Identified particle production, azimuthal anisotropy, and interferometry measurements in Au plus Au collisions at root s(NN)=9.2 GeV

We present the first measurements of identified hadron production, azimuthal anisotropy, and pion interferometry from Au + Au collisions below the nominal injection energy at the BNL Relativistic Heavy-Ion Collider (RHIC) facility. The data were collected using the large acceptance solenoidal tracker at RHIC (STAR) detector at root s(NN) = 9.2 GeV from a test run of the collider in the year 2008. Midrapidity results on multiplicity density dN/dy in rapidity y, average transverse momentum < p(T)>, particle ratios, elliptic flow, and Hanbury-Brown-Twiss (HBT) radii are consistent with the corresponding results at similar root s(NN) from fixed-target experiments. Directed flow measurements are presented for both midrapidity and forward-rapidity regions. Furthermore the collision centrality dependence of identified particle dN/dy, < p(T)>, and particle ratios are discussed. These results also demonstrate that the capabilities of the STAR detector, although optimized for root s(NN) = 200 GeV, are suitable for the proposed QCD critical-point search and exploration of the QCD phase diagram at RHIC.

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.

Fast Haar Transform Based Feature Extraction for Face Representation and Recognition

Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency.

Discriminative Orthogonal Neighborhood-Preserving Projections for Classification

Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.

Image reconstruction by an alternating minimisation

This paper focuses on the problem of incomplete data in the applications of the circular cone-beam computed tomography. This problem is frequently encountered in medical imaging sciences and some other industrial imaging systems. For example, it is crucial when the high density region of objects can only be penetrated by X-rays in a limited angular range. As the projection data are only available in an angular range, the above mentioned incomplete data problem can be attributed to the limited angle problem, which is an ill-posed inverse problem. This paper reports a modified total variation minimisation method to reduce the data insufficiency in tomographic imaging. This proposed method is robust and efficient in the task of reconstruction by showing the convergence of the alternating minimisation method. The results demonstrate that this new reconstruction method brings reasonable performance. (C) 2010 Elsevier B.V. All rights reserved.

Order-Order Transition of C -> sdG -> sL -> S in ABC Triblock Copolymer Thin Film Induced by Solvent Vapor

The morphology transition of polystyrene-block-poly(butadiene)-block-poly(2-vinylpyridine) (SBV) triblock thin film induced in benzene vapor showing weak selectivity for PS is investigated. The order-order transitions (OOT) in the sequence of core-shell cylinders (C), sphere in 'diblock gyroid' (sdG), sphere in lamella (sL) and sphere (S) are observed. The projection along (111) direction in Gyroid phase (sdG(111)) is found to epitaxially grow from C(001) in the film.

Three-dimensional structural features and the toxicity of aminobenzenes and phenols

For a QSAR of the toxicity of aminobenzenes in environment and their structures, the projection areas of the molecules in 3D space were calculated. The combinations of the projection areas and quantum chemical as well as topological parameters were performed for the methods of regression analysis and neural network, and much better results than that by using CoMFA were achieved.

Effective descriptions of molecular structures and the quantitative structure - Activity relationship studies

In this research. we found CoMFA alone could not obtain sufficiently a strong equation to allow confident prediction for aminobenzenes. When some other parameter. such as heat of molecular formation of the compounds, was introduced into the CoMFA model, the results Were improved greatly. It gives us a hint that a better description for molecular structures will yield a better prediction model, and this hint challenged us to look for another method-the projection areas of molecules in 3D space for 3D-QSAR. It is surprising that much better results than that obtained by using CoMFA Were achieved. Besides the CoMFA analysis. multiregression analysis and neural network methods for building the models were used in this paper.

Structure and Conformation of 2,3,6, 7,10,11-hexakispentyloxytriphenylene by TEM and Computer Simulation

Experimental electron diffraction patterns and high resolution images were used to determine the space group and unit cell dimensions of 2,3,6,7,10,11-hexakispentyloxytriphenylene. Subsequently the molecular conformation was calculated by energy minimized package in Cerius2. Using this method, we got the HPT crystal structure: space group: P6/mmm; lattice type: hexogonal; the lattice parameters are a = b = 20.3 angstrom, c = 3.52 angstrom, = = 90 degrees, = 120 degrees. The core of HPT is not perpendicular to the column. The angle between a axis and HPT core plane is 9 degrees which cannot be seen in b-c projection. The simulated ED patterns and HREM images are good agreement with the experimental ED patterns and HREM images.

Analysis of the sea-level variability along the Chinese coast and estimation of the impact of a CO2-perturbed atmospheric circulation

The relationship between monthly sea-level data measured at stations located along the Chinese coast and concurrent large-scale atmospheric forcing in the period 1960-1990 is examined. It is found that sea-level varies quite coherently along the whole coast, despite the geographical extension of the station set. A canonical correlation analysis between sea-level and sea-level pressure (SLP) indicates that a great part of the sea-level variability can be explained by the action of the wind stress on the ocean surface. The relationship between sea-level and sea-level pressure is analyzed separately for the summer and winter half-years. In winter, one factor affecting sea-level variability at all stations is the SLP contrast between the continent and the Pacific Ocean, hence the intensity of the winter Monsoon circulation. Another factor that affects coherently all stations is the intensity of the zonal circulation at mid-latitudes. In the summer half year, on the other hand, the influence of SLP on sea-level is spatially less coherent: the stations in the Yellow Sea are affected by a more localized circulation anomaly pattern, whereas the rest of the stations is more directly connected to the intensity of the zonal circulation. Based on this analysis, statistical models (different for summer and winter) to hindcast coastal sealevel anomalies from the large-scale SLP field are formulated. These models have been tested by fitting their internal parameters in a test period and reproducing reasonably the sea-level evolution in an independent period. These statistical models are also used to estimate the contribution of the changes of the atmospheric circulation on sea-level along the Chinese coast in an altered climate. For this purpose the ouput of 150 year-long experiment with the coupled ocean-atmosphere model ECHAM1-LSG has been analyzed, in which the atmospheric concentration of greenhouse gases was continuously increased from 1940 until 2090, according to the Scenario A projection of the Intergovermental Panel on Climate Change. In this experiment the meridional (zonal) circulation relevant for sea-level tends to become weaker (stronger) in the winter half year and stronger (weaker) in summer. The estimated contribution of this atmospheric circulation changes to coastal sea-level is of the order of a few centimeters at the end of the integration, being in winter negative in the Yellow Sea and positive in the China Sea with opposite signs in the summer half-year.

First record of the genus Gigantione (Epicaridea: Bopyridae: Pseudioninae) from Chinese waters, with description of three new species

The parasitic isopod genus Gigantione is first recorded from China. Four species are reported infesting xanthid and goneplacid crabs, three are new to science, and one is a new record from China. Gigantione ishigakiensis Shiino, 1941, infesting Liagore rubromaculata (De Haan); G. hainanensis sp. nov., infesting Atergatis floridus (L.) and Atergatis sp., which differs from other recorded species in the shape of its barbula, first oostegite and subrectangular maxilliped; G. rhombos sp. nov., infesting Heteroplax dentata Stimpson, Eucrate alcocki Serene and Eucrate sp., its female distinguished from other species of Gigantione by having a prominent rhombic projection on the barbula; and G. tau sp. nov., infesting Carcinoplax longimanus (De Haan), the female of which differs from other species mainly by its T-shaped pigmentation on the head. Four brachyuran crabs are first reported as hosts of bopyrids. A list of all brachyuran species so far recorded as bopyrid hosts in China is provided.

基于改进的OFA-MPCA的监控方法

多向主元分析(MPCA)是利用多变量统计方法从纷杂的海量数据信息中提取出能够准确表征数据信息的几个主元,并通过投影法来降低数据的维数,主要应用于间歇生产过程中.在实际的间歇生产过程中,由于各种原因导致各批次异步造成它们运行时间的不一致,而无法直接建立有效的统计模型,正交函数近似(OFA)是一种基于正交基的投影变换技术,通过对原始数据进行OFA处理后,可以用投影系数来描述原始数据所具有的特征,并且可以达到轨迹同步化和压缩数据量的目的.对OFA法进行了部分改进,并结合MPCA法对典型的间歇过程——青霉素发酵过程进行了仿真研究.结果表明,改进的OFA计算速度有了极大的提高,且改进的OFA-MPCA法能完好地对各批次进行同步、建模并得出准确的监视结果.

基于视觉伺服的输电线巡检机器人抓线控制

巡检机器人在越障时,需要完成机器人手臂的准确抓线控制.结合输电线的几何特征和摄像机成像原理,提出了一种基于单摄像机的立体视觉方法来确定输电线的位置和姿态.基于该定位方法及视觉伺服理论,建立机械手抓线伺服控制模型.利用自行研制的巡检机器人进行了视觉伺服抓线实验;实验结果验证了该方法的有效性.

轮式移动机器人与地形交互运动仿真研究

分析了轮式移动机器人(WMR)在不平坦的三维地形上运动的运动学模型.利用速度投影法,得到了WMR运动模型的一种新形式.基于虚拟现实技术,利用VC++OpenGL实现了WMR虚拟漫游系统.该系统具有较强的交互性和实时性,为星球探测机器人的虚拟导航、遥操作等提供了验证平台.

基于非线性修正的多线阵像机的空间定位

介绍了一种基于多线阵像机构成的视觉空间定位系统.该系统利用线阵像机的快速性与高分辨率的特点,采用了非平行空间投影面相交定位的基本原理,利用几何投影关系定位求解的方法,实现了多线阵像机视觉系统的空间定位.并提出了多线阵像机的神经网络非线性修正方法,使修正后的PSD能在较宽的位置范围内输出高线性度的信号.实验结果表明,基于非线性修正的多线阵像机位姿测量系统简化了立体视觉空间定位计算的复杂性,在定位精度、定位范围和采样速度上均达到了良好效果.