Search

Semantic segmentation is a fundamental technology for autonomous driving. It has a high demand for inference speed and accuracy. However, a good trade‐off between accuracy and latency is yet not present in existing semantic segmentation approaches. Due to the limitation of speed, the authors cannot increase the number of network layers without limit and cannot design modules like in the networks without real‐time. It is a challenging problem how to design a model with good performance under limited resources. To alleviate these issues, in this study, the authors propose a refinement co‐supervision network (RCNet), which is real‐time on a high‐resolution image (1024×2048). The authors first construct the context refinement module, which can provide low computation cost way for obtaining the large receptive field and context information. Furthermore, a boundary co‐supervision mechanism is proposed. It strengthens the optimisation of easily neglected boundaries and small targets. Experimental results reveal that the proposed RCNet outperforms seven representative semantic segmentation methods. [ABSTRACT FROM AUTHOR]

Numerous deep learning-based object detection methods have achieved excellent performance. However, the performance on small-size object detection and positive and negative sample imbalance problems is not satisfactory. We propose a multi-scale feature selective matching network (MFSMNet) to improve the performance of small-size object detection and alleviate the positive and negative sample imbalance problems. First, we construct a multi-scale semantic enhancement module (MSEM) to compensate for the information loss of small-sized targets during down-sampling by obtaining richer semantic information from features at multiple scales. Then, we design the anchor selective matching (ASM) strategy to alleviate the training dominated by negative samples caused by the imbalance of positive and negative samples, which converts the offset values of the localization branch output in the detection head into localization scores and reduces negative samples by discarding low-quality anchors. Finally, a series of quantitative and qualitative experiments on the Microsoft COCO 2017 and PASCAL VOC 2007 + 2012 datasets show that our method is competitive compared to nine other representative methods. MFSMNet runs on a GeForce RTX 3090. [ABSTRACT FROM AUTHOR]

With the development of image segmentation technology, image context information plays an increasingly important role in semantic segmentation. However, due to the complexity of context information in different feature maps, simple context capture operations can easily cause context information omission. Rich context information can better classify categories and improve the quality of image segmentation. On the contrary, poor context information will lead to blurred image category segmentation and an incomplete target edge. In order to capture rich context information as completely as possible, we constructed a Multi-Pooling Context Network (MPCNet), which is a multi-pool contextual network for the semantic segmentation of images. Specifically, we first proposed the Pooling Context Aggregation Module to capture the deep context information of the image by processing the information between the space, channel, and pixel of the image. At the same time, the Spatial Context Module was constructed to capture the detailed spatial context of images at different stages of the network. The whole network structure adopted the form of codec to better extract image context. Finally, we performed extensive experiments on three semantic segmentation datasets (Cityscapes, ADE20K, and PASCAL VOC2012 datasets), which fully proved that our proposed network effectively alleviated the lack of context extraction and verified the effectiveness of the network. [ABSTRACT FROM AUTHOR]

Feature pyramid network (FPN) is widely used in object detection in order to divide and conquer objects of different scales and to fuse high and low-level features, and it has achieved encouraging achievements in multi-scale object processing. However, due to the mismatch between receptive fields at different stages, the direct fusion of the two features from different receptive fields may be unable to achieve satisfactory results. Moreover, simple lateral connections in FPN may lead to loss of spatial relationships and details. To alleviate these problems, in this paper we propose a field-matching attention network (FMANet) for object detection. Particularly, we first propose a receptive field dilated module (RFDM), which is used to normalize receptive fields between features at different stages to the same scale. Furthermore, to capture the spatial informations and details, we build a dual attention module (DAM) by employing the spatial attention and channel attention. Utilizing both spatial and channel attention mechanisms simultaneously improves performance while maintaining speed. Finally, experimental results reveal that our proposed FMANet with DSPDarkNet-53 as backbone achieves a competitive detection performance. [ABSTRACT FROM AUTHOR]

Convolutional neural networks have been widely used in image semantic segmentation. However, continuous downsampling operations in convolutional neural networks (such as pooling or convolution with step size) reduce the initial image resolution and lose the spatial details of the image, resulting in blurred image segmentation results. To alleviate this problem, in this paper we propose a multi-stage context refinement network (MCRNet) for semantic segmentation. Specifically, we first construct a Lowest-resolution Chain Context Aggregation (LCCA) module to encode rich semantic information. For obtaining more spatial detail information, we further build a High-resolution Context Attention Refinement (HCAR) module consisting of context feature extraction and context feature refinement. Finally, MCRNet fuses the context information generated by LCCA and HCAR for pixel prediction. Experimental results on three challenging semantic segmentation datasets, namely PASCAL VOC2012, ADE20K and Cityscapes, reveals that our proposed MCRNet is effective. [ABSTRACT FROM AUTHOR]

Based on the detection technology, regressing predicted bounding boxes provides an effective approach in multiple object tracking. However, if only the information in the current frame is considered, identity (ID) switch is easy to happen when objects interact. In this paper, we propose an Object-Aware Bounding Box Regression (OABBR) for online multi-object tracking. We first propose an Object-Aware Spatial-Temporal Understanding (OASTU) module to mine the correlated information in corresponding object's trajectory. OASTU updates features of predictions by the correlated information. By using the updated features, we further perform bounding box regression. Besides, to make features extracted by the backbone network contain more ID information, we construct a weak ID constraint in the training phase. The introduced weak ID constraint facilitates OASTU to be ID consistent and further alleviates ID switch. By exploring the spatial-temporal information in corresponding object's trajectory, each prediction is able to know the information of the corresponding object, which makes the purpose of its regression clearer. Experimental results on four public benchmarks demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Cartoonization as a special type of artistic style transfer is a difficult image processing task. The current existing artistic style transfer methods cannot generate satisfactory cartoon-style images due to that artistic style images often have delicate strokes and rich hierarchical color changes while cartoon-style images have smooth surfaces without obvious color changes, and sharp edges. To this end, we propose a cartoon loss based generative adversarial network (CartoonLossGAN) for cartoonization. Particularly, we first reuse the encoder part of the discriminator to build a compact generative adversarial network (GAN) based cartoonization architecture. Then we propose a novel cartoon loss function for the architecture. It can imitate the process of sketching to learn the smooth surface of the cartoon image, and imitate the coloring process to learn the coloring of the cartoon image. Furthermore, we also propose an initialization strategy, which is used in the scenario of reusing the discriminator to make our model training easier and more stable. Extensive experimental results demonstrate that our proposed CartoonLossGAN can generate fantastic cartoon-style images, and outperforms four representative methods. [ABSTRACT FROM AUTHOR]

Appearance similarity is of great importance for the association between objects and candidates. Recurrent models and similarity vector are two ways widely used by trackers for calculating similarities between objects and candidates. Recurrent models, like Long Short Term Memory network (LSTM), are capable of modeling the continuous change of object's appearance in trajectory. But it is prone to identity (ID) switch when only employ recurrent models as appearance model. The similarity vector way is able to maintain correct IDs for objects when they reappear. But association fails easily when the object is partially occluded and similarity vector is used as the only appearance model. To obtain more accurate and robust appearance similarity, in this paper, we propose an online association by continuous-discrete appearance similarity measurement, OA-CDASM, for multi-object tracking. For continuous perspective, the concept of "smoothness" is proposed to explicitly model and use the continuous and smooth change of object's appearance in trajectory. For discrete perspective, similarity vector is employed. By taking both continuous smoothness and discrete similarity vector into consideration, we can get the continuous-discrete appearance similarity measurement, CDASM, and further perform online association based on CDASM. Experimental results on three public benchmarks demonstrate the effectiveness of our work. [ABSTRACT FROM AUTHOR]

The metasedimentary rocks of the South Liaohe Group are widely distributed along the Jiao‐Liao‐Ji Belt (JLJB) in eastern Liaoning Province. It is important to understanding tectonic setting and evolution of the JLJB by determining its sediment sources and properties of the provenances, sedimentary facies, metamorphic ages, and facies. After the detailed field geological surveys and comprehensive laboratory observation, many high‐grade metapelitic rocks are identified in the South Liaohe Group. The main rock type is sillimanite‐garnet‐cordierite‐biotite plagiogneiss. The zircon U–Pb geochronology of three samples shows that these high‐grade meta‐pelitic rocks are mainly derived from the Liaoji Granites and the Archean basement. The protolith formation ages are between 2.00 and 1.93 Ga. Subsequently, these protolithes underwent high‐pressure metamorphism resulted from collisional orogeny, with peak metamorphism at ca. 1.88 Ga. Then, at 1.88–1.84 Ga, these rocks experienced the retrograde metamorphism and exhumed to the surface during crustal uplift. Geochemical data show that the protoliths are alumina‐rich claystones and graywackes with low maturity, implying near‐source sedimentation. The chemical index of alteration (CIA) values of these samples range from 60 to 80, indicating that the protoliths underwent moderate weathering. The sediment source was mainly felsic components of the upper crust, possibly with the addition of ancient sediments. Various tectonic environment discrimination diagrams combined with previous results on metamorphism and isotope chronology of these metasedimentary rocks in the JLJB indicate that the rocks in the study area originated from an active continental margin with an evolving island arc. [ABSTRACT FROM AUTHOR]

Many representation methods were built for gray image textures. However, they are not effective for color textures in general. To alleviate this problem, in this paper we propose a novel Compact Interchannel Sampling Difference Descriptor (CISDD) for color texture classification. In particular, considering sampling-based method can capture more directional information, we first use a heavy-tailed distribution, t-distribution to generate sample points in the image patch to calculate the micro-block difference. Then we model the interchannel relationship of color texture image by using dense micro-block differences. Furthermore, we utilize principal component analysis (PCA) to reduce the dimensions of the features encoded by the Fisher vector, and construct a Compact Interchannel Sampling Difference Descriptor (CISDD) for representing color texture image. Finally, experimental results on five published standard texture datasets (KTH-TIPS, VisTex, CUReT, USPTex and Colored Brodatz) reveal that CISDD is effective and outperforms thirteen representative color texture classification methods. [ABSTRACT FROM AUTHOR]

In this letter, we propose a simple yet effective texture descriptor, symmetric mean and directional contour pattern (SMDCP), for texture classification. In particular, first the robust symmetric mean pattern (RSMP) that extracts the sign and amplitude information of the local difference through the neighbourhood average in a new scheme of encoding to further enhance the robustness to noise is constructed. Then a local directional and contour pattern (LDCP) to represent the contour information and direction information of adjacent sampling points is extracted. By concatenating the RSMP and LDCP, a robust and effective texture descriptor (SMDCP) for classification is built. Experimental results reveal that the proposed method obtains significant performance and high discrimination in comparison with 10 representative approaches. [ABSTRACT FROM AUTHOR]

• Inexpensive ammonium metatungstate for synthesis of WS 2 is used. • Composite film with microcavity and nanofilm inside is formed. • The graphene-WS 2 composite presented fast lithium insertation/deinsertation. • The structure of the composite can be modified by the ratio of graphene to WS 2. In this study, the water-evaporation-induced assembly of a graphene oxide-ammonium metatungstate hydrate multilayer film was developed. After chemical reduction and high-temperature sulfurization, a graphene-WS 2 composite nanofilm with microcavities was obtained. When evaluated as an anode material in a lithium-ion battery, the graphene-WS 2 sample (precursor graphene oxide/WS 2 mass ratio of 1:4) exhibited high initial specific capacity (∼980 mAh g−1 at 0.1 A g−1), optimal cycling performance (∼450 mAh g−1 after 100 cycles at 0.1 A g−1), and superior rate capability (∼300 mAh g−1 at 2 A g−1). The graphene/WS 2 mass ratio influenced the contact between WS 2 and graphene and the porous nanofilm structure facilitated the kinetics of lithium-ion diffusion. [ABSTRACT FROM AUTHOR]

Multi-scale analysis has been widely used for constructing texture descriptors by modeling the coefficients in transformed domains. However, the resulting descriptors are not robust to the rotated textures when performing texture classification. To alleviate this problem, we in this paper propose a multi-scale counting and difference representation (CDR) of image textures for texture classification. Particularly, we first extract a single-scale CDR feature consisting of the local counting vector (LCV) and the differential excitation vector (DEV). The LCV is established to capture different types of textural structures using the discrete local counting projection, while the DEV is used to describe the difference information of textures in accordance with the differential excitation projection. Finally, the multi-scale CDR feature of a texture image is constructed by combining CDRs at different scales. Experimental results on Brodatz, VisTex, and Outex databases demonstrate that our proposed multi-scale CDR-based texture classification method outperforms five representative texture classification methods. [ABSTRACT FROM AUTHOR]

High environmental pressure exerts an external stress on the survival of microorganisms that are commonly found under normal pressure. In response, many growth traits alter, including cell morphology and physiology, cellular structure, metabolism, physical and chemical properties, the reproductive process, and defense mechanisms. The high-pressure technology (HP) has been industrially utilized in pressurized sterilization, synthesis of stress-induced products, and microbial/enzymatic transformation of chemicals. This article reviews current research on pressure-induced production of metabolites in normal-pressure microbes and their enzymatic reactions. Factors that affect the production of such metabolites are summarized, as well as the effect of pressure on the performance of microbial fermentation and the yield of flavoring compounds, different categories of induced enzymatic reactions and their characteristics in the supercritical carbon dioxide fluid, effects on enzyme activity, and the selection of desirable bacterial strains. Technological challenges are discussed, and future research directions are proposed. Information presented here will benefit the research, development, and application of the HP technology to improve microbial fermentation and enzymatic production of biologically active substances, thereby help to meet their increasing demand from the ever-expanding market. [ABSTRACT FROM AUTHOR]

Effective representation of image texture is important for an image-classification task. Statistical modelling in wavelet domains has been widely used to image texture representation. However, due to the intraclass complexity and interclass diversity of textures, it is hard to use a predefined probability distribution function to fit adaptively all wavelet subband coefficients of different textures. In this article, we propose a novel modelling approach, Heterogeneous and Incrementally Generated Histogram (HIGH), to indirectly model the wavelet coefficients by use of four local features in wavelet subbands. By concatenating all the HIGHs in all wavelet subbands of a texture, we can construct a nonnegative multiresolution vector (NMV) to represent a texture image. Considering the NMV’s high dimensionality and nonnegativity, we further propose a Hessian regularized discriminative nonnegative matrix factorization to compute a low-dimensional basis of the linear subspace of NMVs. Finally, we present a texture classification approach by projecting NMVs on the low-dimensional basis. Experimental results show that our proposed texture classification method outperforms seven representative approaches. [ABSTRACT FROM AUTHOR]

Abstract: Feature extraction is an important processing procedure in texture classification. For feature extraction in the wavelet domain, the energies of subbands are usually extracted for texture classification. However, the energy of one subband is just a specific feature. In this paper, we propose an efficient feature extraction method for texture classification. In particular, feature vectors are obtained by c-means clustering on the contourlet domain as well as using two conventionally extracted features that represent the dispersion degree of contourlet subband coefficients. The c-means clustering algorithm is initialized via a nonrandom initialization scheme. By investigating these feature vectors, we employ a weighted for comparing any two feature vectors that represent the corresponding subbands of two images and define a new distance between two images. According to the new distance, a k-Nearest Neighbor (kNN) classifier is utilized to perform texture classification, and experimental results show that our proposed approach outperforms five current state-of-the-art texture classification approaches. [Copyright &y& Elsevier]

As a newly developed 2-D extension of the wavelet transform using multiscale and directional filter banks, the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification. In this paper, we propose a novel Bayesian texture classifier based on the adaptive model-selection learning of Poisson mixtures on the contourlet features of texture images. The adaptive model-selection learning of Poisson mixtures is carried out by the recently established adaptive gradient Bayesian Ying-Yang harmony learning algorithm for Poisson mixtures. It is demonstrated by the experiments that our proposed Bayesian classifier significantly improves the texture classification accuracy in comparison with several current state-of-the-art texture classification approaches. [ABSTRACT FROM AUTHOR]

Abstract: The metabolism of trehalose, a marker of general physiological stress, and glutathione (GSH), another marker of oxidative stress marker, was examined in exponentially growing Saccharomyces cerevisiae cells after treatment with high pressure air. The growth of yeast cells was dramatically decreased under high pressure and the duration of stable growth was significantly shortened. Scanning electron microscopy showed that high pressure led to the disruption of membrane structure. Yeast cells were wrinkled or broken under higher pressure, indicating high pressure was a strong stress factor. The maximum yields of trehalose and GSH were obtained when the cells were cultured under 1.0MPa pressure for 3 and 6h, respectively. The increase of the production of trehalose and GSH indicated that trehalose and glutathione had protection effects under the stress of high pressure. [Copyright &y& Elsevier]

Presents a study which determined the activity of water during the metamorphic peak of the Archaean granulite complex near the Taipingzhai-Louizishan region, East Hebei Province, China. Description of the regional geological setting and metamorphic condition; Estimation of the activity of water; Interpretation to the fugacity of oxygen; Variations in the chemical potential of water and oxygen; Composition of fluid.

Hyperspectral unmixing (HU) is regarded as an indispensable preprocessing procedure for many field of spectral data analysis because of the existence of mixed pixels. However, the unmixing algorithms are implemented under the presupposition of special mixing models. In other words, any unmixing algorithm only works on a special mixing model of the spectra. This leads to low generalization performance of most unmixing algorithms. To mitigate this problem, a robust unmixing method is proposed, which exploits dual views with adaptive weights for HU (AwDvHU). The proposed method utilizes multi-kernel learning to construct a high-dimensional space that can reflect the nonlinear interaction between spectra optimally. Then, through fusing the unmixing object of original data and the mapped high-dimensional features, the AwDvHU method takes full advantage of the complementary characteristics of features in dual views. Moreover, the AwDvHU method automatically learns the weights for dual views according to the importance of different feature spaces. Its effectiveness in unmixing is verified by experimental results both on the synthetic and real data. [ABSTRACT FROM AUTHOR]

Binary representations have demonstrated remarkable performance in face recognition for its robustness to local changes and computation efficiency. However, the performance of face recognition based on most binary descriptors are not satisfactory when dealing with blurred face images. To solve this problem, we propose a novel blur invariant binary descriptor for face recognition. Particularly, we maximize the correlation between the binary codes of sharp face images and blurred face images of positive image pairs for learning the projection matrix. After that, we use the learned projection matrix to obtain blur-robust binary codes by quantizing projected pixel difference vectors (PDVs) in the testing stage. Experiment results on FERET and CMU-PIE show that our method achieves better recognition performance than representative binary descriptors LBP and CBFD. [ABSTRACT FROM AUTHOR]

Existing video summarization approaches mainly concentrate on the sequential or structural characteristic of video data. However, they do not pay enough attention to the video summarization task itself. In this article, we propose a meta learning method for performing task-driven video summarization, denoted by MetaL-TDVS, to explicitly explore the video summarization mechanism among summarizing processes on different videos. Particularly, MetaL-TDVS aims to excavate the latent mechanism for summarizing video by reformulating video summarization as a meta learning problem and promote the generalization ability of the trained model. MetaL-TDVS regards summarizing each video as a single task to make better use of the experience and knowledge learned from processes of summarizing other videos to summarize new ones. Furthermore, MetaL-TDVS updates models via a twofold backpropagation, which forces the model optimized on one video to obtain high accuracy on another video in every training step. Extensive experiments on benchmark datasets demonstrate the superiority and better generalization ability of MetaL-TDVS against several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Convolutional Neural Network (CNN) has shown their advantages in salient object detection. CNN can generate great saliency maps because it can obtain high-level semantic information. And the semantic information is usually achieved by stacking multiple convolutional layers and pooling layers. However, multiple pooling operations will reduce the size of the feature map and easily blur the boundary of the salient object. Therefore, such operations are not beneficial to generate great saliency results. To alleviate this issue, we propose a novel edge information-guided hierarchical feature fusion network (HFFNet). Our network fuses features hierarchically and retains accurate semantic information and clear edge information effectively. Specifically, we extract image features from different levels of VGG. Then, we fuse the features hierarchically to generate high-level semantic information and low-level edge information. In order to retain better information at different levels, we adopt a one-to-one hierarchical supervision strategy to supervise the generation of low-level information and high-level information respectively. Finally, we use low-level edge information to guide the saliency map generation, and the edge guidance fusion is able to identify saliency regions effectively. The proposed HFFNet has been extensively evaluated on five traditional benchmark datasets. The experimental results demonstrate that the proposed model is fairly effective in salient object detection compared with 10 state-of-the-art models under different evaluation indicators, and it is superior to most of the comparison models. [ABSTRACT FROM AUTHOR]

Saliency detection technology has been greatly developed and applied in recent years. However, the performance of current methods is not satisfactory in complex scenes. One of the reasons is that the performance improvement is often carried out through utilizing complicated mathematical models and involving multiple features rather than classifying the scene complexity and respectively detecting saliency. To break this unified detection schema for generating better results, we propose a method of scene classification-oriented saliency detection via the modularized prescription in this paper. Different scenes are described by a scene complexity expression model, and they are analyzed and discriminately detected by different pipelines. This process seems like that doctors can tailor the treatment prescriptions when they meet different symptoms. Moreover, two SVM-based classifiers are trained for scene classification and sky region identification, and the proposed sky region discrimination and erase model can be used to efficiently decrease the saliency interference by the high luminance of the background sky regions. Experimental results demonstrate the effectiveness and superiority of the proposed method in both higher precision and better smoothness, especially for detecting in structure complex scenes. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) is one widely used feature extraction technology in the tasks of image clustering and image classification. For the former task, various unsupervised NMF methods based on the data distribution structure information have been proposed. While for the latter task, the label information of the dataset is one very important guiding. However, most previous proposed supervised NMF methods emphasis on imposing the discriminant constraints on the coefficient matrix. When dealing with new coming samples, the transpose or the pseudoinverse of the basis matrix is used to project these samples to the low dimension space. In this way, the label influence to the basis matrix is indirect. Although, there are also some methods trying to constrain the basis matrix in NMF framework, either they only restrict within-class samples or impose improper constraint on the basis matrix. To address these problems, in this article a novel NMF framework named discriminative and orthogonal subspace constraints-based nonnegative matrix factorization (DOSNMF) is proposed. In DOSNMF, the discriminative constraints are imposed on the projected subspace instead of the directly learned representation. In this manner, the discriminative information is directly connected with the projected subspace. At the same time, an orthogonal term is incorporated in DOSNMF to adjust the orthogonality of the learned basis matrix, which can ensure the orthogonality of the learned subspace and improve the sparseness of the basis matrix at the same time. This framework can be implemented in two ways. The first way is based on the manifold learning theory. In this way, two graphs, i.e., the intrinsic graph and the penalty graph, are constructed to capture the intra-class structure and the inter-class distinctness. With this design, both the manifold structure information and the discriminative information of the dataset are utilized. For convenience, we name this method as the name of the framework, i.e., DOSNMF. The second way is based on the Fisher's criterion, we name it Fisher's criterion-based DOSNMF (FDOSNMF). The objective functions of DOSNMF and FDOSNMF can be easily optimized using multiplicative update (MU) rules. The new methods are tested on five datasets and compared with several supervised and unsupervised variants of NMF. The experimental results reveal the effectiveness of the proposed methods. [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]

In complex scenes, multiple objects are often concealed in cluttered backgrounds. Their saliency is difficult to be detected by using conventional methods, mainly because single color contrast can not shoulder the mission of saliency measure; other image features should be involved in saliency detection to obtain more accurate results. Using Dempster-Shafer (D-S) evidence theory based region classification, a novel method is presented in this paper. In the proposed framework, depth feature information extracted from a coarse map is employed to generate initial feature evidences which indicate the probabilities of regions belonging to foreground or background. Based on the D-S evidence theory, both uncertainty and imprecision are modeled, and the conflicts between different feature evidences are properly resolved. Moreover, the method can automatically determine the mass functions of the two-stage evidence fusion for region classification. According to the classification result and region relevance, a more precise saliency map can then be generated by manifold ranking. To further improve the detection results, a guided filter is utilized to optimize the saliency map. Both qualitative and quantitative evaluations on three publicly challenging benchmark datasets demonstrate that the proposed method outperforms the contrast state-of-the-art methods, especially for detection in complex scenes. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) is one of the most popular data representation methods in the field of computer vision and pattern recognition. High-dimension data are usually assumed to be sampled from the submanifold embedded in the original high-dimension space. To preserve the locality geometric structure of the data, k-nearest neighbor (k-NN) graph is often constructed to encode the near-neighbor layout structure. However, k-NN graph is based on Euclidean distance, which is sensitive to noise and outliers. In this article, we propose a refined-graph regularized nonnegative matrix factorization by employing a manifold regularized least-squares regression (MRLSR) method to compute the refined graph. In particular, each sample is represented by the whole dataset regularized with ℓ2-norm and Laplacian regularizer. Then a MRLSR graph is constructed based on the representative coefficients of each sample. Moreover, we present two optimization schemes to generate refined-graphs by employing a hard-thresholding technique. We further propose two refined-graph regularized nonnegative matrix factorization methods and use them to perform image clustering. Experimental results on several image datasets reveal that they outperform 11 representative methods. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) is one of the most popular data representation methods in the field of computer vision and pattern recognition. High-dimension data are usually assumed to be sampled from the submanifold embedded in the original high-dimension space. To preserve the locality geometric structure of the data, k-nearest neighbor (k-NN) graph is often constructed to encode the near-neighbor layout structure. However, k-NN graph is based on Euclidean distance, which is sensitive to noise and outliers. In this article, we propose a refined-graph regularized nonnegative matrix factorization by employing a manifold regularized least-squares regression (MRLSR) method to compute the refined graph. In particular, each sample is represented by the whole dataset regularized with ℓ2-norm and Laplacian regularizer. Then a MRLSR graph is constructed based on the representative coefficients of each sample. Moreover, we present two optimization schemes to generate refined-graphs by employing a hard-thresholding technique. We further propose two refined-graph regularized nonnegative matrix factorization methods and use them to perform image clustering. Experimental results on several image datasets reveal that they outperform 11 representative methods. [ABSTRACT FROM AUTHOR]

Local binary pattern (LBP) is a frequently‐used texture descriptor. Lots of LBP‐variants have been proposed to improve its performance of representing textures. However, most of them ignore the global and neighbour‐difference information of an image texture. In this study, the authors propose a structural difference histogram representation by fusing the segmented structure pattern (SSP), the refined LBP (RLBP) and the neighbour‐difference pattern (NDP) for texture classification. Particularly, the segmented structure, which contains the global contour information of an image texture, is first constructed to compute its SSP histogram feature. Simultaneously, the RLBP is defined to represent the local texture information. Furthermore, the NDP is presented to describe differences between neighbours of a centre pixel in the local patch of texture images. Experimental results on Brodatz and Columbia‐Utrecht reflectance databases indicate that the proposed method can achieve the satisfactory classification accuracy compared with several representative methods. [ABSTRACT FROM AUTHOR]

As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the vehicle. In this paper, an energy management strategy (EMS) based on a hierarchical power splitting structure and deep reinforcement learning (DRL) is proposed. In the higher layer strategy of the proposed EMS, the UC is employed to supply peak power and recover braking energy through the adaptive filter based on fuzzy control. Then, the integrated DRL and equivalent consumption minimization strategy framework is proposed to optimize the power allocation of fuel cell (FC) and BAT in the lower layer, to ensure the highly efficient operation of FC and reduce hydrogen consumption. And the action trimming based on heuristic technique is proposed to further restrain the adverse effect of sudden peak power on FC lifespan. The simulation results show the proposed EMS can make the output of FC smoother, improve its working efficiency to alleviate the stress of BAT, and increase by 14.8% compared with the Q-learning strategy in fuel economy under WLTP driving cycle. Meanwhile, the obtained results under UDDSHDV show fuel economy of the proposed EMS can reach dynamic programming (DP) benchmark level of 89.7 $\%$. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing is an essential research topic for spectral data analysis due to the existence of mixed pixels. Recently, many methods based on sparse nonnegative matrix factorization (NMF) have been widely used for unmixing by incorporating similarity preserving. However, most of them conduct the similarity learning and unmixing by using two separate steps, which may lead to the case that the learned similarity matrix is not the optimal one for unmixing. Thus, the performance of unmixing would be influenced and become undesirable. To alleviate this problem, we propose an adaptive relationship preserving-based sparse NMF (ARP-NMF) for hyperspectral unmixing. Typically, we regard similarity learning and unmixing as an alternative optimization process. During this process, the learned similarity weights and unmixing results can be mutually improved. Thus, our proposed method can learn the optimal similarity weights for unmixing and obtain better generalization ability for different hyperspectral images than traditional methods. Moreover, by using the spectral and spatial local structure, our ARP-NMF method effectively preserves structure consistency between pixels and abundances. Experimental results both on the synthetic data and the real data reveal that our proposed method outperforms several representative sparse NMF-based methods. [ABSTRACT FROM AUTHOR]

Due to the inconsistent imaging environment, the styles of multitemporal multispectral images (MSIs) are quite different, such as image brightness and transparency. For multitemporal MSIs with different styles, the “same object with different spectra” problem is one of the biggest challenges in change detection. To overcome the challenge, a novel unsupervised spatial–spectral feature learning (FL) framework based on style transformation (ST) (called STFL-CD) is proposed for MSI change detection in this article. For dual-temporal MSIs, the proposed STFl-CD algorithm consists of two phases: ST and spatial–spectral FL. Since the image styles are inconsistent under different imaging environments, the first innovation is to transform the image styles through unmixing and reconstruction. Through ST, the challenge of the “same object with different spectra” problem will be reduced fundamentally. By introducing the attention mechanism, the other innovation is to extract the joint spectral–spatial change features based on a 3-D convolutional neural network with spatial and channel attention. In addition, for multitemporal MSIs, a multitemporal version STFL-CD (MT-STFL-CD) framework is designed based on a recurrent neural network to learn the correlation features between multitemporal remote sensing images. Both of the visual and quantitative results on the real MSI datasets indicate that the proposed unsupervised STFL-CD frameworks have significant advantages on multitemporal MSI change detection. In particular, the performance of the proposed unsupervised STFL-CD algorithm is even comparable to that of the state-of-the-art supervised or semisupervised methods. [ABSTRACT FROM AUTHOR]

Eclogite is first discovered from Qiangtang area, central Tibet, which occurs as a lens in the high-pressure metamorphic belt comprising glaucophane-muscovite schist and garnet-muscovite schist, along the south margin of Lungmu Co-Shuanghu Paleo-Tethys suture. A glaucophane 40Ar/39Ar age of 220 Ma and a phengite 40Ar/39Ar age of 221.9 Ma have been obtained from blueschists associated with eclogite. The rock consists of garnet, omphacite, amphibole, phengite, and rutile, etc, and the jadeite content in omphacite ranges from 32.7% to 38.95%. It is estimated that the temperature of the eclogite is <500°C and pressure varies in the range of 1.56-2.35 GPa. The discovery of Qiangtang ecologite further supports that there is a suture zone of Indo-Chinese epoch in central Qiangtang area, and is very important for discssing the evolution of the pa- leo-ocean and paleo-plate tectonics in this area. [ABSTRACT FROM AUTHOR]

Our review paper (Xu et al., 2020) reconstructed the Cambrian–Triassic tectonic evolution of the Qiangtang Block. However, the recent comments from Zeng and Shi (2020) disagreed with some interpretations in our model, including the affiliation of Early Paleozoic magmatic rocks within the Qiangtang Block and Paleozoic continent-continent collisions involving the Qiangtang Block. This reply focuses on the scientific logic of the geological evidence presented by Zeng and Shi (2020) and responds to the individual comments. Finally, we realize that these criticisms enhanced our understanding of the Qiangtang Block, but did not challenge the geodynamic model we proposed. [ABSTRACT FROM AUTHOR]

Central Qiangtang is a tectonic mélange between the northern Qiangtang terrane (NQT) and the southern Qiangtang terrane (SQT), and it preserves a complete Cambrian to Triassic geological record related to Tethyan evolution (e.g., high-pressure metamorphic rocks and ophiolites); thus, this region is the key to understanding the geodynamic processes that occurred during the dispersion of Gondwana and the accretion of Laurussia. Given previous studies on sedimentary, paleomagnetic and seismic records, this paper presents an integrated interpretation of ophiolites, magmatism, metamorphism and tectonism and reconstructs the Cambrian-Triassic tectonic evolution of central Qiangtang as described below. (1) Following the Neoproterozoic-early Cambrian Pan-African orogeny and associated postcollisional extension, the southward subduction of the Proto-Tethys oceanic plate produced Andean-type magmatism along the northern margin of Gondwana. A back-arc basin between the NQT and SQT further opened during the late Cambrian-early Ordovician, accompanying with trench retreat and overriding plate extension resulting from slab rollback. (2) With the middle Ordovician-late Silurian successive incorporation of micro-continental terranes (e.g., Qaidam-Kunlun and Tarim terranes) into the northern margin of Gondwana, the back-arc basin closed through subduction polarity reversal from southward to northward. (3) During the late Devonian-early Carboniferous, the southward subduction of an oceanic plate to the north of the NQT separated the NQT and SQT through back-arc extension. Consequently, the NQT drifted northward rapidly and the back-arc basin gradually evolved into a mature ocean basin (i.e., Longmu Co-Shuanghu Paleo-Tethys Ocean, LSPTO). (4) Subsequently, a late Carboniferous-early Permian continent-continent collision between the NQT and the South China Block (SCB) resulted in the northward subduction of the LSPTO, and the ongoing subduction probably induced the opening of a back-arc basin between the NQT and the SCB during the late Permian. (5) Finally, diachronous collision between the NQT and SQT closed the LSPTO during the early-middle Triassic, and the termination of the collisional orogenic event was evidenced by strong late Triassic magmatism related to postcollisional extension in the Qiangtang Block. In this paper, we propose that the opening and closure of back-arc basins controlled the Cambrian-Triassic tectonic evolution of central Qiangtang, which provides significant insights into the dispersion of Gondwana and the accretion of Laurussia. [ABSTRACT FROM AUTHOR]

The Palaeoproterozoic tectonic evolution of the Jiao–Liao–Ji Belt (JLJB) in the North China Craton remains controversial, particularly in the absence of adequate evidence for oceanic plate subduction. Here, we report SHRIMP U–Pb zircon ages, whole‐rock geochemistry, and Sm–Nd isotope data on felsic tuffs from the Anshan–Helan–Lianshanguang area in the Liaodong Peninsula which provides important insights into the early tectonic evolution of the JLJB. The zircon SHRIMP U–Pb ages suggest formation of the tuffs crystallized at ca. 2.17 Ga, which coincide with the crystallization of the voluminous Liaoji granitoids in this belt. The felsic tuffs show calc‐alkaline features and are characterized by a wide range of SiO2 (61.89–72.55 wt.%), MgO (1.35–5.80 wt.%), Cr (36.0–67.8 ppm) and Ni (5.88–28.8 ppm) contents, and low TiO2 (0.42–0.64 wt.%) and total Fe2O3 (2.14–5.64 wt.%) concentrations. They also display enrichment in LREE and depletion in Nb, Ta, and Ti similar to the features of continental arc andesites. Their wide variation in whole‐rock εNd(t) (−2.6 to +3.4) combined with the synchronous magmatic activity in this region suggests that they were derived from mixing of mafic (e.g., those in the Liaohe suite) and felsic (e.g., the Liaoji granitoids) magmas, followed by fractional crystallization. In view of the widespread distribution of magmatism, including the Helan felsic tuffs and Liaoji granitoids, and the synchronous sedimentary rocks in the Liaodong Peninsula, we propose that the evolution of the JLJB involved an initial back‐arc extension stage, followed by arc‐continent collision. [ABSTRACT FROM AUTHOR]

We introduce a group of virtual sources for generating Lommel–Gauss beams based on beam superposition to analyze nonparaxial propagation. We typically derive the paraxial approximation and integral representations of the nth-order Lommel–Gauss beams. The first three orders of the nonparaxial corrections for the on-axis field of the Lommel–Gauss beams are analytically obtained. The on-axis intensity distribution of the corresponding nonparaxial corrections is also provided. [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]

Highlights • Coarse saliency is measured via saliency prior learning and feature coding. • LLsC-RC is used for enhancing the separability of foreground and background. • Noise are reduced owing to the optimization of adjacency graph. • High precision achieves the state-of-the-art level on SED2, ECSSD and DUT_OMRON. Abstract Recent years have witnessed great progress of salient object detection methods. However, due to the emerging complex scenes, two problems should be solved urgently: one is on the fast locating of the foreground while preserving the precision, and the other is about reducing the noise near the foreground boundary in saliency maps. In this paper, a hybrid method is proposed to ameliorate the above two issues. At first, to reduce the essential runtime of integrating the prior knowledge, a novel Prior Knowledge Learning based Region Classification (PKL-RC) method is proposed for classifying image regions and preliminarily locating foreground; furthermore, to generate more accurate saliency, a Locality-constrained Linear self-Coding based Region Clustering (LLsC-RC) model is proposed to improve the adjacency structure of the similarity graph for Manifold Ranking (MR). Experimental results demonstrate the effectiveness and superiority of the proposed method in both higher precision and better smoothness. [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]

Objectives This study investigated the effect of tannic acid ( TA), a plant-derived hydrolyzable polyphenol, on Kv7.4 and Kv7.5 K+ channels and rat mesenteric artery. Methods Whole-cell patch clamp experiments were used to record the Kv7.4 and Kv7.3/7.5 K+ currents expressed in HEK293 cells; and the tension changes of mesenteric arteries isolated from rats were recorded using small vessel myography apparatus. Key findings Tannic acid increases the Kv7.4 and Kv7.3/7.5 K+ currents in a concentration-dependent manner (median effective concentration ( EC50) = 27.3 ± 3.6 μ m and EC50 = 23.1 ± 3.9 μ m, respectively). In addition, 30 μ m TA shifts the G- V curve of Kv7.4 and Kv7.3/7.5 K+ currents to the left by 14.18 and 25.24 mV, respectively, and prolongs the deactivation time constants by 184.44 and 154.77 ms, respectively. Moreover, TA relaxes the vascular tension of rat mesenteric arteries in a concentration-dependent manner (half inhibitory concentration ( IC50) = 148.7 ± 13.4 μ m). Conclusion These results confirms the vasodilatory effects of TA on rat mesenteric artery and the activating effects on the Kv7.4 and Kv7.3/7.5 K+ channels, which may be a mechanism to explain the vasodilatory effect and this mechanism can be used in the research of antihypertension. [ABSTRACT FROM AUTHOR]

Crocin, a carotenoid component of Crocus sativus L. belonging to the Iridaceae family, has demonstrated cardioprotective effects. To investigate the cellular mechanisms of these cardioprotective effects, here we studied the influence of crocin on L-type Ca2+current ( ICa-L), intracellular Ca2+ ([Ca2+]i), and contraction of isolated rat cardiomyocytes by using the whole-cell patch-clamp technique and video-based edge detection and dual excitation fluorescence photomultiplier systems. Crocin inhibited ICa-L in a concentration-dependent manner with the half-maximal inhibitory concentration (IC50) of 45 μmol/L and the maximal inhibitory effect of 72.195% ± 1.54%. Neither current-voltage relationship of ICa-L, reversal potential of ICa-L, nor the activation/inactivation of ICa-L was significantly changed. Crocin at 1 μmol/L reduced cell shortening by 44.64% ± 2.12% and the peak value of the Ca2+ transient by 23.66% ± 4.52%. Crocin significantly reduced amplitudes of myocyte shortening and [Ca2+]i with an increase in the time to reach 10% of the peak (Tp) and a decrease in the time to 10% of the baseline (Tr). Thus, the cardioprotective effects of crocin may be attributed to the attenuation of [Ca2+]i through the inhibition of ICa-L in rat cardiomyocytes and negative inotropic effects on myocardial contractility. [ABSTRACT FROM AUTHOR]

The electrical conductivity of Tibetan eclogite was investigated at pressures of 1.5-3.5 GPa and temperatures of 500-803 K using impedance spectroscopy within a frequency range of 10-10 Hz. The electrical conductivity of eclogite increases with increasing temperature (which can be approximated by the Arrhenius equation), and is weakly affected by pressure. At each tested pressure, the electrical conductivity is weakly temperature dependent below ∼650 K and more strongly temperature dependent above ∼650 K. The calculated activation energies and volumes are 44±1 kJ/mol and −0.6±0.1 cm/mol for low temperatures and 97±3 kJ/mol and −1.2±0.2 cm/mol for high temperatures, respectively. When applied to the depth range of 45-100 km in Tibet, the laboratory data give conductivities on the order of 10-10 S/m, within the range of geophysical conductivity profiles. [ABSTRACT FROM AUTHOR]