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Meiotic recombination plays an important role in the process of genetic evolution. Previous researches have shown that the recombination rates provide important information about the mechanism of recombination study. However, at present, most methods ignore the hidden correlation and spatial autocorrelation of the DNA sequence. In this study, we proposed a predictor called iRSpot-DTS to identify hot/cold spots based on the benchmark datasets. We proposed a feature extraction method called dinucleotide-based spatial autocorrelation(DSA) which can incorporate the original DNA properties and spatial information of DNA sequence. Then it used t-SNE method to remove the noise which outperformed PCA. Finally, we used SAE softmax classifier to do classification which is based on networks and can get more hidden information of DNA sequence, our iRSpot-DTS achieved remarkable performance. Jackknife cross validation tests were done on two benchmark datasets. We achieved state-of-the-art results with 96.61% overall accuracy(OA), 93.16% Matthews correlation coefficient (MCC) and over 95% in Sn and Sp which are the best in this state.

The practice of inertial sensor calibration has commonly been carried out by taking into account the deterministic and stochastic components of the error measurements issued from a calibration session. Once the deterministic components have been taken into account through physical models, the remaining stochastic component has always been dealt with for each sensor separately. The latter process involves estimating complex probabilistic models for each sensor which has been proven to be extremely complicated over the past years, although recent proposals have allowed to overcome most of the limitations that have characterized this task. However, the separate stochastic calibration of the individual sensors composing an inertial measurement unit may not be wise in many cases since there can be considerable degrees of dependence between the sensors, especially between the gyroscopes. For this reason, there has been growing attention towards this issue in order to consider the influence of the stochastic behaviour of the sensors on each other, with few proposals that address this problem. Among these proposals there has been the idea of integrating the information coming from the different gyroscopes so as to build a virtual gyroscope. In this paper we build on this idea and, using a recently proposed method for multivariate signal modelling, we deliver a general and flexible framework that allows to consider many different modelling options which provide the basis to construct a virtual sensor that optimally combines the information from the individual sensors and considerably improves navigation accuracy.

The atomic norm minimization (ANM) has been successfully incorporated into the two-dimensional (2-D) direction-of-arrival (DOA) estimation problem for super-resolution. However, its computational workload might be unaffordable when the number of snapshots is large. In this paper, we propose two gridless methods for 2-D DOA estimation with L-shaped array based on the atomic norm to improve the computational efficiency. Firstly, by exploiting the cross-covariance matrix an ANM-based model has been proposed. We then prove that this model can be efficiently solved as a semi-definite programming (SDP). Secondly, a modified model has been presented to improve the estimation accuracy. It is shown that our proposed methods can be applied to both uniform and sparse L-shaped arrays and do not require any knowledge of the number of sources. Furthermore, since our methods greatly reduce the model size as compared to the conventional ANM method, and thus are much more efficient. Simulations results are provided to demonstrate the advantage of our methods.

Display Omitted Prediction performance of protein structural class has been improved.A high-quality feature extraction technique has been designed.A recursive feature selection has been used to reduce feature abundance. Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets.

This article introduces three types of covariance matrix structures for Gaussian or elliptically contoured vector random fields in space and/or time, which include fractional, bifractional, and trifractional vector Brownian motions as special cases, and reveals the relationships among these vector random fields, with an orthogonal decomposition established for the multifractional vector Brownian motion.

Graphical abstractDisplay Omitted HighlightsAn algorithm reduces the redundancy among row and column vectors of all images.In this algorithm, the cross-covariance matrix (CCM) is calculated.CCM completely preserves the covariance information of PCA between local geometric structures in the image matrix which is partially maintained in 2DPCA and its variants.A new grouping strategy that reduces the intra-group correlation is proposed.Compared to 2DPCA this algorithm is more robust to changes in lighting conditions. Grouping strategy exactly specifies the form of covariance matrix, therefore it is very essential. Most 2DPCA methods use the original 2D image matrices to form the covariance matrix which actually means that the strategy is to group the random variables by row or column of the input image. Because of their grouping strategies these methods have two main drawbacks. Firstly, 2DPCA and some of its variants such as A2DPCA, DiaPCA and MatPCA preserve only the covariance information between the elements of these groups. This directly implies that 2DPCA and these variants eliminate some covariance information while PCA preserves such information that can be useful for recognition. Secondly, all the existing methods suffer from the relatively high intra-group correlation, since the random variables in a row, column, or a block are closely located and highly correlated. To overcome such drawbacks we propose a novel grouping strategy named cross grouping strategy. The algorithm focuses on reducing the redundancy among the row and the column vectors of the image matrix. While doing this the algorithm completely preserves the covariance information of PCA between local geometric structures in the image matrix which is partially maintained in 2DPCA and its variants. And also in the proposed study intra-group correlation is weak according to the 2DPCA and its variants because the random variables spread over the whole face image. These make the proposed algorithm superior to 2DPCA and its variants. In order to achieve this, image cross-covariance matrix is calculated from the summation of the outer products of the column and the row vectors of all images. The singular value decomposition (SVD) is then applied to the image cross-covariance matrix. The right and the left singular vectors of SVD of the image cross-covariance matrix are used as the optimal projective vectors. Further in order to reduce the dimension LDA is applied on the feature space of the proposed method that is proposed method+LDA. The exhaustive experimental results demonstrate that proposed grouping strategy for 2DPCA is superior to 2DPCA, its specified variants and PCA, and proposed method outperforms bi-directional PCA+LDA.

This series of papers addresses the transport of reactive solutes in groundwater. In part 1, the time-dependent effective retardation factor, R∼eff(t), of reactive solutes undergoing equilibrium sorption is linked to hierarchical stratal architecture using a Lagrangian-based transport model. The model is based on hierarchical expressions of the spatial covariance of the log distribution coefficient, Ξ=ln⁡(Kd), and the spatial cross covariance between Ξ and the log permeability, Y=ln⁡(k). The spatial correlation structure in these covariance expressions is the probability of transitioning across strata types of different scales, and they are parameterized by independent and quantifiable physical attributes of sedimentary architecture including univariate statistics for Y, Ξ, and the proportions and lengths of facies. Nothing is assumed about Y- Ξ point correlation; it is allowed to differ by facies type. The duration of the time-dependent change in R∼eff(t) is a function of the effective ranges of the cross-transition probability structures (i.e., the ranges of indicator correlation structures) for each scale of stratal architecture. The plume velocity and the effective retardation stabilize at a large-time limit after the plume centroid has traveled a distance that encompasses the effective ranges of these cross-transition probability structures. The well-documented perchloroethene (PCE) tracer test at the Borden research site is used to illustrate the model. The model gives a viable explanation for the observed PCE plume deceleration, and thus the observed R∼eff(t) can be explained by the process of linear equilibrium sorption and the heterogeneity in k and Kd. In part 2 [Soltanian et al., 2015a], reactive plume dispersion, as quantified by the particle displacement variance is linked to stratal architecture using a Lagrangian-based transport model.

This work investigates the problem of estimating directions of arrival (DOAs) of coherent sources without the left–right ambiguity by acoustic vector sensor linear arrays. Toeplitz matrices obtained from vector sensor cross covariance matrices are summed to obtain a new matrix with vector sensor directivities as weights. In this way, the proposed method restores the rank of the equivalent signal subspace and eliminates 2π ambiguous DOA information of sources. A robust technique is also developed to improve its performance degradation because of the velocity sensor gain uncertainty during unit conversion, such as imprecise measurements of the sensor sensitivities and the product of the water density and sound speed in the water. The methods can be implemented as a preprocessing stage before applying beamformers, such as multiple signal classification and MVDR. Theoretical analyses show that comparing with existing preprocessing techniques, the proposed methods can fully resolve the coherent sources without the left–right ambiguity while keeping the effective array aperture, and even applicable to the case of closely spaced coherent sources. Their effectiveness and performances are conducted via simulations.

The paper presents a decentralized fusion strategy based on the optimal unbiased finite impulse response (OUFIR) filter for discrete systems with correlated process and measurement noise. We extend OUFIR filter to apply in the model with control inputs. Taking it as local filters, cross covariance between any two is calculated; then it is expressed to the fast iterative form. Finally based on cross covariance, optimal weights are utilized to fuse local estimates and the overall outcome is obtained. The numerical examples show that the proposed filter exhibits better robustness against temporary modeling uncertainties than the fusion Kalman filter used commonly.

In this paper, a distributed estimation algorithm using Bayesian-based forward backward Kalman filter (KF) is proposed for stochastic singular linear systems. The method incorporates generalised versions of KF for bounded cases with complete and incomplete prior information, followed by estimation fusion of these cases. The incorporated filters remain optimal given the cross-covariance of the local estimates. The proposed approach is validated on a coupled-tank system.

Foreground detection in complex scenarios is a challenging task. In this work, we propose to detect foreground by incrementally learning a cross-covariance based subspace. In our method, we first introduce the cross-covariance based two dimensional principal component analysis (2DPCA) algorithm into foreground detection field for better background estimation. Then, we extend the conventional cross-covariance based 2DPCA algorithm into an incremental one, which helps to model background in an adaptive way. Moreover, we consider the sparse and the continuous characteristics of the foreground, and formulate them as a fused lasso problem. By adding the fused lasso regularization into the proposed subspace learning process, we integrate the background recovery and the foreground estimation into a single optimization framework. Finally, we design an efficient approximate algorithm which solves the optimization problem effectively. We compare our method with the state of the art methods on multiple challenging video sequences. The experimental results demonstrate the effectiveness and the advantages of the proposed method.

The Hoffmann reflex (H-wave) is produced by alpha-motoneuron activation in the spinal cord. A feature of this electromyography response is that it exhibits fluctuations in amplitude even during repetitive stimulation with the same intensity of current. We herein explore the hypothesis that physical training induces plastic changes in the motor system. Such changes are evaluated with the fractal dimension (FD) analysis of the H-wave amplitude-fluctuations (H-wave FD) and the cross-covariance (CCV) between the bilateral H-wave amplitudes. The aim of this study was to compare the H-wave FD as well as the CCV before and after track training in sedentary individuals and athletes. The training modality in all subjects consisted of running three times per week (for 13 weeks) in a concrete road of 5 km. Given the different physical condition of sedentary vs. athletes, the running time between sedentary and athletes was different. After training, the FD was significantly increased in sedentary individuals but significantly reduced in athletes, although there were no changes in spinal excitability in either group of subjects. Moreover, the CCV between bilateral H-waves exhibited a significant increase in athletes but not in sedentary individuals. These differential changes in the FD and CCV indicate that the plastic changes in the complexity of the H-wave amplitude fluctuations as well as the synaptic inputs to the Ia-motoneuron systems of both legs were correlated to the previous fitness history of the subjects. Furthermore, these findings demonstrate that the FD and CCV can be employed as indexes to study plastic changes in the human motor system.

In this paper, a novel 2D direction of arrival (DOA) estimation approach based on tensor technique for the conformal array is proposed. In order to improve the estimation performance with low SNR and small snapshots, a third-order measurement tensor is constructed by exploiting the multidimensional information of the conformal array received data. Then, the desired signal subspace is obtained by the higher-order singular value decomposition (HOSVD) of the cross covariance tensor. Finally, the DOAs could be estimated by the conventional subspace based algorithms. This approach enables us to get a better DOA estimation performance owing to utilize the multidimensional information and the cross covariance tensor method, especially in the case of low SNR and small number of snapshots. At last, the simulation results demonstrate the validity and the advantages of the proposed method.

In this paper, two dimensional angle estimation using a new array structure named separate nested acoustic vector sensor (AVS) array is studied. Unlike traditional AVS array, where the pressure sensor and velocity sensors are collocated in space, the separate nested AVS array has two separate and nested subarrays including the pressure sensor array and the velocity vector sensor array. With the extended vectorization of the cross covariance matrix between the two arrays, 2M2-1 degrees of freedom (DOF) can be achieved with only M physical acoustic vector sensors. After analyzing the fitting error, only one dimensional sparse representation is required to estimate the two dimensional angle with automatically pairing. Compared to conventional AVS array based methods, the proposed scheme can achieve better angle estimation performance and handle more sources. Simulation results verify the effectiveness of the proposed method.

This paper deals with a class of second-order vector random fields in the unit ball of Rd , whose direct/cross covariances are invariant or isotropic with respect to a distance defined on the ball, and gives a series representation of such an isotropic vector random field. A necessary format of covariance matrix functions is also derived for isotropic and mean square continuous vector random fields on the ball.

A decorrelation-based data association algorithm for heterogeneous sensors system is proposed, which first fusions multiple measurements to estimate a target position using the pseudo linear estimation method, then a decorrelation-based data association model is built and the unscented transform is proposed to compute the mutual covariance between measurements and the pseudo ones. Meanwhile, taking the real-time problem into consideration, when calculating the cross covariance, the minimum skew simplex sample is used to reduce the complexity of the algorithm.

The mobility and environmental impacts of toxic trace elements are regulated by their reactions with soils, which are complex heterogeneous mixtures of minerals and organic matter. We describe an experiment that maps the composition of elements on an individual soil sand grain using X-ray fluorescence microprobe analyses, after the grain is treated with arsenic solutions, resulting in multivariate spatial lattice maps of elemental abundance. To understand the behavior of arsenic in soils, it is important to disentangle the complex multivariate relationships among the elements in the sample. The abundance of most elements, including arsenic, correlates strongly with that of iron; but conditional on the amount of iron, some elements mitigate or potentiate the accumulation of arsenic. This problem motivates our work to define conditional correlation in spatial lattice models and give general conditions under which two components are conditionally uncorrelated given the rest. We describe how to enforce that two components are conditionally uncorrelated given a third in parametric models, which provides a basis for likelihood ratio tests for conditional correlation between arsenic and chromium given iron. We show how to apply our results to big datasets using the Whittle likelihood, and we demonstrate through simulation that tapering improves Whittle likelihood parameter estimates governing cross covariance.

Many spatial processes in environmental applications, such as climate variables and climate model errors on a global scale, exhibit complex nonstationary dependence structure, in not only their marginal covariance but also their cross-covariance. Flexible cross-covariance models for processes on a global scale are critical for an accurate description of each spatial process as well as the cross-dependences between them and also for improved predictions. We propose various ways to produce cross-covariance models, based on the Matern covariance model class, that are suitable for describing prominent nonstationary characteristics of the global processes. In particular, we seek nonstationary versions of Matern covariance models whose smoothness parameters vary over space, coupled with a differential operators approach for modeling large-scale nonstationarity. We compare their performance to the performance of some existing models in terms of the aic and spatial predictions in two applications: joint modeling of surface temperature and precipitation, and joint modeling of errors in climate model ensembles.

In this work, we investigate the signal transmission in a linear static system driven by correlated multiplicative and additive noises. When the input signal is periodic, we depict the stochastic resonance (SR) phenomenon by employing the signal-to-noise ratio (SNR) theory; while the input signal is aperiodic, we describe the SR phenomenon by using the input–output cross correlation theory. And the exact analytic expressions of the output SNR and the normalized time averaged cross covariance between input and output are obtained. The results show: under the condition of negative correlated noises, SR arises; while with positive correlated or uncorrelated noises, there is no SR. This result may extend the SR theory to a common linear static system.

The Expectation Maximization algorithm can be used to estimate Kalman filter parameter matrices. This requires smoother results and typically the Rauch-Tung-Striebel smoother is used for this purpose. If either of the state transition or plant noise matrices require estimation, a sequence of single time-step smoother cross-covariance matrices, based on the Rauch-Tung-Striebel smoother formulation, are also required. The modified Bryson-Frazier smoother could be used as the smoother for the Expectation Maximization algorithm, but to estimate the state transition or plant noise matrix, this smoother would first need a method to generate the sequence of single time-step smoother cross-covariance matrices based on the modified Bryson-Frazier formulation. This paper develops an expression for these modified Bryson-Frazier cross-covariance matrices, thus allowing the modified Bryson-Frazier smoother to be substituted for the Rauch-Tung-Striebel smoother in the Expectation Maximization algorithm. The modified Bryson-Frazier smoother formulation requires inversion only of filter innovation matrices, which are generally much smaller than state error covariance matrices, which allows both greater computational speed and better numerical accuracy due to smaller matrix size. This formulation can be used even when covariance and transition matrices are singular.

t has special meaning for drug design as well as basic research to study Antimicrobial peptides (AMPs) because they have been demonstrated to kill Gram negative and Gram positive bacteria, mycobacteria, enveloped viruses, fungi and even transformed or cancerous cells. In view of this, it is highly desired to develop an effective computational method for accurately predicting the functional types of AMPs because it can provide us with more candidates and useful insights for drug design. AMP functional recognition is in fact a multi-label classification problem. In this study, up to six kinds of physicochemical properties value are selected to code the AMP sequence as physical-chemical property matrix (PCM), and then auto and cross covariance transformation is performed to extract features from the PCM for AMP sequence expression; At last, a clever use of Fuzzy K nearest neighbor rule will help identify the multiple functions of a query AMP. As a result, the overall classification accuracy about 65% has been achieved through the rigorous Jackknife test on a newly constructed benchmark AMP dataset.

In a distributed estimation system, the local sensor estimates are transferred to fusion center and fused to be an optimal estimation according to some criterion. The well-known best linear unbiased estimation BLUE fusion optimally combines the local estimates by minimising the covariance matrix of estimation errors. However, the BLUE fusion need know the cross-covariances of sensor estimation errors exactly, which are not attainable in most of practical applications. In this article, the random matrix is employed to describe the uncertainty of the estimation error covariance among the sensors. By minimising the estimation error covariance only for the most favorable realisations of the random matrix, we model it as an optimisation problem with chance constraint. With appropriate relaxation method, a robust linear unbiased estimation fusion is proposed analytically. Furthermore, an upper bound on the relaxation gap is proposed. Finally, we demonstrate through some numerical simulations that the presented estimation fusion always has smaller absolute estimation error than that of the covariance intersection filter.

We establish the asymptotic normality of the sample principal components of functional stochastic processes under nonrestrictive assumptions which admit nonlinear functional time series models. We show that the aforementioned asymptotic depends only on the asymptotic normality of the sample covariance operator, and that the latter condition holds for weakly dependent functional time series which admit expansions as Bernoulli shifts. The weak dependence is quantified by the condition of L 4 -m-approximability which includes all functional time series models in practical use. We also demonstrate convergence of the cross covariance operators of the sample functional principal components to their counterparts in the normal limit. c

Models for multivariate space-time geostatistical data have received a growing interest in spatial and spatiotemporal epidemiology. However, specifying models that can capture associations within and among multivariate measurements is usually a challenge. The main goal of this paper is to introduce and review cross-covariance functions that are rich in structure and are computationally feasible. Integrated nested Laplace approximation combined with stochastic partial differential equations were used for inference and prediction, as a fast and precise alternative to the computationally intensive Markov chain Monte Carlo methods. A large set of models is considered in this paper: models assuming independent, shared or correlated spatial and temporal processes (with nine possible combinations), and models with independent, shared and linear models of coregionalization spatiotemporal processes. Different processes are applied to Culicoides data and compared. Bayesian spatial prediction results show that the central and Northeastern parts of Belgium had the highest prevalence of Culicoides in summer months and the lowest prevalence in winter months.

We studied 19-channel event-related potentials (ERPs) during the GO/NOGO task in 517 healthy subjects. Decomposition of multi-channel ERP into independent components was performed with a method based on modeling the cross-covariance structure of the transient response. The results show that this method allows us to obtain low-correlated components with acceptable reliability. The identified components have been associated with psychological processes such as the attended sensory mismatch operation, the decision-making on the subsequent action, the action inhibition operation, the conflict monitoring operation and others. In conclusion, the separation of the ERP recording into independent components and the use of the sLORETA helps localizing sources of ERPs more accurately than the conventional analysis of the ERP.

We present a numerical technique to visualize covariance and cross-covariance fields of a stochastic simulation. The method is local in the sense that it demonstrates the covariance structure of the solution at a point with its neighboring locations. When coupled with an efficient stochastic simulation solver, our framework allows one to effectively concurrently visualize both the mean and (cross-)covariance information for two-dimensional (spatial) simulation results. Most importantly, the visualization provides the scientist a means to identify the interesting correlation structure of the solution field. The mathematical setup is discussed, along with several examples to demonstrate the efficacy of this approach.

Nineteen-channel event-related potentials (ERPs) were studied in the GO/NOGO test in 517 healthy subjects. Multi-channel ERPs were decomposed into independent components by the method of modeling the cross-covariance structure of transient process. The research results showed that low-correlated ERP components could be obtained by this method with acceptable reliability. The identified components were associated with psychological processes, such as the attended sensory mismatch operation, decision making on the subsequent action, the action inhibition operation, and the conflict monitoring operation. In conclusion, decomposition of ERP recordings into independent components and the use of sLORETA help to localize ERP sources more accurately than the conventional ERP analysis.

Meiotic recombination presents an uneven distribution across the genome. Genomic regions that exhibit at relatively high frequencies of recombination are called hotspots, whereas those with relatively low frequencies of recombination are called coldspots. Therefore, hotspots and coldspots would provide useful information for the study of the mechanism of recombination. In this study, we proposed a computational predictor called iRSpot-DACC to predict hot/cold spots across the yeast genome. It combined Support Vector Machines (SVMs) and a feature called dinucleotide-based auto-cross covariance (DACC), which is able to incorporate the global sequence-order information and fifteen local DNA properties into the predictor. Combined with Principal Component Analysis (PCA), its performance was further improved. Experimental results on a benchmark dataset showed that iRSpot-DACC can achieve an accuracy of 82.7%, outperforming some highly related methods.

Background and objectives: Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. Methods: This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. Results: The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time results show that the proposed method has less time complexity after feature selection. Conclusions: The proposed feature extraction method is very effective for getting representatives information from mental states EEG signals in BCI applications and reducing the computational complexity of classifiers by reducing the number of extracted features.

Context: The cross-covariance of solar oscillations observed at pairs of points on the solar surface is a fundamental ingredient in time-distance helioseismology. Wave travel times are extracted from the cross-covariance function and are used to infer the physical conditions in the solar interior. Aims: Understanding the statistics of the two-point cross-covariance function is a necessary step towards optimizing the measurement of travel times. Methods: By modeling stochastic solar oscillations, we evaluate the variance of the cross-covariance function as function of time-lag and distance between the two points. Results: We show that the variance of the cross-covariance is independent of both time-lag and distance in the far field, i.e., when they are large compared to the coherence scales of the solar oscillations. Conclusions: The constant noise level for the cross-covariance means that the signal-to-noise ratio for the cross-covariance is proportional to the amplitude of the expectation value of the cross-covariance. This observation is important for planning data analysis efforts., accepted for publication in A&A

Antimicrobial peptides (AMP) are potent, broad spectrum antibiotics which demonstrate potential as novel therapeutic agents. Because it is both time-consuming and laborious to identify new AMPs by experiment, this paper tries to resolve this problem by pattern recognition. Two major contents included: Firstly, up to six kinds of physicochemical properties value are selected to code the AMP sequence as physical-chemical property matrix (PCM), then auto and cross covariance transformation is performed to extract features from the PCM for AMP sequence expression; Secondly, these feature vectors are input to a powerful Support Vector Machine (SVM) classifier for training and new query AMP recognition. For a newly constructed AMP benchmark dataset, the overall classification accuracy about 96% has been achieved through the rigorous Leave-One-Out cross-validation. For convenience, a user-friendly web server, AMPpred, has been established at http://icpr.jci.jx.cn/bioinfo/AMPpred. It is anticipated that this on-line predictor may become a useful bioinformatics tool for molecular biology and drug development. Also, its novel approach will further stimulate the development of predicting peptide attributes.

A novel stochastic joint inversion method based on cokriging is applied to estimate density and magnetic susceptibility distributions from gravity and total magnetic field data. The method fully integrates the physical relations between density–gravity, on one hand, and magnetic susceptibility–total magnetic field, on the other hand. As a consequence, when the data are considered noise-free, the responses from the inverted density and susceptibility data exactly reproduce the observed data. The required density and magnetic susceptibility auto- and cross covariance are assumed to follow a linear model of coregionalization (LCM). The parameters of the LCM are estimated from v–v plot fitting of the gravity and total magnetic experimental covariances. The model is tested on two synthetic cases and one real data set, the Perseverance mine (Quebec, Canada). Joint inversions are compared to separate inversions. The joint inversions better recover the known models in the synthetic cases. With the real data set, better definition and location of the mineralized lenses are achieved by joint inversion.

We introduce a valid parametric family of cross-covariance functions for multivariate spatial random fields where each component has a covariance function from a well-celebrated Matern class. Unlike previous attempts, our model indeed allows for various smoothnesses and rates of correlation decay for any number of vector components. We present the conditions on the parameter space that result in valid models with varying degrees of complexity. We discuss practical implementations, including reparameterizations to reflect the conditions on the parameter space and an iterative algorithm to increase the computational efficiency. We perform various Monte Carlo simulation experiments to explore the performances of our approach in terms of estimation and cokriging. The application of the proposed multivariate Matern model is illustrated on two meteorological datasets: temperature/pressure over the Pacific Northwest (bivariate) and wind/temperature/pressure in Oklahoma (trivariate). In the latter case, our flexi...

In this paper input-output pairing is done based on concept of energy. Parseval theorem and cross-covariance samples of input-output are used for estimation of energy. After approximating interaction energy between input and output of the plant, input-output pairing is fulfilled. Through examples, it is illustrated that proposed method is appropriate for input-output pairing. The result is compared with Effective Relative Energy Array (EREA) as another energy based approach for input-output pairing.

In geophysical and environmental problems, it is common to have multiple variables of interest measured at the same location and time. These multiple variables typically have dependence over space (and/or time). As a consequence, there is a growing interest in developing models for multivariate spatial processes, in particular, the cross-covariance models. On the other hand, many data sets these days cover a large portion of the Earth such as satellite data, which require valid covariance models on a globe. We present a class of parametric covariance models for multivariate processes on a globe. The covariance models are flexible in capturing non-stationarity in the data yet computationally feasible and require moderate numbers of parameters. We apply our covariance model to surface temperature and precipitation data from an NCAR climate model output. We compare our model to the multivariate version of the Matern cross-covariance function and models based on coregionalization and demonstrate the superior performance of our model in terms of AIC (and/or maximum loglikelihood values) and predictive skill. We also present some challenges in modelling the cross-covariance structure of the temperature and precipitation data. Based on the fitted results using full data, we give the estimated cross-correlation structure between the two variables.

Protein remote homology detection is a critical step toward annotating its structure and function. Supervised learning algorithms such as support vector machine are currently the most accurate methods. The position-specific score matrices (PSSMs) contain wealthy information about the evolutionary relationship of proteins. However, the PSSMs often have different lengths, which are difficult to be used by machine-learning methods. In this study, a simple, fast and powerful method is presented for protein remote homology detection, which combines support vector machine with auto-cross covariance transformation. The PSSMs are converted into a series of fixed-length vectors by auto-cross covariance transformation and these vectors are then input to a support vector machine classifier for remote homology detection. The sequence-order effects can be effectively captured by this scheme. Experiments are performed on well-established datasets, and the remote homology is simulated at the superfamily and the fold level, respectively. The results show that the proposed method, referred to as ACCRe, is comparable or even better than the state-of-the-art methods in terms of detection performance, and its time complexity is superior to those of other profile-based SVM methods. The auto-cross covariance transformation provides a novel way for the usage of evolutionary information, which can be widely used for protein-level studies.

An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships (QSRR) models. First, the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties (SGBP), involving physicochemical, quantum chemical, topological, spatial structural properties, etc.; thereafter, the sequence data are transformed into a uniform matrix by auto cross covariance (ACC). ACC accounts for the interactions between bases at a certain distance apart in an oligonucleotide sequence; hence, this method adequately takes the neighboring effect into account. Then, a genetic algorithm is used to select the variables related to chromatographic retention behavior of oligonucleotides. Finally, a support vector machine is used to develop QSRR models to predict chromatographic retention behavior. The whole dataset is divided into pairs of training sets and test sets with different proportions; as a result, it has been found that the QSRR models using more than 26 training samples have an appropriate external power, and can accurately represent the relationship between the features of sequences and structures, and the retention times. The results indicate that the SGBP-ACC approach is a useful structural representation method in QSRR of oligonucleotides due to its many advantages such as plentiful structural information, easy manipulation and high characterization competence. Moreover, the method can further be applied to predict chromatographic retention behavior of oligonucleotides.

The interacting multiple model (IMM) algorithm has been used in many applications. However, performance analysis of the IMM algorithm is difficult because it uses a set of Kalman filters that are coupled with each other. We present an algorithm to compute the means and cross-covariances of the residuals and state estimation errors of these Kalman filters. Specifically, we derive the cross-covariances, each of which is the covariance of the residuals of two Kalman filters, to account for the mutual interactions. From the means and cross-covariance terms, we then compute the means of the likelihood functions and the mean-squared estimation errors as performance measures of the IMM algorithm.

We develop a new combination strategy to predict retention times of oligonucleotides in ion-pair reversed-phase high performance liquid chromatography. The key step of the strategy is to use score of generalized base properties (SGBP) combined with auto cross covariance (ACC) to resolve the feature representation of nucleic acids. This representation characterize physicochemical, quantumchemical, topological, spatial structural features, etc., and their neighboring effect between bases at a certain distance apart in a sequence. The next step is to use the variables selected by genetic algorithm (GA) to construct prediction models of retention times of oligonucleotides based on support vector machine (SVM). Accordingly, GA-SVM models give different prediction performance using different input descriptors resulting from different step lengths in the ACC transformation, indicating the neighboring effect between bases should not be neglected in the features related to the chromatographic retention of oligonucleotides. As a whole, the GA-SVM predictors obtained from more than 20 training samples can produce satisfying performance in predicting the chromatographic retention of oligonucleotides at a range of temperatures (30 °C, 40 °C, 50 °C, 60 °C and 80 °C), respectively. The present approach based on the SGBP-ACC-GA-SVM combination shows great application prospect in the field of separation and analysis science, bioinformatics and proteomics.

We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spatial random fields, where each constituent component is a Matern process. The model parameters are interpretable in terms of process variance, smoothness, correlation length, and colocated correlation coefficients, which can be positive or negative. Both the marginal and the cross-covariance functions are of the Matern type. In a data example on error fields for numerical predictions of surface pressure and temperature over the North American Pacific Northwest, we compare the bivariate Matern model to the traditional linear model of coregionalization.

In applied econometric literature, the causal inferences are often made based on temporally aggregated or systematically sampled data. A number of theoretical studies have pointed out that temporal aggregation has distorting effects on causal inference and systematic sampling of stationary variables preserves the direction of causality. This paper examines the issue in detail by plugging in theoretical cross covariances into the limiting values of least squares estimates in a VAR framework. The asymptotic distributions of the estimates of systematically sampled process are expressed in terms of the cross covariances of the disaggregated process. An extensive Monte Carlo study is conducted to examine small sample results. Quite contrary to the stationary case, this paper shows that systematic sampling of integrated series may induce spurious causality. In particular, systematic sampling induces spurious bi-directional Granger causality among the variables if the uni-directional causality runs from a non-stationary series to either a stationary or a non-stationary series. On the other hand, systematic sampling preserves the uni-directional causality among the variables if the uni-directional causality runs from a stationary series to either a stationary or a non-stationary series. It is observed that in general the most distorting causal inferences are likely at low levels of sampling intervals where the order of sampling-span just exceeds the actual causal lag. At high levels of systematic sampling, causal information concentrates in contemporaneous correlations. An empirical exercise illustrates the relative usefulness of the results further.

A multi-scale parameterization approach, factor analysis scales of generalized amino acid information combined with auto cross covariance, was used to develop quantitative sequence-activity models of peptides using support vector machines. The results demonstrated that this approach could well characterize sequence features of the peptides studied.

The problem of constructing valid parametric cross-covariance functions is challenging. We propose a simple methodology, based on latent dimensions and existing covariance models for univariate random fields, to develop flexible, interpretable and computationally feasible classes of cross-covariance functions in closed form. We focus on spatio-temporal cross-covariance functions that can be nonseparable, asymmetric and can have different covariance structures, for instance different smoothness parameters, in each component. We discuss estimation of these models and perform a small simulation study to demonstrate our approach. We illustrate our methodology on a trivariate spatio-temporal pollution dataset from California and demonstrate that our cross-covariance performs better than other competing models. Copyright 2010, Oxford University Press.

We propose an improvement of the algorithm of joint assimilation of the data on climatic temperature, salinity, and altimetric sea level in a model of circulation. Unlike the previous works, the variances of the forecast errors of temperature and salinity and the cross-covariance functions of of the forecast errors of salinity-level and temperature-level depend on the dynamics of waters. It is shown that the structure of the fields of cross-covariance functions in the upper mixed layer is formed by the vertical turbulent diffusion of the variances of forecast errors of temperature and salinity. At greater depths, these statistical characteristics are mainly determined by the vertical advection. We compared the results of calculations with and without taking into account the dynamics of the statistical characteristics. The analysis of the influence of the dynamics of these characteristics makes it possible to reconstruct the mutually adapted climatic fields of temperature, salinity, and horizontal and vertical current velocities in the Black Sea with the assimilation of data in the numerical model in each time step.

A method for estimating the parameters of a continuous-time autoregressive exogenous process from discrete-time data is analyzed. The method consists of fitting an expression for the cross-covariance function, parameterized by the unknown parameters, to sample cross-covariances. The main contribution of the note is the derivation of an approximate expression for the covariance matrix of the estimated parameter vector.

Asymptotic cumulants of the distributions of the sample singular vectors and values of cross covariance and correlation matrices are obtained under nonnormality. The asymptotic cumulants are used to have the approximations of the distributions of the estimators by the Edgeworth expansions up to order O(1/n) and Hall’s method with variable transformation. The cases of Studentized estimators are also considered. As an application of the method, the distributions of the parameter estimators in the model of inter-battery factor analysis are expanded. Interpreting the singular vectors and values in the context of the factor model with distributional conditions, the asymptotic robustness of some lower-order normal-theory cumulants of the distributions of the sample singular vectors and values under nonnormality is shown.

In this study, we have carried out a brain-computer interface study that uses time delays between electrodes. As features, we have calculated the time delays that maximizes the absolute value of the cross-covariance between the chosen reference channel and the remaining channels. Performance results of 3 out of 5 participants that were nearly at a %100 accuracy level along with a relatively smaller number of training data, and a lack of similar studies indicate that the proposed approach is open to the further improvements.

24th Signal Processing and Communication Application Conference, SIU 2016; Zonguldak; Turkey; 16 May 2016 through 19 May 2016, Bu çalışmada, elektrotlar arası zaman gecikmesi kullanılarak bir beyin-bilgisayar ara yüzü çalışması gerçekleştirilmiştir. Öznitelik olarak, seçilen referans kanalı ile geriye kalan tüm kanalların çapraz kovaryansının mutlak değerinin en yüksek olduğu zaman gecikmeleri hesaplanmıştır. Çalışmada kullanılan 5 kişi içinden 3 kişinin sınıflandırma performansının %100’e yakın olmasının yanında bu kişilerin eğitim veri seti sayısının diğer iki kişiye göre oldukça düşük olması ve literatürde buna benzer çalışmaların azlığı, önerilen yaklaşımın geliştirilmeye açık olduğunu göstermektedir., In this study, we have carried out a brain-computer interface study that uses time delays between electrodes. As features, we have calculated the time delays that maximizes the absolute value of the cross-covariance between the chosen reference channel and the remaining channels. Performance results of 3 out of 5 participants that were nearly at a %100 accuracy level along with a relatively smaller number of training data, and a lack of similar studies indicate that the proposed approach is open to the further improvements.

Multivariate space–time data are increasingly available in various scientific disciplines. When analyzing these data, one of the key issues is to describe the multivariate space–time dependences. Under the Gaussian framework, one needs to propose relevant models for multivariate space–time covariance functions, i.e. matrix-valued mappings with the additional requirement of non-negative definiteness. We propose a flexible parametric class of cross-covariance functions for multivariate space–time Gaussian random fields. Space–time components belong to the (univariate) Gneiting class of space–time covariance functions, with Matern or Cauchy covariance functions in the spatial margins. The smoothness and scale parameters can be different for each variable. We provide sufficient conditions for positive definiteness. A simulation study shows that the parameters of this model can be efficiently estimated using weighted pairwise likelihood, which belongs to the class of composite likelihood methods. We then illustrate the model on a French dataset of weather variables.