Cross-Domain Object Representation via Robust Low-Rank Correlation Analysis

Cross-domain data has become very popular recently since various viewpoints and different sensors tend to facilitate better data representation. In this article, we propose a novel cross-domain object representation algorithm (RLRCA) which not only explores the complexity of multiple relationships of variables by canonical correlation analysis (CCA) but also uses a low rank model to decrease the effect of noisy data. To the best of our knowledge, this is the first try to smoothly integrate CCA and a low-rank model to uncover correlated components across different domains and to suppress the effect of noisy or corrupted data. In order to improve the flexibility of the algorithm to address various cross-domain object representation problems, two instantiation methods of RLRCA are proposed from feature and sample space, respectively. In this way, a better cross-domain object representation can be achieved through effectively learning the intrinsic CCA features and taking full advantage of cross-domain object alignment information while pursuing low rank representations. Extensive experimental results on CMU PIE, Office-Caltech, Pascal VOC 2007, and NUS-WIDE-Object datasets, demonstrate that our designed models have superior performance over several state-of-the-art cross-domain low rank methods in image clustering and classification tasks with various corruption levels.

On the Meta-Analysis Review Towards efficient mechanisms for resource, energy, and data management in the fog computing

Fog computing is an architecture that uses collaborative end-user edge devices to carry out a large amount of storage, transmission, configuration, and module function. In this computingenvironment, management issue is the process of managing, monitoring and optimizing the correlated components for improving the performance, availability, security and any fundamental operational requirement. The management strategies have a great impact on the fog computing, but, as far as we know, there is not a comprehensive and systematic study in this field. Hence, this paper classifies the management strategies into three main categories, including resource, energy and data management. In addition, it defines the new challenges in each of these categories. Finally, the differences between the reviewed strategies are investigated in terms of scalability,reliability, time, and queries attributes along with providing the main directions for future research.

Comparative Analysis of Robust Modification Quality for Principal Component Analysis to Perform Correlated Data Compression

Principal component analysis is one of the methods traditionally used to solve the problem of reducing the dimensionality of a multidimensional vector with correlated components. We constructed the principal components using a special representation of the covariance or correlation matrix of the indicators observed. The classical principal component analysis uses Pearson sample correlation coefficients as estimates of the correlation matrix elements. These estimates are extremely sensitive to sample contamination and anomalous observations. To robustify the principal component analysis, we propose to replace the sample estimates of correlation matrices with well-known robust analogues, which include Spearman's rank correlation coefficient, Minimum Covariance Determinant estimates, orthogonalized Gnanadesikan --- Kettenring estimates, and Olive --- Hawkins estimates. The study aims to carry out a comparative numerical analysis of the classical principal component analysis and its robust modifications. For this purpose, we simulated nine-dimensional vectors with known correlation matrix structures and introduced a special metric that allows us to evaluate the quality of data compression. Our extensive numerical experiment has shown that the classical principal component analysis boasts the best compression quality for a Gaussian distribution of observations. When observations are characterised by a Student's t-distribution with three degrees of freedom, as well as when a cluster of outliers, individual anomalous observations, or symmetric contaminations described by the Tukey distribution are present in the data, it is the Gnanadesikan --- Kettenring and Olive --- Hawkins estimates modifying the principal component analysis that show the best compression quality. The quality of the classical principal component analysis and Spearman’s rank modification decreases in these cases

Spatial Component Analysis to Improve Mineral Estimation Using Sentinel-2 Band Ratio: Application to a Greek Bauxite Residue

Remote sensing can be fruitfully used in the characterization of metals within stockpiles and tailings, produced from mining activities. Satellite information, in the form of band ratio, can act as an auxiliary variable, with a certain correlation with the ground primary data. In the presence of this auxiliary variable, modeled with nested structures, the spatial components without correlation can be filtered out, so that the useful correlation with ground data grows. This paper investigates the possibility to substitute in a co-kriging system, the whole band ratio information, with only the correlated components. The method has been applied over a bauxite residues case study and presents three estimation alternatives: ordinary kriging, co-kriging, component co-kriging. Results have shown how using the most correlated component reduces the estimation variance and improves the estimation results. In general terms, when a good correlation with ground samples exists, co-kriging of the satellite band-ratio Component improves the reconstruction of mineral grade distribution, thus affecting the selectivity. On the other hand, the use of the components approach exalts the distance variability.

Vegetative propagation, chemical composition and antioxidant activity of yerba mate genotypes

Ilex paraguariensis, commonly known as yerba mate, is a tree species native to South America. Its commercial value is due to the manufacturing of teas, with potential also in the pharmacological and cosmetic industries. Vegetative propagation of yerba mate is considered an innovation to the traditional production systems based on sexual propagation. The present study aimed to evaluate the rhizogenic potential and chemical attributes of mini-cuttings from 15 yerba mate genotypes, as well as to verify the correlation between phytochemical and rooting-related variables. Mini-cuttings were collected from a pre-existing mini-clonal hedge and the experimental design was completely randomized, with 15 treatments (genotypes), four replications and 10 mini-cuttings per plot. After 120 days, mini-cuttings were assessed regarding rooting, mortality, callogenesis and leaf retention percentages, percentage of mini-cuttings with both calluses and roots, number of roots and average root length. At the time of collection, subsamples from each plot were used for phytochemical analyses including total phenolic compounds, protein, caffeine and theobromine contents and antioxidant activity. Rooting percentages ranged from 5 to 72.5%, with significant variation among genotypes. Adventitious rooting and phytochemical profile of yerba mate mini-cuttings are genotype-dependent. Leaf retention is a relevant factor in the rooting of yerba mate mini-cuttings and the levels of total phenolic compounds, antioxidants and theobromine present in mini-cuttings are negatively correlated components to Ilex paraguariensis adventitious rooting.

Emphasising spatial structure in geosocial media data using spatial amplifier filtering

In this article, a new method called spatial amplifier filtering is proposed. The presented method is related to Moran eigenvector filtering and allows the accentuation of spatial structures in heterogeneous data sets. The spatial amplifier filtering technique is based on the inclusion of certain eigenvectors of a spatial weights matrix into a regression model. The application of this method can be seen as a pre-processing step prior to subsequent analyses, and to separate different types of spatially correlated components in a data set. For this purpose, three different types of the so-called spatial amplifiers are proposed, each consisting of different subsets of eigenvectors of the weights matrix. These amplifiers can either emphasise the positive or negative spatial autocorrelation, or spatial structuring in general. In this way, it is possible to make desired spatial structures more visible, especially in spatially highly mixed data sets, whereby the focus here is on geosocial media data. In the empirical part of the article, it is first shown why georeferenced social media data are difficult to handle from a spatial analysis perspective, motivating the need for the method proposed. Subsequently, the technique of amplifier filtering is applied to two data sets: a census data set from Brazil and Twitter data from London. The results obtained show that the method is capable of strengthening existing spatial structures and mitigating potentially disturbing spatial randomness patterns and other nuisances. This facilitates the interpretation especially of the Twitter data used. While the analysis of the unfiltered Twitter data with established methods reveals little information about possible spatial structures in the tweets, the filtered data offer a much clearer picture with distinguishable clusters. In addition, the method also provides insights into the internal irregularity of spatial clusters and thus complements the toolbox for investigating spatial heterogeneity.

Digital technologies in assessing the success component of adolescent athletes

The article suggests that the concept of “success in sports training of children” is integrative, including interrelated and correlated components. From the point of view of the authors, the evaluativeperformance component is one of the most necessary to achieve success. The authors propose to improve the indicators of this component through the trainer-child-parent interaction system, which consists in the use of digital technologies, as well as in the development and application of the educational program of interaction between the trainer and the parents of the children involved in “Success of your child”. The article also presents the diagnostics of the evaluative-effective component, which consists in testing children by using the questionnaire of Stolin. and Panteleeva. Testing was carried out in the course of experimental work in two stages: at the ascertaining stage (before the introduction of the educational program) and the control stage (after the introduction of the educational program). Based on the results of the experimental work, the authors made conclusions about the need for a deeper introduction into the practice of interaction between parents, their children and the coach.

A continuous review inventory model with complex correlations among components

Under a flexible mass-production system, a manufacturer may need to provide highly customized products to meet customer satisfaction. It is likely that components in a customized product are correlated in such a way that the demands of some components depend on those of others. In order to cope with dependence in the demands, we proposed a continuous review multi-item inventory (Q, r) model that included a general form of correlation and dependence in demands among components. We represented the proposed model by using a probabilistic graphical model under the assumption that the demands of all components and their correlations were represented by a multivariate Gaussian probability distribution. By taking an advantage of a directed acyclic graph and its topological order, we demonstrated that the correlated demands among components in the proposed model could be solved without any approximation and assumption. As an illustration of the proposed method, we solved an inventory (Q, r) model of eight correlated components and discussed the experimental results in terms of correlation and dependence in demand.

Planck2018 results

We present full-sky maps of the cosmic microwave background (CMB) and polarized synchrotron and thermal dust emission, derived from the third set ofPlanckfrequency maps. These products have significantly lower contamination from instrumental systematic effects than previous versions. The methodologies used to derive these maps follow closely those described in earlier papers, adopting four methods (Commander,NILC,SEVEM, andSMICA) to extract the CMB component, as well as three methods (Commander,GNILC, andSMICA) to extract astrophysical components. Our revised CMB temperature maps agree with corresponding products in thePlanck2015 delivery, whereas the polarization maps exhibit significantly lower large-scale power, reflecting the improved data processing described in companion papers; however, the noise properties of the resulting data products are complicated, and the best available end-to-end simulations exhibit relative biases with respect to the data at the few percent level. Using these maps, we are for the first time able to fit the spectral index of thermal dust independently over 3° regions. We derive a conservative estimate of the mean spectral index of polarized thermal dust emission ofβd = 1.55 ± 0.05, where the uncertainty marginalizes both over all known systematic uncertainties and different estimation techniques. For polarized synchrotron emission, we find a mean spectral index ofβs = −3.1 ± 0.1, consistent with previously reported measurements. We note that the current data processing does not allow for construction of unbiased single-bolometer maps, and this limits our ability to extract CO emission and correlated components. The foreground results for intensity derived in this paper therefore do not supersede correspondingPlanck2015 products. For polarization the new results supersede the corresponding 2015 products in all respects.