Detection Of Collinearity Research Articles

Alternative splicing dynamics and evolutionary divergence during embryogenesis in wheat species.

SummaryAmong polyploid species with complex genomic architecture, variations in the regulation of alternative splicing (AS) provide opportunities for transcriptional and proteomic plasticity and the potential for generating trait diversities. However, the evolution of AS and its influence on grain development in diploid grass and valuable polyploid wheat crops are poorly understood. To address this knowledge gap, we developed a pipeline for the analysis of alternatively spliced transcript isoforms, which takes the high sequence similarity among polyploid wheat subgenomes into account. Through analysis of synteny and detection of collinearity of homoeologous subgenomes, conserved and specific AS events across five wheat and grass species were identified. A global analysis of the regulation of AS in diploid grass and polyploid wheat grains revealed diversity in AS events not only between the endosperm, pericarp and embryo overdevelopment, but also between subgenomes. Analysis of AS in homoeologous triads of polyploid wheats revealed evolutionary divergence between gene‐level and transcript‐level regulation of embryogenesis. Evolutionary age analysis indicated that the generation of novel transcript isoforms has occurred in young genes at a more rapid rate than in ancient genes. These findings, together with the development of comprehensive AS resources for wheat and grass species, advance understanding of the evolution of regulatory features of AS during embryogenesis and grain development in wheat.

Prediction of the Solubility of CO2 in Imidazolium Ionic Liquids Based on Selective Ensemble Modeling Method

Solubility data is one of the essential basic data for CO2 capture by ionic liquids. A selective ensemble modeling method, proposed to overcome the shortcomings of current methods, was developed and applied to the prediction of the solubility of CO2 in imidazolium ionic liquids. Firstly, multiple different sub–models were established based on the diversities of data, structural, and parameter design philosophy. Secondly, the fuzzy C–means algorithm was used to cluster the sub–models, and the collinearity detection method was adopted to eliminate the sub–models with high collinearity. Finally, the information entropy method integrated the sub–models into the selective ensemble model. The validation of the CO2 solubility predictions against experimental data showed that the proposed ensemble model had better performance than its previous alternative, because more effective information was extracted from different angles, and the diversity and accuracy among the sub–models were fully integrated. This work not only provided an effective modeling method for the prediction of the solubility of CO2 in ionic liquids, but also provided an effective method for the discrimination of ionic liquids for CO2 capture.

Some New Diagnostics of Multicollinearity in Linear Regression Model

The problem of multicollinearity compromises the numerical stability of the regression coefficient estimate and cause some serious problem in validation and interpretation of the model. In this paper, we propose two new collinearity diagnostics for the detection of collinearity among regressors, based on coefficient of determination and adjusted coefficient of determination from auxiliary regression of regressors. A Monte Carlo simulation study has been conducted to compare the existing and proposed collinearity diagnostic tests. Comparison of diagnostics on some existing collinear data are also made.

The red indicator and corrected VIFs in generalized linear models

Investigators that seek to employ regression analysis usually encounter the problem of multicollinearity with dependency on two or more explanatory variables. Multicollinearity is associated with unstable estimated coefficients and it results in high variances of the least squares estimators in linear regression models (LRMs). Thus the detection of collinearity is the compulsory first step in regression analysis. Multicollinearity also come out in generalized linear models (GLMs) and has same serious effects on the maximum likelihood estimates. The purposes of this paper are to propose new collinearity diagnostics criteria in GLMs in the context of both the maximum likelihood and ridge estimators, to examine the properties of new collinearity diagnostics via the ridge constant, to exemplify the theoretical results by numerical examples on Poisson, Binomial and Gamma responses. The effects of centering and scaling the information matrix on the sensitivity of the diagnostics in the presence of collinearity are also investigated.

Gaussian Message Passing-Based Cooperative Localisation with Bootstrap Percolation Scheme in Dense Networks

In dense networks, the tremendous computational complexity and communication overhead of cooperative localisation are the two main bottlenecks that limit practical application. In this study, we introduce a bootstrap percolation scheme into Gaussian message passing-based cooperative localisation for precise positioning, aimed at reducing the system overhead. Considering the uncertainty information and geometric distribution of neighbours, an approximate collinear detection criterion is proposed to detect the possible flip ambiguities in cooperative localisation. According to the detection result and our connection constraint, agents are divided into three categories and are approximated by different distribution families. A message passing rule is designed to control the propagation direction from high precision to low precision, thereby mitigating potential error propagation. Additionally, a layer-by-layer positioning mechanism is established where the agents are located gradually. Analytical and simulation results indicate that when the ranging standard deviation is 0·2 m, 89·3% of the agent nodes can be located within 0·4 m using the proposed algorithm. Compared with the Hybrid Sum-Product Algorithm over A Wireless Network (H-SPAWN), this ratio is increased by 8·4% and the computational complexity is reduced by 72%.

Laser-induced breakdown spectroscopy of major and minor oxides in steel slags: Influence of detection geometry and signal normalization

Slag from secondary metallurgy in industrial steel production is analyzed by laser-induced breakdown spectroscopy (LIBS). The major oxides CaO, Al2O3, MgO, SiO2, FeO, MnO, and TiO2 are determined by calibration-free LIBS (CF-LIBS) method. For the minor oxide P2O5 calibration curves are established and the limits of detection (LOD) and the root-mean squared errors of prediction (RMSEP) are determined. The optical emission of the laser-induced plasma is measured for different detection geometries and varying sample position relative to the focal plane of the laser beam. LIBS spectra, plasma parameters, and analytical results are very similar for light collection with optical fibres close to the plasma (“direct detection”) and at remote position (“collinear detection”). With collinear detection, the CF-LIBS calculated oxide concentrations are insensitive to sample position along the optical axis over wide range. The detection limits and the prediction errors of minor P2O5 depend on the major slag element used for signal normalization. With Mg and Si as internal reference elements the LOD values are 0.31wt% and 0.07wt%, respectively. The RMSEP values are lowest for signal normalization to Si. Calculations of the optical emission of ideal plasma support the experimental preference for Si as reference element in the phosphorous calibration.

The time course of pattern discrimination in the human brain

In electrophysiological experiments on visual pattern discrimination, decision difficulty was manipulated either via the physical characteristics of the test stimuli, or by changing the instruction given to the observer. Visual stimuli were rectangular matrices each composed of 100 Gabor patches having different orientations. Matrices differed in the number of Gabor patches with vertical, or horizontal, orientation. The observers’ task was either to discriminate the dominant orientation or to detect collinear elements in the matrix. Relating task difficulty to performance, in the first experimental paradigm (detection of orientation) we obtained the conventional S-like psychometric function but in the second (detection of collinearity) the psychometric function showed a complicated U-curve. Matching between electrophysiological and psychophysical data and image statistical functions allowed us to establish the relative timing of the cortical processes underlying perception and decision making in relation to textural features. In the first 170ms after stimulus onset coding of the low-level properties of the image takes place. In the time interval 170–400ms, ERP amplitude correlated only with complex image properties, but not with task difficulty. The first effects arising from decision difficulty were observable at 400ms after stimulus onset, and therefore this is probably the earliest electrophysiological signature of the decision making processes, in the given experimental paradigm.

Mctest: An R Package for Detection of Collinearity among Regressors

mctest: An R Package for Detection of Collinearity among Regressors

Grouping and segmentation in binocular rivalry

Dichoptic presentation of dot arrays produces binocular rivalry if the arrays are of opposite contrast relative to background. Rivalry can occur even if individual dots in one eye's array do not overlap with the dots in the contralateral eye's array. The amount of unitary perception of only one array is a measure of the probability that the stimuli rival as textured surfaces rather than as portions of arrays or as individual dot elements. In accordance with Gestalt grouping principles, arrays of uniform brightness or color produced more unitary perception than mixed arrays. However, experiments with parametric variation of dot motion coherence suggested that segmentation mechanisms based on detection of collinearity can also influence perceptual selection and suppression in binocular rivalry.

Cálculo de las coordenadas espaciales de una escena 3-D por medio del análisis de imágenes estereoscópicas

This paper addresses the reconstruction of a 3-D scene from stereoscopic images containing straight line segments. First the edges of the images are detected based on both Canny´s approach and a heuristic method to track the edge points. Each line is extracted by fitting the pixels to a linear model and measuring the variance which gives an estimate of how well the data adjust to the model. Due to fragmentation of segments a collinearity detection stage is implemented and the corresponding pairs are searched. A Graph theory method is applied to find the right matches. Each node of the graph represents a corresponding pair and the adjacency matrix is built establishing the compatibility between nodes. Then the maximum clique is found and 3-D line coordinates are calculated by means of triangulation. Finally an interpolation stage takes place and a dense depth map is recovered.

Cross section transformation with Jacobian and with a Monte Carlo method

We have performed a cross sectional transformation with Jacobians and with a Monte Carlo method for the collinear detection data of α − t coincidences from the breakup reaction of 7Li. It is shown that the same rest frame cross sections result from the two different types of transformation within statistical uncertainties.

Modelling and parameter estimation for fly-by-wire aircraft/control systems

This paper covers in detail the issues related to parameter estimation of open loop dynamics of fly-by-wire aircraft/control systems from closed loop data. System identifiability aspects in the closed loop and the effect of various feedback types on the parameterisation of the system matrices are reviewed. The methods commonly employed for the detection of collinearity in the data are discussed. A brief discussion of the common methods used for analysis of unstable/augmented aircraft are given. Also, controller information based identification method (CIBIM), which utilises knowledge of the controller in the analysis, is presented. The discussion is followed by numerical results of application of the techniques to simulated data.

Guaranteed Convergence of the Hough Transform

The straight-line Hough Transform using normal parameterization with a continuous voting kernel is considered. It transforms the collinearity detection problem to a problem of finding the global maximum of a two dimensional function above a domain in the parameter space. The principle is similar to robust regression using fixed scale M-estimation. Unlike standard M-estimation procedures the Hough Transform does not rely on a good initial estimate of the line parameters: The global optimization problem is approached by exhaustive search on a grid that is usually as fine as computationally feasible.The global maximum of a general function above a bounded domain cannot be found by a finite number of function evaluations. Only if sufficient a priori knowledge about the smoothness of the objective function is available can convergence to the global maximum be guaranteed. The extraction of a priori information and its efficient use are the main challenges in real global optimization problems.Convergence in the Hough Transform is the ability to ensure that the global maximum is in the immediate neighborhood of the maximal grid point. More than 30 years after Hough patented the basic algorithm, it is still not clear how fine the parameter space quantization should be in order not to miss the true maximum.In this paper conditions for the convergence of the Hough Transform to the global maximum are derived. The necessary constraints on the variability of the objective (Hough) function are obtained by using the saturated parabolic voting kernel and by defining an image model with several application dependent parameters. Random errors in the location of edge points and background noise are allowed in the model and lead to statistical convergence guarantees. Significant intermediate results are obtained on the structure of the peak region and on the spatial statistics of noise voting in the continuous kernel Hough Transform. Convergence strategies are studied and the necessary parameter space quantization intervals are derived. Guaranteed focusing policies for multiresolution Hough algorithms are developed. Application of the theoretic results to images that deviate from the image model is considered and exemplified.

Collinearity detection in linear regression models

Multicollinearity can seriously affect least-squares parameter estimates. Many methods have been suggested to determine those parameters most involved. This paper, beginning with the contributions of Belsley, Kuh, and Welsch (1980) and Belsley (1991), forges a new direction. A decomposition of the variable space allows the near dependencies to be isolated in one sub-space. And this, in turn, allows a corresponding decomposition of the main statistics, as well as a new one proposed here, to provide better information on the structure of the collinear relations.

Detection of collinearity- influential observations

Collinearity is defined as the existence of near linear dependencies among the columns of the matrix. It has been found, though, that a strategically located point (i.e. a row of ) can greatly modify the collinearity structure of the data. The whole approach to regression analysis depends, largely, on the measured level of collinearity. It is, thus, crucial for the analyst to be able to detect points that modify this level significantly. The main objective of this paper is to develop a diagnostic tool for the detection of collinearity-influential points. This diagnostic is easy to compute and capable of detecting collinearity influential sets of points, Two examples are given to illustrate its use.

Algorithm AS 178: The Gauss-Jordan Sweep Operator with Detection of Collinearity

Algorithm AS 178: The Gauss-Jordan Sweep Operator with Detection of Collinearity

Levels ofS36fromS34(t,p)S36

Levels of ${\mathrm{S}}^{36}$ have been investigated via the ${\mathrm{S}}^{34}(t,p){\mathrm{S}}^{36}$ reaction, using targets of ${\mathrm{Sb}}_{2}$${\mathrm{S}}_{3}$ with the sulfur content enriched to 86% in the isotope ${\mathrm{S}}^{34}$. The excitation energies (given in MeV) of the levels observed, together with the restrictions on spins and parities resulting from this investigation and also previous work, are as follows: 3.29, ${2}^{+}$; 3.34, ${0}^{+}$; 4.19, ${3}^{\ensuremath{-}}$; 4.52, ${1}^{+}$; 4.57, ${2}^{+}$; 5.26, 1, 2, 3; 5.38, 1, 2, or (3); 5.50, 2, (3), 4; 5.57, 1, (2), 3; 6.20, 2, 3; 6.51, ${4}^{+}$; and 7.12, ${1}^{+}$ or ${2}^{+}$. Information on the $\ensuremath{\gamma}$-ray branching of these levels and on the angular-correlation patterns of major branches was obtained from two-parameter $p\ensuremath{-}\ensuremath{\gamma}$ coincidence studies utilizing collinear detection of the reaction protons. The 3.29-MeV level decays by $E2$ radiation directly to the ${\mathrm{S}}^{36}$ ${J}^{\ensuremath{\pi}}={0}^{+}$ ground state. An intermediate-image pair spectrometer was used to determine that the 3.34-MeV level has ${J}^{\ensuremath{\pi}}={0}^{+}$, since it decays by an $E0$ transition to the ground state. With the exception of the 4.52-, 5.38-, and 7.12-MeV levels, which decay mainly to the ground state, the higher-lying levels were observed to deexcite primarily by cascade transitions through the 3.29-MeV level. Ge(Li) studies of $\ensuremath{\gamma}$-ray Doppler shifts determine the following mean-lifetime restrictions for the indicated levels (excitations in MeV); 3.29, $\ensuremath{\tau}=0.20\ifmmode\pm\else\textpm\fi{}0.03$ psec; 4.19, $\ensuremath{\tau}>2 \mathrm{psec}$; and 4.57, $\ensuremath{\tau}<0.1$ psec. Additional but less restrictive limitations are given for several of the remaining levels. The lifetime of the ${0}^{+}$ 3.34-MeV state was measured by electronic techniques which determine a mean life $\ensuremath{\tau}=12.7\ifmmode\pm\else\textpm\fi{}0.3$ nsec. The spin-parity limitations deduced herein are from a combination of the results of the angular-correlation analysis and these lifetime limitations. No evidence was obtained for the existence of states at 2.00 and 2.85 MeV previously reported from the ${\mathrm{S}}^{34}(t,p){\mathrm{S}}^{36}$ reaction. Finally, the observed level structure as deduced from this and previous experiments is compared with relevant shell-model calculations on the even- and odd-parity states of this ${T}_{z}=2$ nucleus.