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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 Jiaodong gold province, situated in the southeastern margin of the North China Craton, is globally renowned for its substantial gold reserves exceeding 5,000 tonnes. The Zhaoxian gold deposit is part of the significant Jiaojia gold belt within the Jiaodong gold province. Fieldwork has identified four distinct stages of ore formation in this study: an early barren quartz vein stage (Stage 1) containing fine-grained pyrite; a gold-bearing stage (Stage 2) consisting of quartz, pyrite, and native gold; a polymetallic sulfide-rich stage (Stage 3) comprising quartz, polymetallic sulfides, and native gold; and a late-stage (Stage 4) primarily composed of quartz and calcite with minimal pyrite content. We conducted fluid inclusion analysis using microthermometry and Raman spectroscopy techniques to examine the fluid characteristics. In-situ analysis of trace elements in pyrite was performed to investigate the fluid composition and evolution. Additionally, we determined the sulfur isotope composition in pyrite to analyze the source of sulfur. Our findings indicate that the ore-forming fluid in the Zhaoxian gold deposit belongs to a medium-to-low-salinity H2O-NaCl-CO2-CH4 system. Fluctuations in Au and As concentrations observed along with oscillating zones and sulfide inclusions during Stage 2 suggest potential fluid boiling processes occurring during mineralization. High concentrations of Ag, Cu, Zn, Cd, In, Pb, and Bi without oscillating zones during Stage 3 imply precipitation of polymetallic sulfides under stable fluid conditions. The δ3^ values observed in the Zhaoxian gold deposit are slightly higher than those found in granitoids from other areas within Jiaodong but similar to those seen in other deposits within the Jiaojia gold belt region. In conclusion, magmatic-hydrothermal ore-forming fluids were involved along with significant fluid-rock interaction during metallogenic processes of the Zhaoxian gold deposit. [ABSTRACT FROM AUTHOR]

An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM. [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]

Purpose: The white matter (WM) of the brain of type 2 diabetes mellitus (T2DM) patients is susceptible to neurodegenerative processes, but the specific types and positions of microstructural lesions along the fiber tracts remain unclear. Methods: In this study 61 T2DM patients and 61 healthy controls were recruited and underwent diffusion spectrum imaging (DSI). The results were reconstructed with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). WM microstructural abnormalities were identified using tract-based spatial statistics (TBSS). Pointwise WM tract differences were detected through automatic fiber quantification (AFQ). The relationships between WM tract abnormalities and clinical characteristics were explored with partial correlation analysis. Results: TBSS revealed widespread WM lesions in T2DM patients with decreased fractional anisotropy and axial diffusivity and an increased orientation dispersion index (ODI). The AFQ results showed microstructural abnormalities in T2DM patients in specific portions of the right superior longitudinal fasciculus (SLF), right arcuate fasciculus (ARC), left anterior thalamic radiation (ATR), and forceps major (FMA). In the right ARC of T2DM patients, an aberrant ODI was positively correlated with fasting insulin and insulin resistance, and an abnormal intracellular volume fraction was negatively correlated with fasting blood glucose. Additionally, negative associations were found between blood pressure and microstructural abnormalities in the right ARC, left ATR, and FMA in T2DM patients. Conclusion: Using AFQ, together with DTI and NODDI, various kinds of microstructural alterations in the right SLF, right ARC, left ATR, and FMA can be accurately identified and may be associated with insulin and glucose status and blood pressure in T2DM patients. [ABSTRACT FROM AUTHOR]

Lignocellulosic biomass is still considered a feasible source of bioethanol production. Saccharomyces cerevisiae can adapt to detoxify lignocellulose-derived inhibitors, including furfural. Tolerance of strain performance has been measured by the extent of the lag phase for cell proliferation following the furfural inhibitor challenge. The purpose of this work was to obtain a tolerant yeast strain against furfural through overexpression of YPR015C using the in vivo homologous recombination method. The physiological observation of the overexpressing yeast strain showed that it was more resistant to furfural than its parental strain. Fluorescence microscopy revealed improved enzyme reductase activity and accumulation of oxygen reactive species due to the harmful effects of furfural inhibitor in contrast to its parental strain. Comparative transcriptomic analysis revealed 79 genes potentially involved in amino acid biosynthesis, oxidative stress, cell wall response, heat shock protein, and mitochondrial-associated protein for the YPR015C overexpressing strain associated with stress responses to furfural at the late stage of lag phase growth. Both up- and down-regulated genes involved in diversified functional categories were accountable for tolerance in yeast to survive and adapt to the furfural stress in a time course study during the lag phase growth. This study enlarges our perceptions comprehensively about the physiological and molecular mechanisms implicated in the YPR015C overexpressing strain's tolerance under furfural stress. Construction illustration of the recombinant plasmid. a) pUG6-TEF1p-YPR015C, b) integration diagram of the recombinant plasmid pUG6-TEF1p-YPR into the chromosomal DNA of Saccharomyces cerevisiae. [ABSTRACT FROM AUTHOR]

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]

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]

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]

In epidemiological studies, type 2 diabetes mellitus (T2DM) has been associated with cognitive impairment and dementia, but studies about functional network connectivity in T2DM without cognitive impairment are limited. This study aimed to explore network connectivity alterations that may help enhance our understanding of damage-associated processes in T2DM. MRI data were analyzed from 82 patients with T2DM and 66 normal controls. Clinical, biochemical, and neuropsychological assessments were conducted in parallel with resting-state functional magnetic resonance imaging, and the cognitive status of the patients was assessed using the Montreal Cognitive Assessment-B (MoCA-B) score. Independent component analysis revealed a positive correlation between the salience network and the visual network and a negative connection between the left executive control network and the default mode network in patients with T2DM. The differences in dynamic brain network connectivity were observed in the precuneus, visual, and executive control networks. Internal network connectivity was primarily affected in the thalamus, inferior parietal lobe, and left precuneus. Hemoglobin A1c level, body mass index, MoCA-B score, and grooved pegboard (R) assessments indicated significant differences between the two groups (p < 0.05). Our findings show that key changes in functional connectivity in diabetes occur in the precuneus and executive control networks that evolve before patients develop cognitive deficits. In addition, the current study provides useful information about the role of the thalamus, inferior parietal lobe, and precuneus, which might be potential biomarkers for predicting the clinical progression, assessing the cognitive function, and further understanding the neuropathology of T2DM. [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]

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]

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]

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]

Subpixel target detection in hyperspectral imagery is challenging since subpixel targets are smaller in size than the resolution of a single pixel and accurate pixel-level labels on subpixel targets are often unavailable. In particular, this article addresses the problem of learning a prime prototype target signature from imprecisely labeled highly mixed hyperspectral data. Two algorithms, multiple-instance subpixel adaptive cosine estimator (MI-SPACE) and multiple-instance subpixel spectral matched filter (MI-SPSMF), based on multiple-instance learning framework are presented. The proposed methods aim to learn a discriminative prime target signature by maximizing the posterior detection statistics of subpixel hyperspectral targets for the correspondingly proposed subpixel adaptive cosine estimator (SPACE) and subpixel spectral matched filter (SPSMF) detectors, which are also developed in this article. Experimental results demonstrate the effectiveness of the proposed methods on both simulated and real-field hyperspectral subpixel target detection tasks. [ABSTRACT FROM AUTHOR]

Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an “average” user. However, such generic aesthetics models neglect the fact that users’ aesthetic preferences vary significantly depending on their unique preferences. Therefore, it is essential to tackle the issue for personalized IAA (PIAA). Since PIAA is a typical small sample learning (SSL) problem, existing PIAA models are usually built by fine-tuning the well-established generic IAA (GIAA) models, which are regarded as prior knowledge. Nevertheless, this kind of prior knowledge based on “average aesthetics” fails to incarnate the aesthetic diversity of different people. In order to learn the shared prior knowledge when different people judge aesthetics, that is, learn how people judge image aesthetics, we propose a PIAA method based on meta-learning with bilevel gradient optimization (BLG-PIAA), which is trained using individual aesthetic data directly and generalizes to unknown users quickly. The proposed approach consists of two phases: 1) meta-training and 2) meta-testing. In meta-training, the aesthetics assessment of each user is regarded as a task, and the training set of each task is divided into two sets: 1) support set and 2) query set. Unlike traditional methods that train a GIAA model based on average aesthetics, we train an aesthetic meta-learner model by bilevel gradient updating from the support set to the query set using many users’ PIAA tasks. In meta-testing, the aesthetic meta-learner model is fine-tuned using a small amount of aesthetic data of a target user to obtain the PIAA model. The experimental results show that the proposed method outperforms the state-of-the-art PIAA metrics, and the learned prior model of BLG-PIAA can be quickly adapted to unseen PIAA tasks. [ABSTRACT FROM AUTHOR]

Blind image quality assessment (BIQA) has witnessed a flourishing progress due to the rapid advances in deep learning technique. The vast majority of prior BIQA methods try to leverage models pre-trained on ImageNet to mitigate the data shortage problem. These well-trained models, however, can be sub-optimal when applied to BIQA task that varies considerably from the image classification domain. To address this issue, we make the first attempt to leverage the plentiful unlabeled data to conduct self-supervised pre-training for BIQA task. Based on the distorted images generated from the high-quality samples using the designed distortion augmentation strategy, the proposed pre-training is implemented by a feature representation prediction task. Specifically, patch-wise feature representations corresponding to a certain grid are integrated to make prediction for the representation of the patch below it. The prediction quality is then evaluated using a contrastive loss to capture quality-aware information for BIQA task. Experimental results conducted on KADID-10 k and KonIQ-10 k databases demonstrate that the learned pre-trained model can significantly benefit the existing learning based IQA models. [ABSTRACT FROM AUTHOR]

Unlike the maturity of image denoising research, video denoising has remained a challenging problem. A fundamental issue at the core of the video denoising (VD) problem is how to efficiently remove noise by exploiting temporal redundancy in video frames in a principled manner. Based on the maximum a posterior (MAP) estimation framework and recent advances in deep learning, we present a novel deep MAP-based video denoising method named MAP-VDNet with adaptive temporal fusion and deep image prior. The proposed MAP-based VD algorithm allows computationally efficient untangling of motion estimation (frame alignment) and image restoration (denoising). To address the misalignment issue, we also present a robust multi-frame fusion strategy for predicting spatially varying fusion weights by a neural network. To facilitate end-to-end optimization, we unfold the proposed iterative MAP-based VD algorithm into a deep convolutional network named MAP-VDNet. Extensive experimental results on three popular video datasets have shown that the proposed MAP-VDNet significantly outperforms current state-of-the-art VD techniques such as ViDeNN and FastDVDnet. The code is available at https://see.xidian.edu.cn/faculty/wsdong/Projects/MAP-VDNet.htm. [ABSTRACT FROM AUTHOR]

Background: Structural and functional brain alterations that underlie cognitive decline have been observed in elderly adults with type 2 diabetes mellitus (T2DM); however, whether these alterations can be observed in patients with early-onset T2DM remains unclear. Therefore, we aimed to describe the abnormalities in brain volume and functional patterns in patients with early-onset T2DM in the present study. Methods: We enrolled 20 patients with early-onset T2DM and 20 healthy controls (HCs). Changes in brain volume were assessed using voxel-based morphology (VBM), while changes in brain function were assessed using degree centrality (DC) and functional connectivity (FC). Results: Compared to HCs, patients with early-onset T2DM exhibited gray matter reductions in the left orbital superior, middle, and inferior frontal gyri as well as the right superior frontal gyrus. The gray matter reductions in the right superior frontal gyrus were negatively associated with the urine albumin to creatinine ratio. Furthermore, increased DC values were observed in the left superior temporal gyrus, left Heschl gyrus, and left hippocampus in patients with early-onset T2DM. An FC analysis of these regions revealed elevated connectivity in the right precuneus, left inferior parietal gyrus, left Heschl gyrus, bilateral post-central gyrus, bilateral insula, bilateral superior temporal gyrus, and bilateral medial and paracingulate gyrus. Furthermore, the FC of the hubs to the superior temporal gyrus, insula, and Heschl gyrus was increased and positively correlated with trail making test-B. Conclusion: Decreased local gray matter volume and increased DC and FC may represent the neurobiological mechanism underlying cognitive dysfunction in patients with early-onset T2DM. [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 trade-off between spatial and spectral resolution is one of the fundamental issues in hyperspectral images (HSI). Given the challenges of directly acquiring high-resolution hyperspectral images (HR-HSI), a compromised solution is to fuse a pair of images: one has high-resolution (HR) in the spatial domain but low-resolution (LR) in spectral-domain and the other vice versa. Model-based image fusion methods including pan-sharpening aim at reconstructing HR-HSI by solving manually designed objective functions. However, such hand-crafted prior often leads to inevitable performance degradation due to a lack of end-to-end optimization. Although several deep learning-based methods have been proposed for hyperspectral pan-sharpening, HR-HSI related domain knowledge has not been fully exploited, leaving room for further improvement. In this paper, we propose an iterative Hyperspectral Image Super-Resolution (HSISR) algorithm based on a deep HSI denoiser to leverage both domain knowledge likelihood and deep image prior. By taking the observation matrix of HSI into account during the end-to-end optimization, we show how to unfold an iterative HSISR algorithm into a novel model-guided deep convolutional network (MoG-DCN). The representation of the observation matrix by subnetworks also allows the unfolded deep HSISR network to work with different HSI situations, which enhances the flexibility of MoG-DCN. Extensive experimental results are reported to demonstrate that the proposed MoG-DCN outperforms several leading HSISR methods in terms of both implementation cost and visual quality. The code is available at https://see.xidian.edu.cn/faculty/wsdong/Projects/MoG-DCN.htm. [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]

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]

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]

With the rapid growth of digital video through the Internet, a reliable objective video-quality assessment (VQA) algorithm is in great demand for video management. Motion information plays a dominant role for video perception, and the human visual system (HVS) is able to track moving objects effectively with eye movement. Moreover, the middle temporal area of the brain is selective for moving objects with particular velocities. In other words, visual contents that are along the motion trajectories will automatically attract our attention for dedicated processing. Inspired by the motion-related process in the HVS, we suggest analyzing the degradation along attended motion trajectories for VQA. The characteristic of motion velocity along each trajectory is analyzed for temporal quality measurement. Meanwhile, visual information along each trajectory is extracted for joint spatial-temporal quality measurement. Finally, considering the spatial-quality degradation from each frame, a novel full-reference assessor along salient trajectories (FAST) for VQA (which combines the spatial, temporal, and joint spatial-temporal quality degradations) is introduced. Experimental results on five publicly available VQA databases demonstrate that the proposed FAST VQA model performs consistently with the subjective perception. The source code of the proposed method is available at http://web.xidian.edu.cn/wjj/paper.html. [ABSTRACT FROM AUTHOR]

The concept of just noticeable difference (JND), which accounts for the visibility threshold (visual redundancy) of the human visual system, is useful in perception-oriented signal processing systems. In this work, we present a comprehensive review of JND estimation technology. First, the visual mechanism and its corresponding computational modules are illustrated. These include luminance adaptation, contrast masking, pattern masking, and the contrast sensitivity function. Next, the existing pixel domain and subband domain JND models are presented and analyzed. Finally, the challenges associated with JND estimation are discussed. [ABSTRACT FROM AUTHOR]

In the past decade, extensive image quality metrics have been proposed. The majority of them are tailored for the images that contain a specific type of distortion. However, in practice, the images are usually degraded by different types of distortions simultaneously. This poses great challenges to the existing quality metrics. Motivated by this, this paper proposes a no-reference quality index for the multiply distorted images using the biorder structure degradation and the nonlocal statistics. The design philosophy is inspired by the fact that the human visual system (HVS) is highly sensitive to the degradations of both the spatial contrast and the spatial distribution, which are prone to be changed by the joint effects of the multiple distortions. Specifically, the multiresolution representation of the image is first built by downsampling to simulate the hierarchical property of the HVS. Then, the structure degradation is calculated to measure the spatial contrast. Considering the fact that the human visual cortex has the separate mechanisms to perceive the first- and second-order structures, dubbed biorder structures, the degradations of biorder structures are calculated to account for the spatial contrast, producing the first group of the quality-aware features. Furthermore, the nonlocal self-similarity statistics is calculated to measure the spatial distribution, producing the second group of features. Finally, all the features are fed into the random forest regression model to learn the quality model for the multiply distorted images. Extensive experimental results conducted on the three public databases demonstrate the superiority of the proposed metric to the state-of-the-art metrics. Moreover, the proposed metric is also advantageous over the existing metrics in terms of the generalization ability. [ABSTRACT FROM AUTHOR]

Background: Alzheimer disease (AD) is the most common type of dementia with cognitive decline as one of the core symptoms in older adults. Numerous studies have suggested the value of psychosocial interventions to improve cognition in this population, but which one should be preferred are still matters of controversy. Consequently, we aim to compare and rank different psychosocial interventions in the management of mild to moderate AD with cognitive symptoms.Methods: We did a network meta-analysis to identify both direct and indirect evidence in relevant studies. We searched MEDLINE, EMBASE, PsycINFO through the OVID database, CENTRAL through the Cochrane Library for clinical randomized controlled trials investigating psychosocial interventions of cognitive symptoms in patients with Alzheimer disease, published up to August 31, 2017. We included trials of home-based exercise(HE), group exercise(GE), walking program(WP), reminiscence therapy(RT), art therapy(AT) or the combination of psychosocial interventions and acetylcholinesterase inhibitor (ChEIs). We extracted the relevant information from these trials with a predefined data extraction sheet and assessed the risk of bias with the Cochrane risk of bias tool. The outcomes investigated were Mini-Mental State Examination (MMSE) and compliance. We did a pair-wise meta-analysis using the fixed-effects model and then did a random-effects network meta-analysis within a Bayesian framework.Results: We deemed 10 trials eligible, including 682 patients and 11 treatments. The quality of included study was rated as low in most comparison with Cochrane tools. Treatment effects from the network meta-analysis showed WP was better than control (SMD 4.89, 95% CI -0.07 to 10.00) while cognitive training and acetylcholinesterase inhibitor (CT + ChEIs) was significantly better than the other treatments, when compared with simple ChEIs treatment, assessed by MMSE. In terms of compliance, the pair-wise meta-analysis indicated that WP and HE are better than GE and AT, while CT + ChEIs, CST + ChEIs are better than other combined interventions.Conclusion: Our study confirmed the effectiveness of psychosocial interventions for improving cognition or slowing down the progression of cognitive impairment in AD patients and recommended several interventions for clinical practice. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global feature representation. The proposed method is designed inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to luminance change and texture information for image perception. In the proposed method, we first calculate the luminance map through the local normalization, which is further used to extract the statistical luminance features in global scope. Second, inspired by existing studies from neuroscience that high-order derivatives can capture image texture, we adopt four filters with different directions to compute gradient maps from the luminance map. These gradient maps are then used to extract the second-order derivatives by local binary pattern. We further extract the texture feature by the histogram of high-order derivatives in global scope. Finally, support vector regression is applied to train the mapping function from quality-aware features to subjective ratings. Experimental results on the public large-scale SCI database show that the proposed NRLT can achieve better performance in predicting the visual quality of SCIs than relevant existing methods, even including some full reference visual quality assessment methods. [ABSTRACT FROM PUBLISHER]

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]

High-quality depth recovery from RGB-D data has received increasingly more attention in recent years due to their wide applications from depth-based image rendering to three-dimensional imaging and video. Sharp contrast between high-quality color images and low-quality depth maps presents severe challenges to the development of color-guided depth recovery techniques. Previous works have emphasized either locally varying characteristics of color-depth dependence or nonlocal similarities around the discontinuities of the scene geometry. Therefore, it is desirable to exploit both local and nonlocal structural constraints for optimizing the performance of color-guided depth recovery. In this work, we propose a unified variational approach via joint local and nonlocal regularization. The local regularization term consists of two complementary parts—one characterizing the color-depth dependence in the gradient domain and the other in the spatial domain; nonlocal regularization involves a low-rank constraint suitable for large-scale depth discontinuities. Extensive experimental results are reported to show that our approach outperforms several existing state-of-the-art depth recovery methods on both synthetic and real-world data sets. [ABSTRACT FROM PUBLISHER]

Recent advances in sparse representation show that overcomplete dictionaries learned from natural images can capture high-level features for image analysis. Since atoms in the dictionaries are typically edge patterns and image blur is characterized by the spread of edges, an overcomplete dictionary can be used to measure the extent of blur. Motivated by this, this paper presents a no-reference sparse representation-based image sharpness index. An overcomplete dictionary is first learned using natural images. The blurred image is then represented using the dictionary in a block manner, and block energy is computed using the sparse coefficients. The sharpness score is defined as the variance-normalized energy over a set of selected high-variance blocks, which is achieved by normalizing the total block energy using the sum of block variances. The proposed method is not sensitive to training images, so a universal dictionary can be used to evaluate the sharpness of images. Experiments on six public image quality databases demonstrate the advantages of the proposed method. [ABSTRACT FROM PUBLISHER]

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]

In this paper, we introduce a novel just noticeable difference (JND) estimation model based on the unified brain theory, namely the free-energy principle. The existing pixel-based JND models mainly consider the orderly factors and always underestimate the JND threshold of the disorderly region. Recent research indicates that the human visual system (HVS) actively predicts the orderly information and avoids the residual disorderly uncertainty for image perception and understanding. Thus, we suggest that there exists disorderly concealment effect which results in high JND threshold of the disorderly region. Beginning with the Bayesian inference, we deduce an autoregressive model to imitate the active prediction of the HVS. Then, we estimate the disorderly concealment effect for the novel JND model. Experimental results confirm that the proposed JND model outperforms the relevant existing ones. Furthermore, we apply the proposed JND model in image compression, and around 15% of bit rate can be reduced without jeopardizing the perceptual quality. [ABSTRACT FROM PUBLISHER]

Reduced-reference (RR) image quality assessment (IQA) aims to use less data about the reference image and achieve higher evaluation accuracy. Recent research on brain theory suggests that the human visual system (HVS) actively predicts the primary visual information and tries to avoid the residual uncertainty for image perception and understanding. Therefore, the perceptual quality relies to the information fidelities of the primary visual information and the residual uncertainty. In this paper, we propose a novel RR IQA index based on visual information fidelity. We advocate that distortions on the primary visual information mainly disturb image understanding, and distortions on the residual uncertainty mainly change the comfort of perception. We separately compute the quantities of the primary visual information and the residual uncertainty of an image. Then the fidelities of the two types of information are separately evaluated for quality assessment. Experimental results demonstrate that the proposed index uses few data (30 bits) and achieves high consistency with human perception. [ABSTRACT FROM PUBLISHER]

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]

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]