CO2 Flux Estimation by Different Regression Methods from an Alpine Meadow on the Qinghai-Tibetan Plateau

National Key Research and Development Program [2010CB833500]; National Natural Science Foundation of China [30590381]; Chinese Academy of Sciences [KZCX2-YW-432]

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

Robust Tensor Analysis With L1-Norm

Tensor analysis plays an important role in modern image and vision computing problems. Most of the existing tensor analysis approaches are based on the Frobenius norm, which makes them sensitive to outliers. In this paper, we propose L1-norm-based tensor analysis (TPCA-L1), which is robust to outliers. Experimental results upon face and other datasets demonstrate the advantages of the proposed approach.

Robust Tris-Cyclometalated Iridium(III) Phosphors with Ligands for Effective Charge Carrier Injection/Transport: Synthesis, Redox, Photophysical, and Electrophosphorescent Behavior

With the target to design and develop new functionalized green triplet light emitters that possess distinctive electronic properties for robust and highly efficient phosphorescent organic light-emitting diodes (PHOLEDs), a series of bluish-green to yellow-green phosphorescent tris-cyclometalated homoleptic iridium(III) complexes [Ir(ppy-X)(3)] (X=SiPh3, GePh3, NPh2, POPh2, OPh, SPh, SO2Ph, Hppy=2-phenylpyridine) have been synthesized and fully characterized by spectroscopic, redox, and photophysical methods

Robust core-shell supramolecular assemblies based on cationic vesicles and ring-shaped {Mo-154} polyoxomolybdate nanoclusters: Template-directed synthesis and characterizations

Substantial progress has been made recently in extending the supramolecular assembly of biomimetic structures to vesicle-based sophisticated nanocomposites and mesostructures. We report herein the successful preparation of unilamellar surfactant vesicles coated with a monolayer of ring-shaped {Mo-154} polyoxometalate (POM) nanoclusters, (NH4)(28)[Mo-154 (NO)(14)O(448)Hi(4)(H2O)(70)].approximate to 350H(2)O, by coulomb attractions using preformed didodecyldimethylammonium bromide (DDAB) surfactant vesicles as templates. The resultant vesicle-templated supramolecular assemblies are robust (they do not disintegrate upon dehydration) both at room-temperature ambient and vacuum conditions, as characterized by conventional transmission electron microscopy (TEM) and atomic force microscopy (AFM). The flexibility of the complex soft assemblies was also revealed by AFM measurements. The effect of POM-vesicle coulomb attractions on the dimensions of the templating vesicles was also investigated by using dynamic light scattering (DLS).Although origins of the structure stability of the as-prepared supramolecular assemblies are not clear yet, the nanometer scale cavities and the related properties of macroions of the POM clusters may play an important role in it.

Selection for variables in quantitative structure-activity/property relationship studies - Comparison between methods of orthogonal descriptors and leaps-and-bounds regression analysis

In this paper, the comparison of orthogonal descriptors and Leaps-and-Bounds regression analysis is performed. The results obtained by using orthogonal descriptors are better than that obtained by using Leaps-and-Bounds regression for the data set of nitrobenzenes used in this study. Leaps-and-Bounds regression can be used effectively for selection of variables in quantitative structure-activity/property relationship(QSAR/QSPR) studies. Consequently, orthogonalisation of descriptors is also a good method for variable selection for studies on QSAR/QSPR.

Studies on the method of leaps and bounds regression

In this paper, we introduce the method of leaps and bounds regression which can be used to select variables quickly and obtain the best regression models. These models contain one variable, two variables, three variables and so on. The results obtained by using leaps and bounds regression were compared with those achieved by using stepwise regression to lead to the conclusion that leaps and bounds regression is an effective method.

Prediction of molar absorptivities of color reagents during their color reactions with cerium using multiple regression analysis and neural network

A quantitative structure-property study has been made on the relationship between molar absorptivities (epsilon) of asymmetrical phosphone bisazo derivatives of chromotropic acid and their color reactions with cerium by multiple regression analysis and neural network. The new topological indices A(x1) - A(x3) suggested in our laboratory and molecular connectivity indices of 43 compounds have been calculated. The results obtained from the two methods are compared. The neural network model is superior to the regression analysis technique and gave a prediction which was sufficiently accurate to estimate the molar absorptivities of color reagents during their color reactions with cerium.

Study on Phenol Compounds Using Multiple Regression and Neural Network Methods

In this paper, the molecular connectivity indices and the electronic charge parameters of forty-eight phenol compounds nave been calculated. and applied for studying the relationship between partition coefficients and structure of phenol compounds. The results demonstrate that the properties of compounds can be described better with selective parameters, and the results obtained by neural network are superior to that by multiplle regression.

Quantitative structure-property relationships for colour reagents and their colour reactions with ytterbium using regression analysis and computational neural networks

In this paper, the new topological indices A(x1)-A(x3) suggested in our laboratory and molecular connectivity indices have been applied to multivariate analysis in structure-property studies. The topological indices of twenty asymmetrical phosphono bisazo derivatives of chromotropic acid have been calculated. The structure-property relationships between colour reagents and their colour reactions with ytterbium have been studied by A(x1)-A(x3) indices and molecular connectivity indices with satisfactory results. Multiple regression analysis and neural networks were employed simultaneously in this study.

QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS FOR TOXICITY OF PHENOLS USING REGRESSION-ANALYSIS AND COMPUTATIONAL NEURAL NETWORKS

Quantitative structure-toxicity models were developed that directly link the molecular structures of a et of 50 alkYlated and/or halogenated phenols with their polar narcosis toxicity, expressed as the negative logarithm of the IGC50 (50% growth inhibitor

Robust Guaranteed Cost Control for Yaw Control of Helicopter

针对具有时变不确定性且不确定性界为椭球的线性系统提出了一种新的具有自适应机制的鲁棒保性能控制器设计方法。首先,引入一个具有可由自适应律在线调整的可调参数的目标模型,通过该参数来保证由目标模型与被控模型所获得的误差系统渐近稳定。结合保证目标模型稳定性的设计,最终形成保证闭环系统稳定且控制器增益仿射依赖于可调参数的鲁棒保性能跟踪控制器。应用于安装在试验平台上的小型直升机航向控制中,仿真试验表明了该方法的有效性。

Robust Learning Control for Manipulators with Uncertainty Disturbances Based on Internal Model

In this paper, a disturbance controller is designed for making robotic system behave as a decoupled linear system according to the concept of internal model. Based on the linear system, the paper presents an iterative learning control algorithm to robotic manipulators. A sufficient condition for convergence is provided. The selection of parameter values of the algorithm is simple and easy to meet the convergence condition. The simulation results demonstrate the effectiveness of the algorithm..