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Type 2 diabetes mellitus (T2DM) is closely linked to cognitive decline and alterations in brain structure and function. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to diagnose neurodegenerative diseases, such as cognitive impairment (CI), Alzheimer's disease (AD), and vascular dementia (VaD). However, whether the functional connectivity (FC) of patients with T2DM and mild cognitive impairment (T2DM-MCI) is conducive to early diagnosis remains unclear. To answer this question, we analyzed the rs-fMRI data of 37 patients with T2DM and mild cognitive impairment (T2DM-MCI), 93 patients with T2DM but no cognitive impairment (T2DM-NCI), and 69 normal controls (NC). We achieved an accuracy of 87.91% in T2DM-MCI versus T2DM-NCI classification and 80% in T2DM-NCI versus NC classification using the XGBoost model. The thalamus, angular, caudate nucleus, and paracentral lobule contributed most to the classification outcome. Our findings provide valuable knowledge to classify and predict T2DM-related CI, can help with early clinical diagnosis of T2DM-MCI, and provide a basis for future studies. [ABSTRACT FROM AUTHOR]

Due to the lack of adequate training data and sufficient mining of prior knowledge related to perceived quality, most existing image quality assessment (IQA) methods show limited generalization performance. In this paper, we study the prior knowledge from the factors affecting perceived quality, and introduce a novel progressive multi-task learning based blind IQA method. Inspired by the definition of IQA: human comprehensive perception for degradation of image content, we firstly deconstruct IQA into three elements, i.e., image content, pattern of degradation, and intensity of degradation. Based on these elements, we design the corresponding auxiliary tasks for instructing the network to learn IQA. By statistical analysis on a great deal of data, we find that there is progressive relevance among the four tasks. Furthermore, we mathematically derive that introducing the progressive relevance into a multi-task learning network can effectively constrain the hypothesis space of the main task. Under the guidance of the derivation, we propose an end-to-end IQA framework based on progressive multi-task learning. Experimental results demonstrate the excellent generalization capability of the proposed method, which achieves state-of-the-art performance against these existing IQA methods. [ABSTRACT FROM AUTHOR]

Blind video quality assessment (BVQA) is of great importance for video-related applications, yet still challenging even in this deep learning era. The difficulty lies in the shortage of large-scale labeled data, thus making it hard to train a robust spatiotemporal encoder for BVQA. To relieve such difficulty, we first build a video dataset, which contains over 320K samples suffering from various compression and transmission artifacts. While manually annotating the dataset with subjective perception is much labor-intensive and time-consuming, we adopt reference-based VQA algorithms to weakly label the data automatically. We consider that single weak label is derived from single knowledge, which is deficient and incomplete for VQA. To alleviate the bias from single weak label (i.e., single knowledge) in the weakly labeled dataset, we propose HEterogeneous Knowledge Ensemble (HEKE) for spatiotemporal representation learning. Compared to learning from single knowledge, learning with HEKE is thought to achieve a lower infimum theoretically, and obtain richer representation. On the basis of the built dataset and the HEKE methodology, a feature encoder specific to BVQA is formed, and directly extract spatiotemporal representation from videos. Then, the video quality can be either acquired in a completely BVQA manner without ground truth, or via a finetuning-based regressor with labels. Extensive experiments on various VQA databases show that our BVQA model with the pretrained encoder achieves the state-of-the-art performance. More surprisingly, even trained on the synthetic data, our model still shows competitive performance on authentic databases. The data and source code will be available at https://github.com/Sissuire/BVQA-HEKE. [ABSTRACT FROM AUTHOR]

Blind image quality assessment (BIQA) is a useful but challenging task. It is a promising idea to design BIQA methods by mimicking the working mechanism of human visual system (HVS). The internal generative mechanism (IGM) indicates that the HVS actively infers the primary content (i.e., meaningful information) of an image for better understanding. Inspired by that, this paper presents a novel BIQA metric by mimicking the active inference process of IGM. Firstly, an active inference module based on the generative adversarial network (GAN) is established to predict the primary content, in which the semantic similarity and the structural dissimilarity (i.e., semantic consistency and structural completeness) are both considered during the optimization. Then, the image quality is measured on the basis of its primary content. Generally, the image quality is highly related to three aspects, i.e., the scene information (content-dependency), the distortion type (distortion-dependency), and the content degradation (degradation-dependency). According to the correlation between the distorted image and its primary content, the three aspects are analyzed and calculated respectively with a multi-stream convolutional neural network (CNN) based quality evaluator. As a result, with the help of the primary content obtained from the active inference and the comprehensive quality degradation measurement from the multi-stream CNN, our method achieves competitive performance on five popular IQA databases. Especially in cross-database evaluations, our method achieves significant improvements. [ABSTRACT FROM AUTHOR]

The deep convolutional neural network (CNN) has achieved great success in image recognition. Many image quality assessment (IQA) methods directly use recognition-oriented CNN for quality prediction. However, the properties of IQA task is different from image recognition task. Image recognition should be sensitive to visual content and robust to distortion, while IQA should be sensitive to both distortion and visual content. In this paper, an IQA-oriented CNN method is developed for blind IQA (BIQA), which can efficiently represent the quality degradation. CNN is large-data driven, while the sizes of existing IQA databases are too small for CNN optimization. Thus, a large IQA dataset is firstly established, which includes more than one million distorted images (each image is assigned with a quality score as its substitute of Mean Opinion Score (MOS), abbreviated as pseudo-MOS). Next, inspired by the hierarchical perception mechanism (from local structure to global semantics) in human visual system, a novel IQA-orientated CNN method is designed, in which the hierarchical degradation is considered. Finally, by jointly optimizing the multilevel feature extraction, hierarchical degradation concatenation (HDC) and quality prediction in an end-to-end framework, the Cascaded CNN with HDC (named as CaHDC) is introduced. Experiments on the benchmark IQA databases demonstrate the superiority of CaHDC compared with existing BIQA methods. Meanwhile, the CaHDC (with about 0.73M parameters) is lightweight comparing to other CNN-based BIQA models, which can be easily realized in the microprocessing system. The dataset and source code of the proposed method are available at https://web.xidian.edu.cn/wjj/paper.html. [ABSTRACT FROM AUTHOR]

Though blind image quality assessment (BIQA) is highly desired in perceptual-oriented image processing systems, it is extremely difficult to design a reliable BIQA method. With the help of the prior knowledge, the human visual system (HVS) hierarchically perceives the quality degradation during the visual recognition. Inspired by this, we suggest different levels of distortion generate individual degradations on hierarchical features, and propose to consider the degradations on both low and high level features for quality prediction. By mimicking the orientation selectivity (OS) mechanism in the primary visual cortex, an OS based local structure is designed for low-level visual information representation. At the meantime, the deep residual network, which possesses multiple levels for feature integration, is employed to extract the deep semantics for high-level visual content representation. By fusing the local structure and the deep semantics, a hierarchical feature set is acquired. Next, the correlations between the degradations of image qualities and their corresponding hierarchical feature sets are analyzed, and a novel hierarchical feature degradation (HFD) based BIQA (HFD-BIQA) method is built. Experimental results on the legacy and wild image quality assessment databases demonstrate the prediction accuracy of the proposed HFD-BIQA method, and verify that the HFD-BIQA performs highly consistent with the subjective perception. [ABSTRACT FROM AUTHOR]

Highlights • A novel blind image quality assessment is proposed. • The proposed method evaluates distorted image from a completely new perspective of human subjective perception. • The experiment results demonstrate the superiority of the proposed method. Abstract No-reference (NR) image quality assessment (IQA) aims to evaluate the quality of an image without reference image, which is greatly desired in the automatic visual signal processing system. Distortions degrade the visual contents and affect the semantics acquisition during the process of human perception. Although the existing methods evaluate the quality of images based on the structure, texture, or statistical characteristics, and deliver high quality prediction accuracy, they do not take the spatial semantics into account. From the perspective of human perception, distortions decrease the structural semantics that represent the structural information, and disturb the spatial semantics that describe the contents of images. Therefore, we attempt to measure the image quality by its degradation of semantics in an image. To extract the semantics of an image, a semantic network is proposed. The network contains convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) that correspond to structural semantics and spatial semantics, respectively. CNN can be regarded as a coarse imitation of human visual mechanism to obtain the structural information, and LSTM can express the contents of an image. Then, by measuring the degradations of different semantics on images, a novel NR IQA is introduced. The proposed approach is evaluated on the databases of LIVE, CSIQ, TID2013, and LIVE multiply distorted database as well as LIVE in the wild image quality challenge database, and the results show superior performance to other state-of-the-art NR IQA methods. Furthermore, we explore the generalization capability of the proposed approach, and the experimental results indicate the proposed approach has a high robustness. [ABSTRACT FROM AUTHOR]

Strategies to control biofouling without using antimicrobial chemicals are needed to prevent the spread of antibiotic resistance genes and disruption of microbial activities in biological treatment. This study developed an environmentally friendly biofouling resistant membrane by incorporating d -tyrosine onto a commercial nanofiltration membrane using FAU type zeolite nanoparticles that covalently bound to the membrane surface as carriers for slow release. The d -tyrosine loaded membrane had similar water permeability as the unmodified membrane, but greatly reduced initial cell attachment and strongly inhibited subsequent biofilm formation without inactivating the bacteria. The membrane slowly released d -tyrosine in the time course of over 5 days, and retained its anti-biofouling capability in repeated 24 h efficacy tests for as long as 6 days. In nanofiltration operation, the d -tyrosine incorporated zeolite coating completely inhibited cell adhesion on the membrane surface and significantly alleviated membrane flux decline. [ABSTRACT FROM AUTHOR]

The just noticeable difference (JND) in an image, which reveals the visibility limitation of the human visual system (HVS), is widely used for visual redundancy estimation in signal processing. To determine the JND threshold with the current schemes, the spatial masking effect is estimated as the contrast masking, and this cannot accurately account for the complicated interaction among visual contents. Research on cognitive science indicates that the HVS is highly adapted to extract the repeated patterns for visual content representation. Inspired by this, we formulate the pattern complexity as another factor to determine the total masking effect: the interaction is relatively straightforward with a limited masking effect in a regular pattern, and is complicated with a strong masking effect in an irregular pattern. From the orientation selectivity mechanism in the primary visual cortex, the response of each local receptive field can be considered as a pattern; therefore, in this paper, the orientation that each pixel presents is regarded as the fundamental element of a pattern, and the pattern complexity is calculated as the diversity of the orientation in a local region. Finally, considering both pattern complexity and luminance contrast, a novel spatial masking estimation function is deduced, and an improved JND estimation model is built. Experimental results on comparing with the latest JND models demonstrate the effectiveness of the proposed model, which performs highly consistent with the human perception. The source code of the proposed model is publicly available at http://web.xidian.edu.cn/wjj/en/index.html. [ABSTRACT FROM AUTHOR]

Silver as a broad spectrum antimicrobial agent has been widely studied as anti-biofouling coatings in membrane surface modifications. Incorporation of Ag in nano-zeolite grafted on the membrane surface was recently demonstrated to provide sustainable and regenerable antimicrobial activity. In this study, we examined two new methods of forming the Ag-zeolite based antimicrobial coating using polyvinyl alcohol (PVA) and polydopamine (PDA), respectively. Both methods achieved a wide range of Ag loading. Silver release and bacterial exposure experiments showed that increasing Ag-zeolite loading accelerated Ag release, which led to stronger antimicrobial activity, but did not significantly extend the antimicrobial lifetime of the membrane. Reduction of Ag + to Ag(0), however, effectively stabilized Ag, reduced Ag release rate, and significantly prolonged the antimicrobial efficacy against bacterial growth on the membrane surface. All coatings strongly inhibited cell attachment and growth on the membrane surface despite the bacterial growth in the suspension, demonstrating the benefit of surface coating versus disinfection pretreatment of feed water. These results show the potential of polymer/Ag-zeolite nanocomposite coatings as versatile approach towards long lasting and regenerable anti-biofouling activity in membrane systems. They also highlight the importance of implementing slow-release mechanism in high Ag-zeolite loading samples for efficient utilization of loaded Ag. [ABSTRACT FROM AUTHOR]

Reduced-reference (RR) image quality assessment (IQA) aims to use less reference data for quality evaluation. Global features are demanded to effectively express quality degradation caused by distortion. Since the human visual system (HVS) is highly sensitive to structure degradation, we suggest to represent the visual content of an image with several structural patterns. Also, image quality is measured based on the structural degradation on these patterns. Firstly, the spatial correlation of image structure is analyzed with the local binary pattern (LBP), and several representative structural patterns are extracted. Then, the energy changes on these patterns between the reference image and the distorted image are calculated. Finally, the support vector regression (SVR) procedure is adopted for feature pooling, and the quality score of the input image is returned. Experimental results for three publicly available databases demonstrate that the proposed RR IQA method is highly consistent with the human perception, which uses a small amount of reference data (20 values) and achieves a promising improvement on quality evaluation accuracy. [ABSTRACT FROM AUTHOR]

Control of microbial growth at the membrane surface on a continuous basis is needed to mitigate biofouling in long term membrane applications. In this study, a regenerable antimicrobial nanocomposite membrane was developed for sustainable membrane biofouling control via slow release of silver. Nanozeolites attached to the surface of a commercial polyamide nanofiltration membrane by covalent binding served as carriers for Ag + or Ag(0). At a silver loading of 7.18±0.62 mg/m 2 , the nanocomposite membranes effectively inhibited growth of P. aeruginosa for up to seven 24 h exposure cycles, and the antimicrobial efficacy correlated well with silver release kinetics data. The release of silver strongly depended on the Cl − concentration in the solution. At low Cl − concentrations, the low aqueous solubility of silver controlled silver release; the Ag + and Ag(0) loaded membranes showed similar release rate. At high Cl − concentration, silver release from the Ag(0) loaded membrane was notably more stable and longer lasting as Ag(0) oxidation became the rate-limiting step. After depletion, reloading silver via a simple physisoprtion protocol resumed the antimicrobial activity. The regenerated membranes had similar silver loading and antimicrobial activity as the freshly prepared membranes, demonstrating the potential of this approach in long-term control of membrane biofouling. [ABSTRACT FROM AUTHOR]

Coding artifacts are annoying in highly compressed signals. Most of the existing artifact reduction methods are designed for one specific type of artifacts, codecs, and bitrates, which are complex and exclusive for one type of artifact reduction. Since both the compressed image/video and the coding error contain information of the original signal, they are highly correlated. Therefore, we try to recover some lost data based on the correlation between the compressed signal and the coding error, and introduce a novel and universal artifact reduction method. Firstly, according to the spatial correlation among pixels, a pixel-adaptive anisotropic filter is designed to reconstruct the distorted signal. Next, a globally optimal filter is designed to further recover the coding loss. Experimental results demonstrate that within an extensive range of bitrates, the proposed method achieves about 0.8 dB, 0.45 dB, 0.3 dB, and 0.2 dB on average of PSNR improvement for JPEG, MPEG4, H.264/AVC, and HEVC compressed signals, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] The Jiaodong gold province hosts world-class gold deposits with giant resources of >5000 t, where the Jiaojia-Xincheng fault belt and the Sanshandao-Cangshang fault belt occupy more than two-thirds of the gold deposits. Recent developments of exploration have advanced in tracking deep mineralization. The nature of the ore-forming fluid, source, and the extreme enrichment mechanism of the gold in this region remain debated. Here, we investigate the deep ore body of the Qianchen gold deposit at the southern part of the Jiaojia gold belt based on mineralogy, fluid inclusion, elemental and isotopic composition of pyrite, and the primary halo geochemistry. Natural gold is the important form of Au in this deposit with a relatively large (up to 200 μm) grain size, suggesting the prospect of mineralization at deeper domains. The accompanying PGE minerals (Hexaferrum) hint at contributions from mantle material. The ore-forming fluids of the Qianchen gold deposit are characterized by medium–low temperatures and salinities and have a general H 2 O–NaCl–CO 2 ± CH 4 composition, which indicates that the Jiaodong gold province has a unified ore-forming fluid system from shallow to deep. However, the trace elements and S isotopes of pyrite at different elevations show slight differences, suggesting the composition and properties of the fluid changed during the process of ascending, affected by ore-forming conditions such as temperature, pressure, and oxygen fugacity. Furthermore, the evidence of a decrease of salinity with depth and temperature and fluid immiscibility indicates that fluids from different sources continuously mixed during the upward migration of fluid, with the mineralization triggered possibly by phase separation. The primary halo model suggests promising prospecting potential. We formulate a comprehensive metallogenic model for the mineralization process in the Jiaodong gold province. The Au-bearing hydrous magma derived from the partial melting of the metasomatized SCLM, underwent further magmatic-hydrothermal differentiation and fluid exsolution, and thus the Au was enriched and partitioned into exsolved fluids that evolved to auriferous fluids. During the upward migration of ore-forming fluids (magmatic-hydrothermal), many factors affected mineralization, mainly including fluid-rock interaction and mixing of the metamorphic-hydrothermal and meteoric fluids. The fluid activities, multiple sources of Au in the mantle and ancient basement, together with the large fault structures resulted in the formation of the giant Jiaodong gold province. [ABSTRACT FROM AUTHOR]

Abstract: Despite extensive studies on the relationship between membrane surface properties and fouling, the roles of specific surface functional groups in RO membrane fouling under different solution conditions are still poorly understood. This research aims to elucidate how specific surface functional groups affect RO membrane fouling by alginate in solution conditions relevant to seawater desalination. Common RO membrane surface functionalities are represented by self-assembled mono-layers with –COOH, –NH2, –CONH2 and –OH ending groups. Alginate adsorption and removal was studied under relevant solution conditions using quartz crystal microbalance with dissipation monitoring (QCM-D). The results show that ionic composition has great impacts on alginate–alginate interaction, which is more important than membrane surface chemistry in determining alginate adsorption equilibrium at the high ionic strength of seawater. The initial adsorption rate is highly sensitive to surface chemistry. The –NH2 group has strong affinity for alginate, yet the –COOH group exhibits the highest adsorption rate in simulated seawater. The reversibility of the adsorbed layer was influenced by both alginate–surface interaction and the structure of the layer. The large alginate aggregates formed in the presence of Ca2+ result in loosely packed and less viscous adsorbed layer on the surface, which is easier to remove by surfactant cleaning. [Copyright &y& Elsevier]

A model of visual masking, which reveals the visibility of stimuli in the human visual system (HVS), is useful in perceptual based image/video processing. The existing visual masking function mainly considers luminance contrast, which always overestimates the visibility threshold of the edge region and underestimates that of the texture region. Recent research on visual perception indicates that the HVS is sensitive to orderly regions that possess regular structures and insensitive to disorderly regions that possess uncertain structures. Therefore, structural uncertainty is another determining factor on visual masking. In this paper, we introduce a novel pattern masking function based on both luminance contrast and structural uncertainty. Through mimicking the internal generative mechanism of the HVS, a prediction model is firstly employed to separate out the unpredictable uncertainty from an input image. In addition, an improved local binary pattern is introduced to compute the structural uncertainty. Finally, combining luminance contrast with structural uncertainty, the pattern masking function is deduced. Experimental result demonstrates that the proposed pattern masking function outperforms the existing visual masking function. Furthermore, we extend the pattern masking function to just noticeable difference (JND) estimation and introduce a novel pixel domain JND model. Subjective viewing test confirms that the proposed JND model is more consistent with the HVS than the existing JND models. [ABSTRACT FROM AUTHOR]

Objective image quality assessment (IQA) aims to evaluate image quality consistently with human perception. Most of the existing perceptual IQA metrics cannot accurately represent the degradations from different types of distortion, e.g., existing structural similarity metrics perform well on content-dependent distortions while not as well as peak signal-to-noise ratio (PSNR) on content-independent distortions. In this paper, we integrate the merits of the existing IQA metrics with the guide of the recently revealed internal generative mechanism (IGM). The IGM indicates that the human visual system actively predicts sensory information and tries to avoid residual uncertainty for image perception and understanding. Inspired by the IGM theory, we adopt an autoregressive prediction algorithm to decompose an input scene into two portions, the predicted portion with the predicted visual content and the disorderly portion with the residual content. Distortions on the predicted portion degrade the primary visual information, and structural similarity procedures are employed to measure its degradation; distortions on the disorderly portion mainly change the uncertain information and the PNSR is employed for it. Finally, according to the noise energy deployment on the two portions, we combine the two evaluation results to acquire the overall quality score. Experimental results on six publicly available databases demonstrate that the proposed metric is comparable with the state-of-the-art quality metrics. [ABSTRACT FROM PUBLISHER]

Abstract: Effective control of biofouling is necessary for sustainable application of reverse osmosis and nanofiltration in seawater desalination as well as water and wastewater treatment. In this study, the effectiveness and mechanisms of d-tyrosine in controlling biofouling of a polyamide nanofiltration (NF) membrane by a model biofilm bacterium, Pseudomonas aeruginosa, were investigated. The impact of d-tyrosine on cell attachment and biofilm formation of P. aeruginosa was studied in both batch and continuous flow filtration systems. d-tyrosine was found to strongly inhibit P. aeruginosa attachment and biofilm formation on the NF membrane without impacting bacterial growth. When continuously supplemented to the membrane feed water in a bench scale NF system, d-tyrosine prevented irreversible biofouling by prohibiting adhesion and biofilm formation of P. aeruginosa on the NF membrane. Such inhibitive effect, however, may not be reflected in the membrane flux behavior during one single filtration cycle when the bacterial concentration in the system is high due to the dominant effect of cake enhanced osmotic pressure. The effective concentration of d-tyrosine was as low as 3μM, although the effectiveness of biofouling control by d-tyrosine strongly depended on the ratio of d-amino acid concentration to bacterial cell number. [Copyright &y& Elsevier]

Abstract: In this paper we present a redundancy reduction based approach for computational bottom-up visual saliency estimation. In contrast to conventional methods, our approach determines the saliency by filtering out redundant contents instead of measuring their significance. To analyze the redundancy of self-repeating spatial structures, we propose a non-local self-similarity based procedure. The result redundancy coefficient is used to compensate the Shannon entropy, which is based on statistics of pixel intensities, to generate the bottom-up saliency map of the visual input. Experimental results on three publicly available databases demonstrate that the proposed model is highly consistent with the subjective visual attention. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a novel just noticeable difference (JND) threshold estimation model based on a spatial masking function taking both luminance difference and structural regularity into account. Existing spatial masking functions underestimate the JND threshold for irregular textural regions, because they mainly consider the amplitude of luminance change for simplicity. As regular areas show weak masking effect due to their self-similar structures while irregular regions present strong masking effect, the spatial structure directly determines spatial masking. To effectively measure structural regularity in images under different contents, we propose an adaptive non-local self-similarity analysis based procedure. Then we weight luminance differences with similarity coefficients and deduce a new spatial masking function. Finally, an accurate JND estimation model is introduced. Experimental results demonstrate that the proposed JND model has a better visual effect than other models: it injects much noise into the insensitive regions, whereas little into the sensitive regions. [Copyright &y& Elsevier]

• Mineralization and magmatism occurred simultaneously in the Middle Triassic. • Four stages of mineralization are identified, which have different genesis. • Gold shows a genetic relationship with As, and an inverse correlation with Co and Ni. • The Yanjingou gold deposit is a shallow denudation orebody. • The Yanjingou gold deposit is an intrusion-related orogenic gold deposit. [Display omitted] The Wulonggou gold district is one of the most important gold producers of the East Kunlun metallogenic belt in China. Due to its complex geological background and limited investigations, the age of mineralization and ore-forming process remain unclear. Here, we focus on the Yanjingou gold deposit which is the largest and most typical deposit in the Wulonggou gold district, with a view to investigate the metallogenic mechanism and timing of ore formation with a view to evaluate the ore-forming processes and sources through a comprehensive study of the ore mineralogy, monazite and zircon U-Pb dating, in situ trace element analysis and mapping, and in situ S isotopes. Our results show that the magmatism and mineralization were coeval, during 239 Ma and 244 Ma in the Middle Triassic. Four stages of mineralization are identified with distinct textures, mineral assemblages, trace element characteristics and S isotopes. Stage 1 is represented by euhedral pyrite bearing quartz veins, which originated from deep metamorphic fluid, and was rarely affected by magmatic hydrothermal fluids. Stage 2 is characterized by the occurrence of abundant oriented arsenopyrite, which originated from the mixture of deep metamorphic fluid and magmatic hydrothermal fluid. Stage 3 is characterized by the occurrence of polymetallic sulfide of copper, with two generation of pyrite (stage 3 Py1 and Py2). Stage 3 Py1 formed from magmatic hydrothermal fluids whereas Py2 originated from deep metamorphic fluid and was also slightly influenced by magmatic hydrothermal fluids. Stage 4 is represented by late calcite and pyrite combination, which originated from another stage of deep metamorphic fluid. Stage2 and 3 are the main gold mineralization stages, and Au has a close relationship with As, and an obvious inverse correlation with Co and Ni. Combined with the trace element characteristics, we infer that the denudation degree of the Yanjingou gold deposit is very shallow with good prospects for exploration. Through multi-proxy investigation of the different stages and types of pyrite, we postulate that the Yanjingou gold deposit is an orogenic gold deposit related to intrusive rocks formed during Middle Triassic compressional transformation. [ABSTRACT FROM AUTHOR]

The Wulonggou Au district in the East Kunlun Orogen is one of the most important Au producing regions in China. The Yanjingou Au deposit occurs within a shear zone in the northeastern Wulonggou Au district. Based on detailed field investigations, geochemical data for the primary halo, and in situ thermoelectric data for pyrite, the following key results were obtained: (1) the Yanjingou Au deposit has the fractured-altered-rock type gold mineralization that is arsenopyrite-rich; (2) elemental correlations and cluster analysis show that Au and As are the most diagnostic elements; (3) geochemical data for the primary halo indicate the deposit is a shallow supra-ore halo ore body; and (4) in situ pyrite thermoelectric data show that the proportion of P-type pyrite is >80% and the detachment rate is 50%, which can be inferred that the location of the ore body is shallow. Based on our data, we present a mineralization prediction model for the ore body. The Yanjingou Au deposit has a good mineralization and high prospecting potential, with at least half of the ore body being concealed at depth, which has important scientific guiding significance for the breakthrough of prospecting and exploration. [ABSTRACT FROM AUTHOR]

The destruction of the North China Craton (NCC) culminated during the Early Cretaceous, inducing widespread magmatism and related mineralization in the eastern China. The Shizhuzi magmatic complex (SMC) from the Liaodong Peninsula is typical products of this event, thus could provide important insights on the destruction process. Here, we present petrological, geochronological, and elemental and isotopic geochemical data for the SMC rocks in order to reveal their magma sources, petrogenesis and the possible geodynamic process. The complex comprises quartz diorite, granodiorite, and granite, accompanied with Au, Cu and Mo mineral deposits. Zircon U Pb dating shows that the different rocks were coeval with the formation ages of 129–126 Ma, coincident with the associated mineralization. However, field relations, whole-rock major and trace element contents and isotopic compositions indicate different origin for the quartz diorites and the coeval granodiorites and granites. The quartz diorites are calc-alkaline rocks with high MgO, Mg# and low SiO 2. They are enriched in large ion lithophile elements (LILEs) and light rare earth elements (LREEs), and depleted in high field strength elements (HFSEs) with significant negative Nb, Ta and Ti anomalies. These geochemical data and the highly enriched zircon Hf isotopes suggest that they were derived from partial melting of enriched subcontinental lithospheric mantle. The granodiorites and granites from the SMC have high SiO 2 and low MgO contents, suggesting that they were derived from crustal sources. These rocks are I-type rocks with high-K calc-alkaline features. Their primary magma originated from the partial melting of basaltic lower crust and experienced fractional crystallization. The ascending mantle-derived magma provided heat for the melting of the crustal rocks. Based on regional geology, the SMC was generated in the extensional setting related to the roll-back of the subducted Paleo-Pacific plate. The concurrent mantle and crustal melting in the formation of the SMC revealed the loss of stability of the cratonic lithosphere, related to the destruction of the NCC during Early Cretaceous. Besides, the emplacement of SMC might also contribute to the formation of contemporaneous Au, Mo, and Cu deposits. • The SMC was formed in the Early Cretaceous and contributed to Mo–Au–Cu mineralization. • The melting of the mantle and crust occurs simultaneously during the SMC formation. • Two stage evolution: quartz diorite (mantle influenced) to granodiorite and granite. • Hornblende, plagioclase and zircon play an important role in the fractionation. • The SMC was formed in an extensional setting related to the destruction of NCC. [ABSTRACT FROM AUTHOR]

Recent mapping-based motion deblurring methods lack the regularization of prior knowledge, resulting in an over-reliance on the training data and limited generalization ability. As deblurring aims to improve image quality, we quantitatively analyze and further discover the strong correlation between image quality and sharpness. Motivated by the above facts and notable accomplishments of recent no-reference image quality assessment (NR-IQA), we present a novel framework that incorporates quality knowledge into mapping-based deblurring models. Specifically, we extract quality-aware features from NR-IQA models as prior knowledge, and subsequently propose a prediction-based strategy and an encoder-reuse strategy to integrate knowledge into the encoder and decoder, respectively. After training, the model can simultaneously deblur images and predict quality features, indicating that it has grasped the knowledge and validating the effectiveness of the proposed embedding strategies. Extensive experimental results show that embedding quality knowledge consistently improves model performance and the model achieves state-of-the-art intra/cross-dataset results. Code and pre-trained models are available at https://github.com/esnthere/QAMD. • Integrating no-reference image quality assessment into image deblurring. • Prediction-based strategy for encoder knowledge embedding. • Encoder-reuse strategy for decoder knowledge embedding. • Simultaneously deblurring images and predicting quality features. • SOTA intra/cross-dataset test performance. [ABSTRACT FROM AUTHOR]

The Baiyinnuoer Pb–Zn deposit, estimated at 26.57 million metric tonnes (Mt) with grades of 1.77% Pb and 5.21% Zn, is situated in the southern Great Xing'an Range (SGXR) of northeast China. The deposit comprises southern and northern ore belts. The southern belt primarily contains skarn ore bodies (32% of reserves) associated with Triassic granodiorite, while the northern belt is dominated by skarn ore bodies (30% of reserves) related to Triassic diorite porphyry. In addition to Triassic skarn‐type mineralization, the deposit also exhibits Early Cretaceous mineralization. This includes vein‐type occurrences (36% of reserves) within volcanic tuff and syenite porphyry, alongside a small amount of skarn ore bodies (2% of reserves) related to syenite porphyry. LA‐ICP‐MS U–Pb dating of intrusive rocks and garnets was conducted to investigate the timing and genesis of these mineralization events, revealing two distinct stages of skarn‐type mineralization. The first stage, associated with Early Triassic granitoids, dates to 254.6 ± 1.4–241.7 ± 2.7 Ma, while the second stage is closely associated with Early Cretaceous syenite porphyry, constrained to 135.4 ± 1.1–135.2 ± 1.6 Ma. Garnet U–Pb dating in the syenite porphyry‐related skarn yielded an age of 134.9 ± 4.7 Ma; however, due to low U content, reliable dating of garnets associated with Early Triassic granitoid‐related skarn was not feasible. Micro‐thermometry and Laser Raman analyses of fluid inclusions within vein‐type ore bodies highlighted distinct differences between vein‐type mineralization and Early Triassic skarn‐type mineralization. Additionally, in situ sulfur isotope analyses of sphalerite from both Early Triassic and Early Cretaceous ore bodies indicate a magmatic origin for sulfur in both stages. Through comprehensive geological, geochronological, in situ sulfur isotope and fluid inclusion studies, the Baiyinnuoer Pb–Zn deposit is conclusively identified as a large deposit characterized by two distinct periods of mineralization (Early Triassic and Early Cretaceous), akin to several other significant polymetallic deposits in NE China. [ABSTRACT FROM AUTHOR]

The development of neuromorphic optoelectronic systems opens up the possibility of the next generation of artificial vision. In this work, the novel broadband (from 365 to 940 nm) and multilevel storage optoelectronic synaptic thin‐film transistor (TFT) arrays are reported using the photosensitive conjugated polymer (poly[(9,9‐dioctylfluorenyl‐2,7‐diyl)‐co‐(bithiophene)], F8T2) sorted semiconducting single‐walled carbon nanotubes (sc‐SWCNTs) as channel materials. The broadband synaptic responses are inherited to absorption from both photosensitive F8T2 and sorted sc‐SWCNTs, and the excellent optoelectronic synaptic behaviors with 200 linearly increasing conductance states and long retention time > 103 s are attributed to the superior charge trapping at the AlOx dielectric layer grown by atomic layer deposition. Furthermore, the synaptic TFTs can achieve IOn/IOff ratios up to 106 and optoelectronic synaptic plasticity with the low power consumption (59 aJ per single pulse), which can simulate not only basic biological synaptic functions but also optical write and electrical erase, multilevel storage, and image recognition. Further, a novel Spiking Neural Network algorithm based on hardware characteristics is designed for the recognition task of Caltech 101 dataset and multiple features of the images are successfully extracted with higher accuracy (97.92%) of the recognition task from the multi‐frequency curves of the optoelectronic synaptic devices. [ABSTRACT FROM AUTHOR]

The Jianbeigou gold deposit is a typical lode gold deposit in the Qinling metallogenic belt, located on the southern margin of the North China Craton. Three stages of the hydrothermal process can be distinguished, including the quartz ± pyrite, quartz‐polymetallic sulfide, and quartz‐carbonate ± pyrite stages. From the early to late stages, the homogenization temperatures of primary fluid inclusions are 281–362°C, 227–331°C, and 149–261°C, respectively. The corresponding salinities estimated for these fluids are 3.9–9.9 wt%, 0.4–9.4 wt%, and 0.7–7.2 wt% NaCl equiv. Combined with laser Raman spectroscopy data, the ore‐forming fluid belongs to a H2O‐CO2‐NaCl ± CH4 system with medium–low temperature and salinity. The δ18Ofluid and δD values for the quartz veins are –1.0‰ to 6.0‰ and –105‰ to –84‰, respectively, which indicates that the ore‐forming fluid is of mixed source, mainly derived from magma, with a contribution from meteoric water. Pyrite has been identified into three generations based on mineral paragenetic sequencing, including Py1, Py2, and Py3. The pyrites have δ34S sulfur isotopic compositions from three stages between 3.7‰ and 8.4‰, indicating that sulfur mainly originated from magma. Te, Bi, Sb, and Cu contents in pyrite were all high and showed a strong correlation with Au concentrations. Native gold and the Au‐Ag‐Bi telluride minerals were formed concurrently, and the As concentration was low and decoupled from the Au content. Therefore, Te, Bi, Sb and other low‐melting point chalcophile elements play an important role for gold mineralization in arsenic‐deficient ore‐forming fluid. Combined with the geological setting, evolution of pyrite, and ore‐fluids geochemistry, we propose that the Jianbeigou deposit can be classified as a magmatic–hydrothermal lode gold deposit. Gold mineralization on the southern margin of the North China Craton is related to Early Cretaceous magmatism and formed in an extensional setting. [ABSTRACT FROM AUTHOR]

Video quality assessment (VQA) task is an ongoing small sample learning problem due to the costly effort required for manual annotation. Since existing VQA datasets are of limited scale, prior research tries to leverage models pre-trained on ImageNet to mitigate this kind of shortage. Nonetheless, these well-trained models targeting on image classification task can be sub-optimal when applied on VQA data from a significantly different domain. In this paper, we make the first attempt to perform self-supervised pre-training for VQA task built upon contrastive learning method, targeting at exploiting the plentiful unlabeled video data to learn feature representation in a simple-yet-effective way. Specifically, we implement this idea by first generating distorted video samples with diverse distortion characteristics and visual contents based on the proposed distortion augmentation strategy. Afterwards, we conduct contrastive learning to capture quality-aware information by maximizing the agreement on feature representations of future frames and their corresponding predictions in the embedding space. In addition, we further introduce distortion prediction task as an additional learning objective to push the model towards discriminating different distortion categories of the input video. Solving these prediction tasks jointly with the contrastive learning not only provides stronger surrogate supervision signals, but also learns the shared knowledge among the prediction tasks. Extensive experiments demonstrate that our approach sets a new state-of-the-art in self-supervised learning for VQA task. Our results also underscore that the learned pre-trained model can significantly benefit the existing learning based VQA models. Source code is available at https://github.com/cpf0079/CSPT. [ABSTRACT FROM AUTHOR]

Recently, researchers have shown great interest in using convolutional neural networks (CNNs) for no-reference image quality assessment (NR-IQA). Due to the lack of big training data, the efforts of existing metrics in optimizing CNN-based NR-IQA models remain limited. Furthermore, the diversity of distortions in images result in the generalization problem of NR-IQA models when trained with known distortions and tested on unseen distortions, which is an easy task for human. Hence, we propose a NR-IQA metric via deep meta-learning, which is highly generalizable in the face of unseen distortions. The fundamental idea is to learn the meta-knowledge shared by human when evaluating the quality of images with diversified distortions. Specifically, we define NR-IQA of different distortions as a series of tasks and propose a task selection strategy to build two task sets, which are characterized by synthetic to synthetic and synthetic to authentic distortions, respectively. Based on these two task sets, an optimization-based meta-learning is proposed to learn the generalized NR-IQA model, which can be directly used to evaluate the quality of images with unseen distortions. Extensive experiments demonstrate that our NR-IQA metric outperforms the state-of-the-arts in terms of both evaluation performance and generalization ability. [ABSTRACT FROM AUTHOR]

Quality of experience (QoE) that serves as a direct evaluation of viewing experience from the end users is of vital importance for network optimization, and should be constantly monitored. Unlike existing video-on-demand streaming services, real-time interactivity is critical to the mobile live broadcasting experience for both broadcasters and their audiences. While existing QoE metrics that are validated on limited video contents and synthetic stall patterns have shown effectiveness in their trained QoE benchmarks, a common caveat is that they often encounter challenges in practical live broadcasting scenarios, where one needs to accurately understand the activity in the video with fluctuating QoE and figure out what is going to happen to support the real-time feedback to the broadcaster. In this paper, we propose a temporal relational reasoning guided QoE evaluation approach for mobile live video broadcasting, namely TRR-QoE, which explicitly attends to the temporal relationships between consecutive frames to achieve a more comprehensive understanding of the distortion-aware variation. In our design, video frames are first processed by deep neural network (DNN) to extract quality-indicative features. Afterwards, besides explicitly integrating features of individual frames to account for the spatial distortion information, multi-scale temporal relational information corresponding to diverse temporal resolutions are made full use of to capture temporal-distortion-aware variation. As a result, the overall QoE prediction could be derived by combining both aspects. The results of experiments conducted on a number of benchmark databases demonstrate the superiority of TRR-QoE over the representative state-of-the-art metrics. [ABSTRACT FROM AUTHOR]

With the increasing prevalence of free-viewpoint video applications, virtual view synthesis has attracted extensive attention. In view synthesis, a new viewpoint is generated from the input color and depth images with a depth-image-based rendering (DIBR) algorithm. Current quality evaluation models for view synthesis typically operate on the synthesized images, i.e. after the DIBR process, which is computationally expensive. So a natural question is that can we infer the quality of DIBR-based synthesized images using the input color and depth images directly without performing the intricate DIBR operation. With this motivation, this paper presents a no-reference image quality prediction model for view synthesis via COlor-Depth Image Fusion, dubbed CODIF, where the actual DIBR is not needed. First, object boundary regions are detected from the color image, and a Wavelet-based image fusion method is proposed to imitate the interaction between color and depth images during the DIBR process. Then statistical features of the interactional regions and natural regions are extracted from the fused color-depth image to portray the influences of distortions in color/depth images on the quality of synthesized views. Finally, all statistical features are utilized to learn the quality prediction model for view synthesis. Extensive experiments on public view synthesis databases demonstrate the advantages of the proposed metric in predicting the quality of view synthesis, and it even suppresses the state-of-the-art post-DIBR view synthesis quality metrics. [ABSTRACT FROM AUTHOR]

The yeast Saccharomyces cerevisiae able to tolerate lignocellulose-derived inhibitors like furfural. Yeast strain performance tolerance has been measured by the length of the lag phase for cell growth in response to the furfural inhibitor challenge. The aims of this work were to obtain RDS1 yeast tolerant strain against furfural through overexpression using a method of in vivo homologous recombination. Here, we report that the overexpressing RDS1 recovered more rapidly and displayed a lag phase at about 12 h than its parental strain. Overexpressing RDS 1 strain encodes a novel aldehyde reductase with catalytic function for reduction of furfural with NAD(P)H as the co-factor. It displayed the highest specific activity (24.8 U/mg) for furfural reduction using NADH as a cofactor. Fluorescence microscopy revealed improved accumulation of reactive oxygen species resistance to the damaging effects of inhibitor in contrast to the parental. Comparative transcriptomics revealed key genes potentially associated with stress responses to the furfural inhibitor, including specific and multiple functions involving defensive reduction–oxidation reaction process and cell wall response. A significant change in expression level of log2 (fold change >1) was displayed for RDS1 gene in the recombinant strain, which demonstrated that the introduction of RDS1 overexpression promoted the expression level. Such signature expressions differentiated tolerance phenotypes of RDS 1 from the innate stress response of its parental strain. Overexpression of the RDS1 gene involving diversified functional categories is accountable for stress tolerance in yeast S. cerevisiae to survive and adapt the furfural during the lag phase. [ABSTRACT FROM AUTHOR]

With the outstanding performance of deep learning based single image super-resolution (SISR) methods, some widely used quality evaluation metrics such as peak signal to noise ratio (PSNR) and structure similarity index (SSIM) have become more and more difficult to meet the assessment requirements of SISR methods, especially in terms of their consistency with subjective visual perception. To deal with this super-resolution (SR) image quality assessment (IQA) issue, it calls for a specialized objective evaluation metric based on the visual characteristics of SR images and the human visual system (HVS). Notwithstanding, there is a lack of practical databases for analysis. In this paper, the SISR subjective evaluation tests are conducted to build an SISRSet database, which requires a large number of reconstructed images generated by various SR algorithms. The statistical analyses of SISRSet reveal that high-frequency texture details play an important role in the performance evaluation of SR algorithms. Inspired by the origination selectivity mechanism (OSM) and the internal generative mechanism (IGM) of the HVS, a visual content prediction model (VCPM) is proposed to measure different visible structural contents of SR images, especially the texture details. Finally, a novel SISR quality assessment metric is devised based on the VCPM similarity comparison between the references and SR images. The experimental results demonstrate that the proposed SISR-IQA metric does well in the performance evaluation of SISR methods and well correlates with those by the subjective evaluation. [ABSTRACT FROM AUTHOR]

Video deblurring is one of the most challenging vision tasks because of the complex spatial-temporal relationship and a number of uncertainty factors involved in video acquisition. As different moving objects in the video exhibit different motion trajectories, it is difficult to accurately capture their spatial-temporal relationships. In this paper, we proposed a memory-based temporal fusion network (TFN) to capture local spatial-temporal relationships across the input sequence for video deblurring. Our temporal fusion network consists of a memory network and a temporal fusion block. The memory network stores the extracted spatial-temporal relationships and guides the temporal fusion blocks to extract local spatial-temporal relationships more accurately. In addition, in order to enable our model to more effectively fuse the multiscale features of the previous frame, we propose a multiscale and multi-hop reconstruction memory network (RMN) based on the attention mechanism and memory network. We constructed a feature extractor that integrates residual dense blocks with three downsample layers to extract hierarchical spatial features. Finally, we feed these aggregated local features into a reconstruction module to restore sharp video frames. Experimental results on public datasets show that our temporal fusion network has achieved a significant performance improvement in terms of PSNR metrics (over 1dB) over existing state-of-the-art video deblurring methods. [ABSTRACT FROM AUTHOR]

No-reference/blind image quality assessment (BIQA) is designed to measure the image quality without any knowledge of the reference image. Most existing BIQA metrics employ natural scene statistics or learning based models, which have achieved great progress but there still remains much room for improvement. Cognitive researches indicate that natural images possess sparse structures which can be represented by a small number of descriptors. Considering the sparse property of images, we utilize the bag-of-words (BoW) model for image representation and propose a novel BIQA metric. By analyzing the effect of neighboring pixel number and quantization depth of local pattern on image content extraction, we adopt the local quantized pattern (LQP) to extract image feature descriptors. The codebook is constructed by clustering LQP based descriptors from a set of natural images instead of distorted images, which presents strong generalization ability. The BoW-based image feature representation is highly sensitive to various distortion types and levels. Experiments on three public databases verify the effectiveness of the proposed metric and indicate our method is highly consistent with human perception. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) has been attracting tremendous attention in recent years. Extensive CS image recovery algorithms have been proposed for more effective image reconstruction. However, very little work has been addressed for the quality assessment of CS recovered images, which may hinder further development of CS image recovery techniques. The CS recovered images are typically contaminated by multiple distortions, particularly at low sampling rates. Unfortunately, the existing quality metrics are ineffective for evaluating multiple distortions, so they are limited in predicting the quality of CS recovered images. Motivated by this, this paper presents a no-reference CS Recovered Image Quality (CSRIQ) metric based on the measurement of both local and global distortions in CS recovered images. The local features consist of a local phase coherence based edge sharpness measure and a gray level co-occurrence matrix based texture measure. The global features are extracted based on the natural scene statistics and can be divided into two types. One type is obtained by calculating image naturalness parameters. The other type is computed based on the statistics of singular value decomposition coefficients. Support vector regression is employed to do model training and the subsequent quality prediction. Experimental results conducted on a CS recovered image database demonstrate the advantages of the proposed method. As a application, the proposed metric is used for automatic parameter selection for CS image recovery algorithms. [ABSTRACT FROM AUTHOR]

Background and purpose: Neurodegenerative processes are widespread in the brains of type 2 diabetes mellitus (T2DM) patients; gaps remain to exist in the current knowledge of the associated gray matter (GM) microstructural alterations. Methods: A cross‐sectional study was conducted to investigate alterations in GM microarchitecture in T2DM patients by diffusion tensor imaging and neurite orientation dispersion and density imaging (NODDI). Seventy‐eight T2DM patients and seventy‐four age‐, sex‐, and education level‐matched healthy controls (HCs) without cognitive impairment were recruited. Cortical macrostructure and GM microstructure were assessed by surface‐based analysis and GM‐based spatial statistics (GBSS), respectively. Machine learning models were trained to evaluate the diagnostic values of cortical intracellular volume fraction (ICVF) for the classification of T2DM versus HCs. Results: There were no differences in cortical thickness or area between the groups. GBSS analysis revealed similar GM microstructural patterns of significantly decreased fractional anisotropy, increased mean diffusivity and radial diffusivity in T2DM patients involving the frontal and parietal lobes, and significantly lower ICVF values were observed in nearly all brain regions of T2DM patients. A support vector machine model with a linear kernel was trained to realize the T2DM versus HC classification and exhibited the highest performance among the trained models, achieving an accuracy of 74% and an area under the curve of 83%. Conclusions: NODDI may help to probe the widespread GM neuritic density loss in T2DM patients occurs before measurable macrostructural alterations. The cortical ICVF values may provide valuable diagnostic information regarding the early GM microstructural alterations in T2DM. [ABSTRACT FROM AUTHOR]

• A multi-task multi-objective evolutionary network is proposed. • The framework combines two tasks by effective high-frequency information sharing. • Multi-task learning is modeled as a deep MOEAD to obtain trade-off solutions. • The method provides diverse and sufficient samples for multi-task. Multi-task learning has commonly been used and performed well at joint visual perception tasks. Hyperspectral pansharpening (HP) and hyperspectral classification (HC) tasks extract high-frequency information to enhance edges and classify samples, offering potential for performance improvements in multi-task learning. However, differences between tasks can make it challenging to balance their performances. To address this challenge, this paper proposes a multi-task multi-objective evolutionary network (DMOEAD) for joint learning of HC and HP. A multi-task sufficiency-and-diversity sampling method is designed to unify the heterogeneity of sample construction between two types of tasks. Two types of task-specific networks are constructed to decompose high-frequency information. Further, a collaborative learning module is designed to dynamically learn complementary high-frequency information from another task in different layers. To be compatible with the optimization direction of two types of tasks, multi-task optimization is realized using a deep multi-objective evolutionary algorithm (DMEO). In the DMEO, the set of parameters of the DMOEAD is regarded as an individual. A deep mutation operator is designed and used for network optimization, which accelerates large-scale network parameter searching. The DMEO can coordinate the differences between multiple tasks and provide a set of Pareto network parameter solutions. Finally, the experimental results demonstrate that the proposed method can significantly enhance the performance of both pansharpening and classification tasks. [ABSTRACT FROM AUTHOR]

The Wulonggou Gold Field is one of the giant gold fields in the East Kunlun Orogenic Belt, northwestern China. Previous studies mainly focused on elementary mineral isotopic studies, fluid inclusions, and geological features in the Wulonggou Gold Field. In this study, we report some research on the precise age and the specific ore-forming process of the WGF: the hydrothermal monazite U–Pb ages; the way of gold precipitation; the composition, evolution, and source of ore-forming fluids of the Wulonggou Gold Field. Finally, we demonstrate a link between two-stage hydrothermal events and sequential episodes of crust-derived magmas, with implications for gold metallogeny in the East Kunlun Orogenic Belt. There are four hydrothermal stages that are recognized: a quartz–pyrite stage (stage 1), a quartz–pyrite–arsenopyrite–chalcopyrite stage (stage 2), a quartz–galena–sphalerite–pyrite stage (stage 3) and a quartz–stibnite–carbonate stage (stage 4). The monazite U–Pb ages of the Huanglonggou and Hongqigou deposits in the Wulonggou Gold Field were 422.2 ± 2.4 Ma and 236.7 ± 3.7 Ma, respectively, which support the opinion of two epochs of mineralization. Stages 1 and 2 are the main gold mineralization stages, wherein Au and As have a close genetic relationship. The Hongqigou and Huanglonggou deposits seem to have been formed in different metallogenic events due to the contrast on the trace element compositions in pyrite. The sources of the ore-forming materials and fluids of the Hongqigou and Huanglonggou deposits show apparent characteristics of orogenic gold deposit, and the magmatic events during Paleozoic and Mesozoic have an important contribution to the formation of the gold deposits. The gold deposits in the Wulonggou Gold Field can be interpreted as an orogenic gold system related to two-epoch tectonic–magmatic events. [ABSTRACT FROM AUTHOR]

Human neuroimaging studies have demonstrated that exercise influences the cortical structural plasticity as indexed by gray or white matter volume. It remains elusive, however, whether exercise affects cortical changes at the finer‐grained myelination structure level. To answer this question, we scanned 28 elite golf players in comparison with control participants, using a novel neuroimaging technique—quantitative magnetic resonance imaging (qMRI). The data showed myeloarchitectonic plasticity in the left temporal pole of the golf players: the microstructure of this brain region of the golf players was better proliferated than that of control participants. In addition, this myeloarchitectonic plasticity was positively related to golfing proficiency. Our study has manifested that myeloarchitectonic plasticity could be induced by exercise, and thus, shed light on the potential benefits of exercise on brain health and cognitive enhancement. [ABSTRACT FROM AUTHOR]

Correlation Filters (CFs) have shown outstanding performance in tracking, but are subject to unwanted boundary effects. Spatial regularization (SR) is widely used as an efficient method to alleviate the boundary effects. However, spatial regularization is almost handcrafted and fixed during tracking process, which cannot handle the diversity of objects and the complexity of motion. Furthermore, the rich spatio-temporal correlations among multiple targets of interest cannot be fully exploited. Herein, we propose a spatio-temporal Gaussian scale mixture model (ST-GSM) for correlation-filter-based visual tracking. In our Gaussian scale mixture (GSM) model, each correlation filter coefficient is decomposed into the product of a positive scalar multiplier with sparsity and a Gaussian random variable. The reliable components of the Gaussian random variable can be adaptively selected based on the positive multipliers, aiming at alleviating the notorious boundary effects. To exploit the temporal consistency between adjacent frames, nonzero-means GSM models are developed to characterize the temporal correlations. Specifically, the filter coefficient obtained in the previous frame is used as the mean prior for the current frame. The spatial correlations among filter coefficients have been considered in the structured GSM model, thereby further improving the tracking performance. Experimental results show that the proposed model can significantly improve the performance of CF-based trackers. [ABSTRACT FROM AUTHOR]

Images are subject to blocking artifacts when they are compressed using the JPEG standard. Knowing the extent of blocking artifacts is thus necessary for such applications as automatic quality monitoring and restoration. The current blocking artifacts measures are based on a strong prior that the block boundaries are known in advance, which often does not hold in real-world applications. Therefore, their performances degrade significantly when block boundaries are misaligned. To address the problem, this paper presents a robust no-reference blocking artifacts evaluation metric for JPEG images based on grid strength and regularity (GridSAR). The underlying idea is to extract block grids from a JPEG image and quantify the grid image to evaluate the strength of blocking artifacts. To this end, a grid map of blocking artifacts is first extracted from the image in the spatial domain. Then the blocking artifacts of the image are evaluated by quantifying the strength and regularity of the grid image. Furthermore, in order to account for the varying sensitivities of human eyes to the blocking artifacts in smooth and textured areas, a masking function is also proposed. Experimental results on seven popular image quality databases demonstrate that GridSAR achieves the state-of-the-art performance, and is robust to block misalignments. [ABSTRACT FROM AUTHOR]

The Baishitouwa deposit is a medium-scale quartz–wolframite vein-type deposit in the southern Great Xing'an Range tungsten (W) belt. The W mineralization occurs mainly as veins and dissemination within the mica schist of the Mesoproterozoic Baiyunebo Group. The formation of the deposit can be divided into four stages. The wolframite yielded a lower intercept 206Pb/238U age of 221.0 ± 3.4 Ma (1σ, MSWD = 2.0), which records a late Triassic W mineralization event in the Baishitouwa deposit. In combination with previous geochronological data, we suggest that NE China may have an enormous potential for Triassic W mineralization and more attention should be given to the Triassic ore prospecting in the region. This work highlights that the chemical composition of wolframite is controlled by both the crystallochemical parameters and the composition of the primary ore-forming fluid. Trace-element compositions suggest that wolframite (I) was controlled by the substitution mechanism of 4A(Fe, Mn)2+ + 8BW6+ + B□ ↔ 3AM3+ + AN4+ + 7B(Nb, Ta)5+ + 2BN4+, whereas wolframite (II) was controlled by the substitution mechanism of A(Fe, Mn)2+ + A□ + 2BW6+ ↔ 2AM3+ + 2BN4+. Wolframite (I) contains higher concentrations of Nb, Ta, Sc, and heavy rare earth elements (HREEs), and lower Mn/(Mn + Fe) ratios than wolframite (II). Both wolframite (I) and (II) have similar trace elements and left-dipped REEN patterns, and analogical Nb/Ta ratios. They have similar Y/Ho ratios to Mesozoic highly fractionated W-mineralized granitoids in NE China. These data indicate that the W mineralization at Baishitouwa is genetically related to an underlying highly fractionated granite, and the compositional variation of fluids is likely driven by crystallization of wolframite during the processes of fluid evolution. A change of the ore-forming fluids from an oxidized to a relatively reduced state during the evolution occurred from stage 1 to 2. [ABSTRACT FROM AUTHOR]

Correlation filters (CF), a popular tool for visual tracking, suffer from unwanted boundary effects due to the periodic assumption needed for FFT implementation. To address this issue, spatially regularized discriminative correlation filters (SRDCF) have been proposed by introducing a weighting matrix to the regularization term. However, the existing design of spatial weighting matrix is often heuristic and non-adaptive. Inspired by recent advances in joint discrimination and reliability learning for correlation tracking, we propose a principled Bayesian correlation filter learning method using Gaussian scale mixture (GSM) model. The key idea is to decompose each CF coefficient into the product of a positive scalar multiplier and a Gaussian random variable. Treating positive multipliers as weighting coefficients, GSM-based modeling of CFs leads to a spatially adaptive regularization strategy with improved capability of handling various appearance-related uncertainty factors (e.g., scale variation, out-of-plane rotation, and motion blur). Moreover, by imposing a sparse prior over the multipliers, we can jointly learn multipliers and CFs under a unified Bayesian estimation framework. Structured GSM model allows us to better exploit the spatial correlations among CFs and further improve the tracking performance. Experimental results on OTB-2013, OTB-2015, Temple Color-128, VOT-2016, and VOT-2017 show that our tracking method performs favorably when compared with current state-of-the-art methods. [ABSTRACT FROM AUTHOR]