In this paper, we investigated the effectiveness of price limit on stock market with correlation study and complex network technology. We proposed a time-migrated DCCA cross-correlation coefficient which is beneficial to detect asynchronous correlations nonstationary time series. The constructed threshold method based DCCA. during crash period studied network. results indicate that ensures more...

We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn complex nonlinear transformations of two views of data such that the resulting representations are highly linearly correlated. Parameters of both transformations are jointly learned to maximize the (regularized) total correlation. It can be viewed as a nonlinear extension of the linear method canonical correlation analys...

Phenoxyalkanoic acid degradation is well studied in Beta- and Gammaproteobacteria, but the genetic background has not been elucidated so far in Alphaproteobacteria. We report the isolation of several genes involved in dichlor- and mecoprop degradation from the alphaproteobacterium Sphingomonas herbicidovorans MH and propose that the degradation proceeds analogously to that previously reported f...

In this study, we first build two empirical cross-correlationmatrices in the US stockmarket by two different methods, namely the Pearson’s correlation coefficient and the detrended cross-correlation coefficient (DCCA coefficient). Then, combining the twomatriceswith the method of random matrix theory (RMT), we mainly investigate the statistical properties of cross-correlations in the US stock m...

The computation of the bivariate Hurst exponent constitutes an important technique to test power-law cross-correlation time series. For this objective, detrended analysis (DCCA) method represents most used one. In paper, we prove robustness DCCA method, where trend is estimated using polynomial fitting, estimate when series are corrupted by outliers observations. On other hand, give exact order...

BACKGROUND Several studies have indicated that estrogen may prime inflammatory and nociceptive pathways, leading to symptoms that mimic cholecystitis. We set out to confirm the relation between recent estrogen use and cholecystectomy in postmenopausal women and to test the novel hypothesis that a similar relation exists for appendectomy. METHODS We developed a retrospective cohort using presc...

Pyrethroid insecticides, the most commonly used insecticides worldwide, are suspected endocrine-disrupting chemicals. But their interactions with hormone receptors are still unclear. The present study intended to evaluate and compare the hormone receptor (estrogen receptor [ER], androgen receptor [AR], and thyroid hormone receptor [TR]) activities of nine pyrethroids (cycloprothrin, cyfluthrin,...

Discriminative canonical correlation analysis (DCCA) is a powerful supervised feature extraction technique for two sets of multivariate data, which has wide applications in pattern recognition. DCCA consists parts: (i) mean-centering that subtracts the sample mean from and (ii) solving generalized eigenvalue problem. The cost expensive when dealing with large number high-dimensional samples. To...

Stochastic local search (SLS) algorithms have shown effectiveness on satisfiable instances of the Boolean satisfiability (SAT) problem. However, their performance is still unsatisfactory on random k-SAT at the phase transition, which is of significance and is one of the empirically hardest distributions of SAT instances. In this paper, we propose a new heuristic called DCCA, which combines two ...

Multimodal recognition is an emerging technique to overcome the non-robustness of the unimodal recognition in real applications. Canonical correlation analysis (CCA) has been employed as a powerful tool for feature fusion in the realization of such multimodal system. However, CCA is the unsupervised feature extraction and it does not utilize the class information of the samples, resulting in th...