric models which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner. These models exploit the Takagi–Sugeno fuzzy model architecture, being able to model flexibly different degrees of non-linearities implicitly contained in the mapping between near infrared spectra (NIR) and reference values. Updating the inner structures is achieved by moving the position of already existing local regions and by evolving (increasing non-linearity) or merging (decreasing non-linearity) new local linear predictors on demand, which are guided by distance-based and similarity criteria. Gradual forgetting mechanisms may be integrated in order to out-date older learned relations and to account for more flexibility of the models. The results show that our approach is able to overcome the huge prediction errors produced by various state-of-the-art chemometric models. It achieves a high correlation between observed and predicted target values in the range of [0.95,0.98] over a 3 months period while keeping the relative error below the reference error value of 3%. In contrast, the off-line techniques achieved correlations below 0.5, ten times higher error rates and the more deteriorate, the more time passes by.     

Markov chain-based degree distributions of evolving networks

In this paper, we study a class of stochastic processes, called evolving network Markov chains, in evolving networks. Our approach is to transform the degree distribution problem of an evolving network to a corresponding problem of evolving network Markov chains. We investigate the evolving network Markov chains, thereby obtaining some exact formulas as well as a precise criterion for determining whether the steady degree distribution of the evolving network is a power-law or not. With this new method, we finally obtain a rigorous, exact and unified solution of the steady degree distribution of the evolving network.     

Relational Evolution of the Degrees of Freedom of Generally Covariant Quantum Theories

We study the classical and quantum dynamics of generally covariant theories with vanishing Hamiltonian and with a finite number of degrees of freedom. In particular, the geometric meaning of the full solution of the relational evolution of the degrees of freedom is displayed, which means the determination of the total number of evolving constants of motion required. Also a method to find evolving constants is proposed. The generalized Heisenberg picture needs M time variables, as opposed to the Heisenberg picture of standard quantum mechanics where one time variable t is enough. As an application, we study the parametrized harmonic oscillator and the SL(2, R) model with one physical degree of freedom that mimics the constraint structure of general relativity where a Schrödinger equation emerges in its quantum dynamics.     

渐进因子分析－pH－分光法测定多元酸的离解常数

根据多元酸平衡体系中型体具有先出现先消失的特点,提出了用渐进因子分析与化学平衡结合迭代,从pH-分光矩阵中求解多元酸离解常数的新方法。用该法测定了多元酸茜素S和铬天青S的离解常数,并讨论了确定型体存在区域的经验式。结果表明该方法稳定、可靠、算法收敛快,所测得的离解常数与文献值吻合。     

Evolving model of weighted networks inspired by scientific collaboration networks

Inspired by scientific collaboration networks (SCN), especially our empirical analysis of econophysicists network, an evolutionary model for weighted networks is proposed. Besides a new vertex added in at every time step, old vertices can also attempt to build up new links, or to reconnect the existing links. The number of connections repeated between two nodes is converted into the weight of the link. This provides a natural way for the evolution of link weight. The path-dependent preferential attachment mechanism with local information is also introduced. It increases the clustering coefficient of the network significantly. The model shows the scale-free phenomena in degree and vertex weight distribution. It also gives well qualitatively consistent behavior with the empirical results.     

Determination of the volatile chemical constituents of Notoptergium incium by gas chromatography-mass spectrometry and iterative or non-iterative chemometrics resolution methods

The qualitative and quantitative determination of the chemical constitutes in traditional Chinese medicine (TCM) is an important task, which builds the foundation of the theory of pharmacological activity. The hyphenated chromatography instruments combined with the related chemometric methods provide powerful tools for the resolution of such complex systems. The familiar chemometrics methods can be roughly divided into two different kinds, the iterative one such as orthogonal projection approach (OPA) and non-iterative one representing by evolving window orthogonal projection (EWOP). One can use different kinds of methods according to overlapping condition, and then the measured data matrix can be resolved into pure concentration profiles and mass spectra of the chemical components with relative high efficiency and acceptable accuracy. One kind of TCM, named Notoptergium incium (NI) was analyzed by gas chromatography-mass spectrometry (GC-MS) and resolved by above chemometric approach. Experiment results show the efficiency and convenience of the proposed approach. 65 of the 98 separated constituents in essential oil, accounting for 92.13%, were identified by mass spectroscopy (MS).     

化学计量学和红外光谱研究3,5-二氨基-1,2,4-三唑合成机理

通过红外光谱在线监测3,5-二氨基-1,2,4-三唑(DAT)的合成过程,应用多元曲线分辨-交替最小二乘法(MCR-ALS)对过程中获得的光谱数据矩阵进行解析,推导出合理的反应机理.反应以水为背景,依次加入二氰二胺与二盐酸肼固体,用红外探头监测反应全过程.用MCR-ALS法对所得光谱数据进行解析,得到反应物、中间体和产...