Moment estimator for an AR(1) model with non-zero mean driven by a long memory Gaussian noise

In this paper, we consider an inference problem for the first order autoregressive process with non-zero mean driven by a long memory stationary Gaussian process. Suppose that the covariance function of the noise can be expressed as $|k|^{2H-2}$ times a positive constant when $k$ tends to infinity, and the fractional Gaussian noise and the fractional ARIMA model are special examples that satisfy this assumption. We propose moment estimators and prove the strong consistency, the asymptotic normality and joint asymptotic normality

Optimisation par essaim de particules application au clustering des données de grandes dimensions

Clustering high-dimensional data is an important but difficult task in various data mining applications. A fundamental starting point for data mining is the assumption that a data object, such as text document, can be represented as a high-dimensional feature vector. Traditional clustering algorithms struggle with high-dimensional data because the quality of results deteriorates due to the curse of dimensionality. As the number of features increases, data becomes very sparse and distance measures in the whole feature space become meaningless. Usually, in a high-dimensional data set, some features may be irrelevant or redundant for clusters and different sets of features may be relevant for different clusters. Thus, clusters can often be found in different feature subsets rather than the whole feature space. Clustering for such data sets is called subspace clustering or projected clustering, aimed at finding clusters from different feature subspaces. On the other hand, the performance of many subspace/projected clustering algorithms drops quickly with the size of the subspaces in which the clusters are found. Also, many of them require domain knowledge provided by the user to help select and tune their settings, like the maximum distance between dimensional values, the threshold of input parameters and the minimum density, which are difficult to set. Developing effective particle swarm optimization (PSO) for clustering high-dimensional data is the main focus of this thesis. First, in order to improve the performance of the conventional PSO algorithm, we analyze the main causes of the premature convergence and propose a novel PSO algorithm, call InformPSO, based on principles of adaptive diffusion and hybrid mutation. Inspired by the physics of information diffusion, we design a function to achieve a better particle diversity, by taking into account their distribution and the number of evolutionary generations and by adjusting their"social cognitive" abilities. Based on genetic self-organization and chaos evolution, we build clonal selection into InformPSO to implement local evolution of the best particle candidate, gBest, and make use of a Logistic sequence to control the random drift of gBest. These techniques greatly contribute to breaking away from local optima. The global convergence of the algorithm is proved using the theorem of Markov chain. Experiments on optimization of unimodal and multimodal benchmark functions show that, comparing with some other PSO variants, InformPSO converges faster, results in better optima, is more robust, and prevents more effectively the premature convergence. Then, special treatments of objective functions and encoding schemes are proposed to tailor PSO for two problems commonly encountered in studies related to high-dimensional data clustering. The first problem is the variable weighting problem in soft projected clustering with known the number of clusters k . With presetting the number of clusters k, the problem aims at finding a set of variable weights for each cluster and is formulated as a nonlinear continuous optimization problem subjected to bound. constraints. A new algorithm, called PSOVW, is proposed to achieve optimal variable weights for clusters. In PSOVW, we design a suitable k -means objective weighting function, in which a change of variable weights is exponentially reflected. We also transform the original constrained variable weighting problem into a problem with bound constraints, using a non-normalized representation of variable weights, and we utilize a particle swarm optimizer to minimize the objective function in order to obtain global optima to the variable weighting problem in clustering. Our experimental results on both synthetic and real data show that the proposed algorithm greatly improves cluster quality. In addition, the results of the new algorithm are much less dependent on the initial cluster centroids. The latter problem aims at automatically determining the number of clusters k as well as identifying clusters. Also, it is formulated as a nonlinear optimization problem with bound constraints. For the problem of automatical determination of k , which is troublesome to most clustering algorithms, a PSO algorithm called autoPSO is proposed. A special coding of particles is introduced into autoPSO to represent partitions with different numbers of clusters in the same population. The DB index is employed as the objective function to measure the quality of partitions with similar or different numbers of clusters. autoPSO is carried out on both synthetic high-dimensional datasets and handcrafted low-dimensional datasets and its performance is compared to other selected clustering techniques. Experimental results indicate that the promising potential pertaining to autoPSO applicability to clustering high-dimensional data without the preset number of clusters k

Analysis on the Business Model of Fresh E-commerce------Taking Hema Supermarket as an Example

Enterprises are beginning to involve the fresh produce industry, but most companies have withdrawn from the fresh produce industry due to poor performance. This shows that there are many problems with e-commerce of fresh produce. In particular, the business model of e-commerce for fresh produce is a major factor constraining its development. This article takes Hema Supermarket as an example to analyze its business model. It summarizes the areas that can be used for product control, power distribution system construction, platform operation, etc., and provides reference and reference for the operation of fresh agricultural products

Birth Weight, Genetic Susceptibility, and Adulthood Risk of Type 2 Diabetes

OBJECTIVE Both stressful intrauterine milieus and genetic susceptibility have been linked to later-life diabetes risk. The current study aims to examine the interaction between low birth weight, a surrogate measure of stressful intrauterine milieus, and genetic susceptibility in relation to risk of type 2 diabetes in adulthood. RESEARCH DESIGN AND METHODS The analysis included two independent, nested case-control studies of 2,591 type 2 diabetic case subjects and 3,052 healthy control subjects. We developed two genotype scores: an obesity genotype score based on 32 BMI-predisposing variants and a diabetes genotype score based on 35 diabetes-predisposing variants. RESULTS Obesity genotype scores showed a stronger association with type 2 diabetes risk in individuals with low birth weight. In low–birth weight individuals, the multivariable-adjusted odds ratio (OR) was 2.55 (95% CI 1.34–4.84) by comparing extreme quartiles of the obesity genotype score, while the OR was 1.27 (1.04–1.55) among individuals with birth weight >2.5 kg (P for interaction = 0.017). We did not observe significant interaction between diabetes genotype scores and birth weight with regard to risk of type 2 diabetes. In a comparison of extreme quartiles of the diabetes gene score, the multivariable-adjusted OR was 3.80 (1.76–8.24) among individuals with low birth weight and 2.27 (1.82–2.83) among those with high birth weight (P for interaction = 0.16). CONCLUSIONS Our data suggest that low birth weight and genetic susceptibility to obesity may synergistically affect adulthood risk of type 2 diabetes

Engineering Clostridium Strain to Accept Unmethylated DNA

It is difficult to genetically manipulate the medically and biotechnologically important genus Clostridium due to the existence of the restriction and modification (RM) systems. We identified and engineered the RM system of a model clostridial species, C. acetobutylicum, with the aim to allow the host to accept the unmethylated DNA efficiently. A gene CAC1502 putatively encoding the type II restriction endonuclease Cac824I was identified from the genome of C. acetobutylicum DSM1731, and disrupted using the ClosTron system based on group II intron insertion. The resulting strain SMB009 lost the type II restriction endonuclease activity, and can be transformed with unmethylated DNA as efficiently as with methylated DNA. The strategy reported here makes it easy to genetically modify the clostridial species using unmethylated DNA, which will help to advance the understanding of the clostridial physiology from the molecular level

Time-delay signature suppression in a chaotic semiconductor laser by fiber random grating induced distributed feedback

We demonstrate that a semiconductor laser perturbed by the distributed feedback from a fiber random grating can emit light chaotically without the time delay signature. A theoretical model is developed based on the Lang-Kobayashi model in order to numerically explore the chaotic dynamics of the laser diode subjected to the random distributed feedback. It is predicted that the random distributed feedback is superior to the single reflection feedback in suppressing the time-delay signature. In experiments, a massive number of feedbacks with randomly varied time delays induced by a fiber random grating introduce large numbers of external cavity modes into the semiconductor laser, leading to the high dimension of chaotic dynamics and thus the concealment of the time delay signature. The obtained time delay signature with the maximum suppression is 0.0088, which is the smallest to date

Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction

This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization (EDIW-PSO). Based on the inertia weight decreasing strategy, we propose a new Exponential Decreasing Inertia Weight (EDIW) to improve the PSO algorithm. We use the modified EDIW-PSO algorithm to determine the centers, widths, and connection weights of RBF neural network. To assess the performance of the proposed EDIW-PSO-RBF model, we choose the daily air quality index (AQI) of Xi’an for prediction and obtain improved results

Synergistic Effects of Serum Uric Acid and Cardiometabolic Risk Factors on Early Stage Atherosclerosis: The Cardiometabolic Risk in Chinese Study

Objective: To comprehensively examine the associations of serum uric acid (SUA) with central and peripheral arterial stiffness in Chinese adults, and particularly assess the interactions between SUA and other cardiometabolic risk factors. Methods: The study included 3,772 Chinese men and women with carotid radial pulse wave velocity (crPWV), carotid femoral PWV (cfPWV), carotid artery dorsalis pedis PWV (cdPWV) and SUA measured. Results: After adjustment for age, sex, and BMI, the levels of SUA were significantly associated with increasing trend of cfPWV, crPWV and cdPWV (P for trend <0.0001). Further adjustment for heart rate (HR), blood pressure (BP) and lipids attenuated the associations with crPWV and cdPWV to be non-significant (P = 0.1, P = 0.099 respectively), but the association between SUV and cfPWV remained significant (P = 0.004). We found significant interactions between SUA and HR or BP in relation to cfPWV (P for interaction = 0.03, 0.003 respectively). The associations between SUA and cfPWV were more evident among individuals with higher HR or normal BP than those with lower HR or hypertension. Conclusions: SUA was associated with elevated aortic arterial stiffness in Chinese adults, independent of conventional cardiovascular risk factors. BP and HR might modify the deleterious effects of SUA