Preservation of long range temporal correlations under extreme random dilution

Many natural time series exhibit long range temporal correlations that may be characterized by power-law scaling exponents. However, in many cases, the time series have uneven time intervals due to, for example, missing data points, noisy data, and outliers. Here we study the effect of randomly missing data points on the power-law scaling exponents of time series that are long range temporally correlated. The Fourier transform and detrended fluctuation analysis (DFA) techniques are used for scaling exponent estimation. We find that even under extreme dilution of more than 50%, the value of the scaling exponent remains almost unaffected. Random dilution is also applied on heart interbeat interval time series. It is found that dilution of 70%-80% of the data points leads to a reduction of only 8% in the scaling exponent; it is also found that it is possible to discriminate between healthy and heart failure subjects even under extreme dilution of more than 90%.     

DFA化简算法的一种改进方法

在深入研究DFA化简基本原理的基础上,首先给出现有分析算法（分割法）的形式化算法描述,然后证明了该算法存在着漏洞,并给出相应的解决方法。最后,借鉴时序逻辑门电路中原始状态表化简的方法,对现有分割算法进行修改,提出一种新的适用于任何DFA的化简算法——K次划分法。     

Breathing of voltage dependent anion channel as revealed by the fractal property of its gating

The gating of voltage dependent anion channel (VDAC) depends on the movement of voltage sensors in the transmembrane region, but the actual mechanism is still not well understood. With a view to understand the phenomenon we have analyzed the current recordings of VDAC in lipid bilayer membrane (BLM) and found that the data show self-similarity and fractal characteristics. We look for the microscopic and molecular basis of fractal behavior of gating of VDAC. A model describing the oscillatory dynamics of voltage sensors of VDAC in the transmembrane region under applied potential has been proposed which gives rise to the aforesaid fractal behavior.     

Merging two traditional methods: the Hartree–Fock and the Heitler–London and adding density functional correlation corrections

The variational combination of the Hartree– Fock (HF) with the Heitler–London (HL) methods, yielding the Hartree–Fock–Heitler–London (HF–HL) method is analyzed for diatomic hydrides and homonuclear molecules of the first and second row atoms. This recent quantum chemistry development is considered in the light of the continuous evolution of hypotheses and corresponding verifications occurring in quantum chemistry. The correlation energy correction needed in HF and HL computations is reduced in the HF–HL method to its dynamic component, since the non-dynamical correlation is accounted by explicitly considering near degeneracy and state crossing. The dynamical correlation is computed either (1) via expansion with multi-configuration of HF and HL functions or (2) including ionic HL structures, yielding a large fraction of the molecular extra correlation energy or (3) using the Coulomb hole density functional. With the latter the computed total and binding energies for all internuclear separations are in excellent agreement with laboratory data. Contribution to the Serafin Fraga Memorial Issue.     

ToF-SIMS studies of Bacillus using multivariate analysis with possible identification and taxonomic applications

In this paper we discuss the application of ToF-SIMS with an Au₃⁺ primary ion beam, combined with principal components analysis (PCA) and discriminant function analysis (DFA) for the identification of individual strains of two Bacillus species. The ToF-SIMS PC-DFA methodology is capable of distinguishing bacteria at the strain level based on analysis of surface chemical species. By classifying the data using hierarchical cluster analysis (HCA) we are able to show quantitative separation of species and of these strains. This has taxonomic implications in the areas of rapid identification of pathogenic microbes isolated from the clinic, food and environment.     

Real-time fractal signal processing in the time domain

Fractal analysis has proven useful for the quantitative characterization of complex time series by scale-free statistical measures in various applications. The analysis has commonly been done offline with the signal being resident in memory in full length, and the processing carried out in several distinct passes. However, in many relevant applications, such as monitoring or forecasting, algorithms are needed to capture changes in the fractal measure real-time. Here we introduce real-time variants of the Detrended Fluctuation Analysis (DFA) and the closely related Signal Summation Conversion (SSC) methods, which are suitable to estimate the fractal exponent in one pass. Compared to offline algorithms, the precision is the same, the memory requirement is significantly lower, and the execution time depends on the same factors but with different rates. Our tests show that dynamic changes in the fractal parameter can be efficiently detected. We demonstrate the applicability of our real-time methods on signals of cerebral hemodynamics acquired during open-heart surgery.     

面向品牌认知的产品形态推演方法

指出了体验经济时期品牌识别对于企业经营的重要性以及产品系列延伸策略的普遍性。针对品牌视觉认知,采用Design Formation Analysis(DFA)方法提取品牌识别的形态隐式和显式特征。在此基础上提出了基于形态推演法则的形状文法,并通过此进行产品形态推演。最后通过吸油烟机形态推演实例验证了方法的可行性。     

一种基于语法扩展的自动互斥锁实现机制

针对多道程序开发过程中存在的互斥锁标准不统一、使用复杂、易造成死锁等不足,提出扩展C/C++标准语法,增加临界资源定义及操作关键字的方案,通过隐藏互斥锁的概念及实现从而降低开发复杂度、实现平台无关.在扩展语法基础上,提出采用分支栈检查算法在代码进行临界资源访问顺序合法化及配对完整性检查,可有效地减少死锁隐患、增强系统的可靠性.     

Breaks or long memory behavior: An empirical investigation

Are structural break models true switching models or long memory processes? The answer to this question remains ambiguous. In recent years, many papers have dealt with this problem. Some studies have shown that, under specific conditions, switching models and long memory processes can easily be confused. In this paper, using several generating models (the mean-plus-noise model, the stochastic permanent break model, the Markov switching model, the threshold autoregressive (TAR) model, the sign model, and the structural change model) and several estimation techniques (the Geweke–Porter–Hudak (GPH) technique, detrended fluctuation analysis (DFA), the exact local Whittle (ELW) method, and wavelet methods) we show that, even if the answer is quite simple in some cases, it can be mitigated in other cases. Using French and American inflation rates, we found that the most appropriate process that takes into account the important features of these series is a model that simultaneously combines changes in regimes and long memory behavior. The main result of this study indicates that estimating a long memory parameter without taking into account the presence of breaks in the data sets may lead to misspecification and hence to overestimating the true parameter.