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The presence and/or generation mechanism of a mantle plume associated with early Permian rifting on the northern margin of Gondwana are topics of debate. Here we report LA–ICP–MS U–Pb zircon ages, whole-rock geochemistry, and Sr–Nd isotope data for high-Ti mafic dykes from southern Qiangtang, Tibet, with the aim of assessing if a mantle plume formed in this region during the early Permian. Zircon U–Pb dating of diabase dykes yielded ages of 290.6 ± 3.5 Ma and 290.1 ± 1.5 Ma, indicating they were emplaced during the early Permian. Whole-rock geochemistry shows that these mafic dykes are alkaline (Nb/Y = 0.73–0.99), have high TiO2 (3.6%–4.8%), and have ocean-island basalt (OIB)-like trace element patterns with enrichments in Nb, Ta, and Ti. Whole-rock Sr–Nd isotope data show a relatively narrow range of εNd(t) (+ 2.29 to + 3.53), similar to basalts produced by a mantle plume (e.g., Emeishan continental flood basalts (ECFB)). Elemental and isotope data suggest that the dykes have undergone fractionation crystallization of mafic minerals and have experienced negligible crustal contamination. These mafic rocks show an affinity to OIB and may have been generated by partial melting of an OIB-type, garnet-bearing asthenospheric mantle source. On the basis of a similar emplacement age to the Panjal Traps basalts in the Himalayas, combined with a tectonic reconstruction of Gondwana in the early to middle Permian, our work suggests that the high-Ti mafic dykes in the Southern Qiangtang terrane and the coeval Panjal Traps basalts in the Himalayas together comprise a ca. 290 Ma large igneous province linked to a mantle plume, which probably played an active role in early Permian rifting on the northern margin of Gondwana and was related to circum-Pangea subduction. [ABSTRACT FROM AUTHOR]

Spectral analysis was usually used to guide unsupervised feature selection. However, the performances of these methods are not always satisfactory due to that they may generate continuous pseudo labels to approximate the discrete real labels. In this paper, a novel unsupervised feature selection method is proposed based on self-expression model. Unlike existing spectral analysis based methods, we utilize self-expression model to capture the relationships between the features without learning the cluster labels. Specifically, each feature can be reconstructed by using a linear combination of all the features in the original feature space, and a representative feature should give a large weight to reconstruct other features. Besides, a structure preserved constraint is incorporated into our model for keeping the local manifold structure of the data. Then an efficient alternative iterative algorithm is utilized to solve our proposed model with the theoretical analysis on its convergence. The experimental results on different datasets show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

As one of the most important information of the data, the geometry structure information is usually modeled by a similarity graph to enforce the effectiveness of nonnegative matrix factorization (NMF). However, pairwise distance based graph is sensitive to noise and can not capture the subspace structure of the data. Reconstruction coefficients based graph can capture the subspace structure of the data, but the procedure of building the representation based graph is usually independent to the framework of NMF. To address this issue, a novel subspace clustering guided convex nonnegative matrix factorization (SC-CNMF) is proposed. In this NMF framework, the nonnegative subspace clustering is incorporated to learning the representation based graph, and meanwhile, a convex nonnegative matrix factorization is also updated simultaneously. To tackle the noise influence of the dataset, only k largest entries of each representation are kept in the subspace clustering. To capture the complicated geometry structure of the data, multiple centroids are also introduced to describe each cluster. Additionally, a row constraint is used to remove the relevance among the rows of the encoding matrix, which can help to improve the clustering performance of the proposed model. For the proposed NMF framework, two different objective functions with different optimizing schemes are designed. Image clustering experiments are conducted to demonstrate the effectiveness of the proposed methods on several datasets and compared with some related works based on NMF together with k -means clustering method and PCA as baseline. [ABSTRACT FROM AUTHOR]

The development of large surface-area and high conductivity electrode is a prerequisite for the construction of high-performance microbial fuel cells. Herein, we report an innovative approach to the fabrication of such high-performance electrodes via the welding assembly of 3D interconnected carbon nanotubes (CNTs) on a carbon-fiber (CF) paper electrode. The minimized interfacial ohmic loss between CNTs and the CF scaffold endowed the microbial fuel cells with the welding-assembled CNT-CF electrodes excellent electrochemical properties with the maximum power density of 2015.6 mW m−2, 10.0 times higher than that obtained with the untreated CP/CNT (499.8 mW m−2) carbon paper anode. As compared to the conventional chemical vapor deposition (CVD) growth technique for fabricating CNT- CF electrodes, this welding assembly approach is more versatile and much easier for up-scaling; on this basis, our work may pave a new avenue to the large-scale production of high-performance microbial fuel cells. • A novel approach to fabricating 3D welding-assembled CNT-CF electrodes for MFC. • Formation of robust network by welding assembly of the interconnected CNTs. • A modified anode providing the power density of MFC 10 times higher than CP. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • ∼2130 Ma mafic rocks both in the North and South Liaohe Groups of the Jiao-Liao-Ji Belt and these rocks have undergone metamorphism at ca. 1880 Ma. • Geochemically they are different from those formed in intra-continental rifts and volcanic arcs. • The Jiao-Liao-Ji belt was involved the opening of a back-arc basin above the northern subduction of oceanic plate and the closure of this basin at ca. 1900 Ma. Abstract The Paleoproterozoic Jiao-Liao-Ji Belt (JLJB) in the North China Craton, a major terrane boundary, has remained controversial with respect to its tectonic history. Here we report LA–ICP–MS U–Pb zircon ages, and whole-rock elemental and isotopic compositions of mafic rocks intruding into the North and South Liaohe Groups, with the aim of evaluating their petrogenetic affinities and metamorphic history, and their implications for the tectonic evolution of the JLJB. The mafic rocks in the North Liaohe Group dominantly composed of meta-diabase and meta-gabbro, whereas mafic rocks in the South Liaohe Group are mainly (garnet-) amphibolite. LA–ICP–MS zircon U–Pb analyses yield crystallization and metamorphic ages of ca. 2130 Ma and ca. 1880 Ma, respectively, suggesting that they are coeval, and were metamorphosed at the same time. Whole-rock elemental and isotopic compositions suggest tholeiitic affinity for the protoliths of both of them, with variable SiO 2 and MgO concentrations and Mg# and εNd(t) values. They both display enriched mid-oceanic-ridge basalt (E-MORB)-like trace element patterns with enrichment in light rare earth elements and depletion in some high field strength elements (HFSEs; e.g., Nb, Ta, and Ti). The data suggest derivation of the melts from a depleted asthenospheric mantle, coupled with fractional crystallization and crustal contamination. Some of the mafic rocks in the South Liaohe Group have lower HFSE (e.g., Nb, Ta, Zr, Hf, and Ti) concentrations, and La/Sm and Th/La ratios, and higher U/Th ratios than their counterparts in the North Liaohe Group, possibly suggesting enhanced metasomatism by subduction-related fluids and/or melts. The geochemical features of the mafic rocks from JLJB contrast with those of typical intra-continental rifts and volcanic arcs, and may have formed in a back-arc basin. Our study supports the presence of a ca. 2.2–2.1 Ga back-arc basin that formed due to northward subduction and subsequent closure of an oceanic plate, resulting in arc–continent collision and formation of the JLJB at ca. 1.9 Ga. [ABSTRACT FROM AUTHOR]

Meta-mafic rocks as sills, dykes and veins are widely distributed within the North Liaohe Group, Liaodong Peninsula, which consist mainly of meta-diabase/gabbro and (garnet) amphibolite with minor felsic clastic rocks as enclaves. Although a long-standing debate existed about the petrogenesis for the meta-mafic rocks, it is a critical judgement for using geochemistry and geochronology of these rocks to reveal the complicated tectonic evolution of the Paleoproterozoic Jiao-Liao-Ji Belt (JLJB). In this paper, a combined study of zircon U-Pb isotopic data, whole-rock geochemistry and Sm-Nd isotope analyses are reported for these rocks to investigate their sources, petrogenesis and tectonic settings. Abundant zircon U-Pb LA-ICP-MS age data reveal that the meta-mafic rocks have a magmatic emplacement age of ca. 2130 Ma with a metamorphic age of ca. 1878 Ma. These meta-mafic rocks have characteristic geochemical compositions with moderate SiO 2 (46.68–53.90 wt%), low TiO 2 (0.56–2.33 wt%) and variable total Fe 2 O 3 (9.65–17.58 wt%), MgO (2.77–11.49 wt%), Cr (1.44–1580 ppm) and Ni (6.4–168 ppm) similar to evolved tholeiitic basalt. They exhibit enriched mid-ocean ridge basalt (E-MORB)-like geochemical features (e.g., trace element patterns) with highly variable εNd(t) values from −1.5 to +3.1, relative enrichment of light rare earth elements (LREEs) and depletion of high field strength elements (HFSEs; e.g., Nb, Ta, Zr, Hf, and Ti). The geochemical trends of major and trace elements and Nd isotopes suggest that the primary magma for these meta-mafic rocks were probably derived from partial melting of a depleted asthenospheric mantle in the spinel stability field, with extensive fractional crystallization of a three-phase assemblage of olivine, clinopyroxene and plagioclase, crustal assimilation (e.g., continental detritus), and limited metasomatism from subduction-related fluids and/or melts. In view of the widespread distribution of Paleoproterozoic magmatism, including the Liaoji granites, and the voluminous sedimentary rocks in the Liaodong Peninsula, a newly tectonic model has been proposed that a ca. 2.1 Ga oceanic plate subduction event induced a back-arc basin opening, which closed to form the JLJB in the period of ca. 1.9 Ga. [ABSTRACT FROM AUTHOR]

Driving behavior recognition has attracted wide attention as it can act as an important reference input of many vehicle intelligent control systems. In this paper, a real-time recognition of driver's longitudinal driving behavior is investigated by proposing a hybrid adaptive pattern recognition method. Firstly, a framework of integrated behavior recognition model is established consisting of two sub models to cluster and label the sample data, respectively. Secondly, a new fast high-stability clustering method is proposed to solve the problem of clustering samples with non-negligible noise. And the test results show the clustering process can be completed within a short time lower than 0.1 s with different number of cluster centers. Then, support vector machine and artificial neural network are employed and trained to construct a heuristic self-labeling approach to label the clustered sample automatically in real time with high accuracy (92.9%) under an experimental driving cycle generated from our real-vehicle test bench. Subsequently, the two established modules are integrated and offline trained by 96 thousand historical data, and employed to a 400-second-long online application under a smoothly-varying driving condition, and three further applications under extreme driving cycles with about 1000 s, respectively. Simulation results show that the proposed integrated model is capable of eliminating the interference of noise, and has a relatively stable performance of recognition for different driving cycles with different driving behaviors (92.7% of overall performance for commonly cycles, and greater than 91.2% for extreme cycles), enhancing the capacity for online application. [ABSTRACT FROM AUTHOR]

• The original SAE is expanded to suit with the multiplicative noise in SAR change detection. • The features extracted by SFAE are more discriminative than the original stacked autoencoder due to that Fisher discriminant criterion is incorporated into SFAE. • Experiments on the simulated and real SAR datasets reveal that the proposed SFAE algorithm is effective on multitemporal single/multi-polarization SAR change detection. Specifically, the proposed SFAE method is obviously superior to the real-time methods on detection accuracy and the non-realtime methods on computational complexity. Stacked autoencoder is effective in image denoising and classification when it is used for synthetic aperture radar (SAR) change detection. However, the resulting features may not be discriminative enough for in some sense. To alleviate this problem, in this paper we propose a stacked Fisher autoencoder (SFAE) for SAR change detection. Specifically, in the framework of SFAE, unsupervised layer-wise feature learning and supervised fine-tuning are jointly performed when training the network. The trained network can be used to detect the changes in both of the single and multi-polarization SAR datasets in real-time. The proposed SFAE has two advantages. The first one is to expand the stacked autoencoder to suit the environment with the multiplicative noise in SAR change detection. The second is that the features extracted by SFAE are more discriminative than the original stacked autoencoder due to that Fisher discriminant criterion is incorporated into SFAE. The results on the simulated and real SAR datasets indicate that the proposed SFAE algorithm has a significant advantage on multitemporal single/multi-polarization SAR (SAR/PolSAR) change detection. [ABSTRACT FROM AUTHOR]