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Early Paleozoic tectonic evolution of Qinling Orogenic Belt and Shangdan Ocean remains an argument because of the different understandings of Early Paleozoic geological records, including the petrogenesis of magmatic and metamorphic rocks and formation setting of Liuling Groups. In this paper, we present zircon U–Pb data and geochemical and isotopic compositions of the lamprophyres in the southern part of North Qinling Orogenic Belt. The lamprophyres yield zircon U–Pb ages of ca. 470 Ma and are coeval with the Early Paleozoic potassium–rich mafic rocks (Fushui complex). The lamprophyres belong to calc-alkaline series and are characterized by enrichment in large-ion lithophile elements and light rare earth elements and depletion in high field strength elements. Lamprophyre and mafic rocks, etc, the crust-like geochemical signatures represent a source feature. The lamprophyres also have high initial Sr ratios of 0.706859 to 0.713288 and enrich Nd isotopic composition with eNd(t) values of − 6.8 to − 8.3, implying that they are derived from the enriched subcontinental lithospheric mantle. In addition, the lamprophyres intrude into the supracrustal Luohansi Formation, representing their shallow intrusive level, and cross through the crust and carry the key information. In the lamprophyres, there are many Neoproterozoic zircon xenocrysts with ages of 813.3 to 825.6 Ma, which is affinity with South Qinling Orogenic Belt. This is an implication of the closure of Shangdan Ocean and collision between North Qinling Orogenic Belt and South Qinling Orogenic Belt during the formation of lamprophyre.

A cooperative control of a manipulator and a human operator has been proposed for an efficient direct teaching operation in this research. The main goal is making the operator be convenient and relaxed when he is operating the manipulator for a direct teaching. The proposed control strategy has two layers: In the first layer, human motion estimator (HME) has been designed to estimate a human intention. The recursive least square method has been utilized for the HME to simultaneously estimate the interaction force and the human arm admittance model. In the second layer, human motion reactor has been designed to keep the human motion intention precisely by a proportional derivative and gravity compensation in real time. Real experiments with a 3-degree of freedom robotic manipulator guided by the human operator have been conducted to draw a diamond shape on a panel. The experimental results demonstrate the effectiveness of the proposed cooperative control strategy.

Illumination models of the image set of an object (e.g., human face) under varying lighting conditions have been either empirically or analytically explored. However, the theoretical dimensionality of video bricks of an object under varying illumination is still unknown. In this paper, we focus on this question concretely and give both analytical and empirical results. We derive the theoretical upper bound of the dimensionality of video bricks by investigating the analytical formula of appearance changes due to motion variables of light sources. Theoretical results show in real-world scenes video bricks of an object under varying illumination could be expressed well by a low-dimensional linear subspace. Empirical results of the principal component analysis on the YaleB Face database and our video database are consistent with the theoretical results completely. The application of the low-dimensional linear models of video bricks is demonstrated by the foreground detection task in visual surveillance with drastic illumination changes.

In this paper, we study mathematical models of atomic visual patterns from natural videos and establish a generative visual vocabulary for video representation. Empirically, we employ small video patches (e.g., 15×15×5, called video "bricks") in natural videos as basic analysis unit. There are a variety of brick subspaces (or atomic video words) of varying dimensions in the high dimensional brick space. The structures of the words are characterized by both appearance and motion dynamics. Here, we categorize the words into two pure types: structural video words (SVWs) and textural video words (TVWs). A common generative model is introduced to model these two type video words in a unified form. The representation power of a word is measured by its information gain, based on which words are pursued one by one via a novel pursuit algorithm, and finally a holistic video vocabulary is built up. Experimental results show the potential power of our framework for video representation.

In this paper, a novel background model on spatio-temporal patches is introduced for video surveillance, especially for night outdoor scene, where extreme lighting conditions often cause troubles. The spatio-temporal patch, called brick, is presented to simultaneously capture spatio-temporal information in surveillance video. The set of bricks of a given background patch, under all possible lighting conditions, lies in a low-dimensional subspace, which can be learned by online subspace learning. The proposed method can efficiently model the background and detect the appearance and motion variance caused by foreground. Experimental results on real data show that the proposed method is insensitive to dramatic lighting changes and achieves superior performance to two classical methods.

In this paper, a novel local discriminant embedding method, Discriminant Clustering Embedding (DCE), is proposed for face recognition with image sets. DCE combines the effectiveness of submanifolds, which are extracted by clustering for each subject's image set, characterizing the inherent structure of face appearance manifold and the discriminant property of discriminant embedding. The low-dimensional embedding is learned via preserving the neighbor information within each submanifold, and separating the neighbor submanifolds belonging to different subjects from each other. Compared with previous work, the proposed method could not only discover the most powerful discriminative information embedded in the local structure of face appearance manifolds more sufficiently but also preserve it more efficiently. Extensive experiments on real world data demonstrate that DCE is efficient and robust for face recognition with image sets.

In this study, an expression manifold is constructed by Neighborhood Preserving Embedding (NPE) based on the expression semantic metric for a global representation of all possible facial expression images. On this learned manifold, images with semantic `similar' expression are mapped onto nearby points whatever their lighting, pose and individual appearance are quite different. The proposed manifold extracts the universal expression feature and reveals the intrinsic semantic global structure and the essential relations of the expression data. Experimental results demonstrate the effectiveness of our approach.