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Homography estimation serves an important role in many computer vision tasks. Depending heavily on hand‐craft feature quality, traditional methods degenerate sharply in scenes with low texture. Existing deep homography methods can handle the low‐texture problem but are not robust for scenes with low overlap rates and/or illumination changes. This paper proposes a novel unsupervised homography estimation method that can simultaneously handle such low overlap and illumination change. Specifically, a powerful module, named global transformer contextual encoder (GTCE) is first designed, together with a correlation encoder to effectively aggregate global contextual information and reduce matching ambiguity between feature maps. Moreover, a hybrid photo‐perceptual loss for unsupervised homography estimation is proposed. The proposed loss function considers alignment information on both pixel level and perceptual level thus helping this network to be more adaptive to various scenes, including normal cases and illumination change cases. The results of extensive experiments on synthetic and real‐world datasets demonstrate the superiority of this proposed method over current state‐of‐the‐art solutions especially on challenging scenes with low overlap rates, repetitive patterns and illumination changes. [ABSTRACT FROM AUTHOR]

As Jennifer Johnston's novels continue to be the focal point of critical attention, her plays have been largely neglected. This article attempts to read Johnston's two less noticed plays The Desert Lullaby and Moonlight and Music from the perspective of trauma studies and to explore how trauma and silence are psychologically, culturally, and socially imposed on women. In both plays, Johnston takes on women's domestic traumas – death and incest in the former and sexual abuse and silence in the latter – that have been tabooed for so long in Ireland. Both plays expose how psychological pathology, gender, and culture undermine women's lives and oppress their voices. By uncovering the hidden traumas and their devastating effects on women's lives, Johnston breaks the silence imposed by the perpetrators and society. Rereading Johnston's plays thirty years later is to acknowledge her pioneering work of staging women's domestic trauma through dramatic experiments of narratives, monologues, and juxtaposition of the past and the present. [ABSTRACT FROM AUTHOR]

This essay gives a traumatic reading of Maurya's passivity. Traumatized by her sons' deaths, Maurya develops PTSD symptoms including hyperarousal, intrusion, and constriction, and through keening she moves towards a recovery. Synge's deft use of symbols, repetition, and imagery to stage trauma makes Riders a precursor to later trauma plays. [ABSTRACT FROM AUTHOR]

RGBT tracking combines visible and thermal infrared images to achieve tracking and faces challenges due to motion blur caused by camera and target movement. In this study, we observe that the tracking in motion blur is significantly affected by both frequency and spatial aspects. And blurred targets exhibit sharp texture details that are represented as high-frequency information. But existing trackers capture low-frequency components while ignoring high-frequency information. To enhance the representation of sharp information in blurred scenes, we introduce multi-frequency and multi-spatial information in network, called FSBNet. First, we construct a modality-specific unsymmetrical architecture and integrate an adaptive soft threshold mechanism into a DCT-based multi-frequency channel attention adapter (DFDA). DFDA adaptively integrates rich multi-frequency information. Second, we propose a masked frequency-based translation adapter (MFTA) to refine drifting failure boxes caused by camera motion. Moreover, we find that small targets get more affected by motion blur compared to larger targets, and we mitigate this issue by designing a cross-scale mutual conversion adapter (CFCA) between the frequency and spatial domains. Extensive experiments on GTOT, RGBT234 and LasHeR benchmarks demonstrate the promising performance of our method in the presence of motion blur. [ABSTRACT FROM AUTHOR]

Parallax disposition is a challenging task in image stitching. Thus far, the most effective method for parallax processing is the seam-driven method. However, its ability to handle complex textures is poor. To apply the seam-driven method to more scenes, we have incorporated the idea of spatially varying warps. Before finding the optimal seam, we try to register the overlapping area in as projective a manner as possible. Meanwhile, we set some constraints for the registered image to preserve the original appearance as much as possible. Among the constraints, we introduce local features to further improve the stitched image. After registration, we use the maximum flow method to obtain the optimal seam and introduce a series of seam evaluation systems. On one hand, these systems evaluate whether the resulting seam is misaligned, and on the other hand, they provide feedback to the previous registration, increasing the weights of the feature points near the dislocation point to fine-tune the seam. Comparisons with existing image stitching algorithms demonstrate the effectiveness and universality of our approach. [ABSTRACT FROM AUTHOR]

Inspired by enormous success of fully convolutional network (FCN) in semantic segmentation, as well as the similarity between semantic segmentation and pixel-by-pixel polarimetric synthetic aperture radar (PolSAR) image classification, exploring how to effectively combine the unique polarimetric properties with FCN is a promising attempt at PolSAR image classification. Moreover, recent research shows that sparse and low-rank representations can convey valuable information for classification purposes. Therefore, this paper presents an effective PolSAR image classification scheme, which integrates deep spatial patterns learned automatically by FCN with sparse and low-rank subspace features: (1) a shallow subspace learning based on sparse and low-rank graph embedding is firstly introduced to capture the local and global structures of high-dimensional polarimetric data; (2) a pre-trained deep FCN-8s model is transferred to extract the nonlinear deep multi-scale spatial information of PolSAR image; and (3) the shallow sparse and low-rank subspace features are integrated to boost the discrimination of deep spatial features. Then, the integrated hierarchical subspace features are used for subsequent classification combined with a discriminative model. Extensive experiments on three pieces of real PolSAR data indicate that the proposed method can achieve competitive performance, particularly in the case where the available training samples are limited. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative Adaptive Component Selection-Based Discriminative Model (ACSDM) for object detection in high-resolution synthetic aperture radar (SAR) imagery. In order to explore the structural relationships between the target and the components, a multi-scale detector consisting of a root filter and several part filters is established, using Histogram of Oriented Gradient (HOG) features to describe the object from different resolutions. To make the detected components of practical significance, the size and anchor position of each component are determined through statistical methods. When training the root model and the corresponding part models, manual annotation is adopted to label the target in the training set. Besides, a penalty factor is introduced to compensate information loss in preprocessing. In the detection stage, the Small Area-Based Non-Maximum Suppression (SANMS) method is utilised for filtering out duplicate results. In the experiments, the aeroplanes in TerraSAR-X SAR images are detected by the ACSDM algorithm and different comparative methods. The results indicate that the proposed method has a lower false alarm rate and can detect the components accurately. [ABSTRACT FROM AUTHOR]

The article discusses play "O Ananias, Azarias and Miseal" by Jennifer Johnston and book "A Belfast Woman" by Mary Beckett. Topics discussed include Irish women authors and troubles and trauma in women's life in the play and the book. Other topics such as feminism and characters in the play and the book are also discussed.

Aiming at the problems of uneven students' abilities and the absence of specificity in MOOC teaching process, this paper presented a novel topic interaction model based on the local community detection. It modeled the evaluation process of students' ability as hierarchical structure of a topic model. It introduced the local community detection algorithm to reasonably classify various abilities. Meanwhile, based on the original course experiment platform proposed before, it established an improved MOOC platform to widely trace the behavior information and provided with highly interaction. Experimental results on the teaching practice of digital signal processing course show that topic interaction model achieves higher accuracy rates compared with traditional assessment methods. Supplemented with specific communication and guidance, students' abilities can be enhanced effectively to some degree. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multiple feature based hierarchal component model for aeroplane detection on remote sensing image. In order to take the use of multiple features together with Part-based Model approach, we propose the new algorithm: first, in order to give a more clear description of the target, MKL learning method is used to combine the multiple feature extracted from the target through a liner combination procedure, then, because the object itself has structure features, we build a latent hierarchical structure model(LHSM), at last, we combine the MKL and LHS together to form the algorithm proposed in the beginning. We also test the effects of the new algorithm by collecting the remote sensing images from ten international airports; the result shows that the new approach is worthwhile. [ABSTRACT FROM AUTHOR]

This paper presents a frame for classifying polarimetric synthetic aperture radar (PolSAR) data. The frame is based on the combination of wavelet polarization information, textons, and sparse coding. Polarimetric synthesis unites with the discrete wavelet frame to obtain wavelet polarization variance through the calculation of the wavelet variance in the space of polarization states. The K-means cluster algorithm is implemented to cluster the wavelet polarization variance vectors of the training samples for the purpose of constructing a texton dictionary. A patch, in which all the wavelet polarization variance vectors match those in the texton dictionary, is used to obtain a statistical histogram. Sparse coding is applied to describe the histogram feature and generate a new texture feature called sparse coding of a wavelet polarization texton. Finally, support vector machine is used for the classification. All experiments are carried out on five sets of PolSAR data. The experimental results confirm that the proposed method effectively classifies PolSAR data. [ABSTRACT FROM AUTHOR]

The lowest-energy structures and the electronic properties of CdnSn (n = 1-8) clusters have been studied by using density-functional theory simulating package DMol3 in the generalized gradient approximation (GGA). The ring-like structures are the lowest-energy configurations for n = 2, 3 and the three-dimensional spheroid configurations for n = 4 - 8. The three-dimensional structures may be considered as being built from the Cd2S2 and Cd3S3 rings. Compared to the previous reports, we have found the more stable structures for CdnSn(n = 7,8). Calculations show that the magic numbers of CdnSn (n = 1 - 8) clusters are n = 3 and 6. As cluster size increases, the properties of CdnSn clusters tend to bulk-like ones in binding energy per CdS unit and Mulliken atomic charge, obtained by comparing with the calculated results of the wurtzite and zinc blende CdS for the same simulating parameters. [ABSTRACT FROM AUTHOR]

Abstract: Hemiaminals of pyrroles and indoles have been prepared from the lithium aluminum hydride reduction of 1-pyrrolyl and 1-indolyl carbamates with good yields (67–82%). These carbamates are more reactive than aliphatic amides and carbamates under the LAH reduction, but less reactive than esters. [Copyright &y& Elsevier]

Semantic segmentation is an important field for automatic processing of remote sensing image data. Existing algorithms based on Convolution Neural Network (CNN) have made rapid progress, especially the Fully Convolution Network (FCN). However, problems still exist when directly inputting remote sensing images to FCN because the segmentation result of FCN is not fine enough, and it lacks guidance for prior knowledge. To obtain more accurate segmentation results, this paper introduces edge information as prior knowledge into FCN to revise the segmentation results. Specifically, the Edge-FCN network is proposed in this paper, which uses the edge information detected by Holistically Nested Edge Detection (HED) network to correct the FCN segmentation results. The experiment results on ESAR dataset and GID dataset demonstrate the validity of Edge-FCN. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence, how to take advantage of deep learning and big data to classify polarimetric synthetic aperture radar (PolSAR) imagery is a hot topic in the field of remote sensing. As a key step for PolSAR image classification, feature extraction technology based on target decomposition is relatively mature, and how to extract discriminative spatial features and integrate these features with polarized information to maximize the classification accuracy is the core issue. In this context, this paper proposes a PolSAR image classification algorithm based on fully convolutional networks (FCNs) and a manifold graph embedding model. First, to describe different types of land objects more comprehensively, various polarized features of PolSAR images are extracted through seven kinds of traditional decomposition methods. Afterwards, drawing on transfer learning, the decomposed features are fed into multiple parallel and pre-trained FCN-8s models to learn deep multi-scale spatial features. Feature maps from the last layer of each FCN model are concatenated to obtain spatial polarization features with high dimensions. Then, a manifold graph embedding model is adopted to seek an effective and compact representation for spatially polarized features in a manifold subspace, simultaneously removing redundant information. Finally, a support vector machine (SVM) is selected as the classifier for pixel-level classification in a manifold subspace. Extensive experiments on three PolSAR datasets demonstrate that the proposed algorithm achieves a superior classification performance. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Rradar (SAR) provides rich ground information for remote sensing survey and can be used all time and in all weather conditions. Polarimetric SAR (PolSAR) can further reveal surface scattering difference and improve radar's application ability. Most existing classification methods for PolSAR imagery are based on manual features, such methods with fixed pattern has poor data adaptability and low feature utilization, if directly input to the classifier. Therefore, combining PolSAR data characteristics and deep network with auto-feature learning ability forms a new breakthrough direction. In fact, feature learning of deep network is to realize function approximation from data to label, through multi-layer accumulation, but finite layers limit the network's mapping ability. According to manifold hypothesis, high-dimensional data exists in potential low-dimensional manifold and different types of data locates in different manifolds. Manifold learning can model core variables of the target, and separate different data's manifold as much as possible, so as to complete data classification better. Therefore, taking manifold hypothesis as a starting point, nonlinear manifold learning integrated with fully convolutional networks for PolSAR image classification method is proposed in this paper. Firstly, high-dimensional polarized features are extracted based on scattering matrix and coherence matrix of original PolSAR data, whose compact representation is mined by manifold learning. Meanwhile, drawing on transfer learning, pre-trained Fully Convolutional Networks (FCN) model is utilized to learn deep spatial features of PolSAR imagery. Considering complementary advantages, weighted strategy is adopted to embed manifold representation into deep spatial features, which are input into support vector machine (SVM) classifier for final classification. A series of experiments on three PolSAR datasets have verified effectiveness and superiority of the proposed classification algorithm. [ABSTRACT FROM AUTHOR]

Due to the development of deep convolutional neural networks (CNNs), great progress has been made in semantic segmentation recently. In this paper, we present an end-to-end Bayesian segmentation network based on generative adversarial networks (GANs) for remote sensing images. First, fully convolutional networks (FCNs) and GANs are utilized to realize the derivation of the prior probability and the likelihood to the posterior probability in Bayesian theory. Second, the cross-entropy loss in the FCN serves as an a priori to guide the training of the GAN, so as to avoid the problem of mode collapse during the training process. Third, the generator of the GAN is used as a teachable spatial filter to construct the spatial relationship between each label. Some experiments were performed on two remote sensing datasets, and the results demonstrate that the training of the proposed method is more stable than other GAN based models. The average accuracy and mean intersection (MIoU) of the two datasets were 0.0465 and 0.0821, and 0.0772 and 0.1708 higher than FCN, respectively. [ABSTRACT FROM AUTHOR]

In the past two decades, traditional hand-crafted feature based methods and deep feature based methods have successively played the most important role in image classification. In some cases, hand-crafted features still provide better performance than deep features. This paper proposes an innovative network based on deep learning integrated with binary coding and Sinkhorn distance (DBSNet) for remote sensing and texture image classification. The statistical texture features of the image extracted by uniform local binary pattern (ULBP) are introduced as a supplement for deep features extracted by ResNet-50 to enhance the discriminability of features. After the feature fusion, both diversity and redundancy of the features have increased, thus we propose the Sinkhorn loss where an entropy regularization term plays a key role in removing redundant information and training the model quickly and efficiently. Image classification experiments are performed on two texture datasets and five remote sensing datasets. The results show that the statistical texture features of the image extracted by ULBP complement the deep features, and the new Sinkhorn loss performs better than the commonly used softmax loss. The performance of the proposed algorithm DBSNet ranks in the top three on the remote sensing datasets compared with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural networks (CNNs) achieve impressive results on remote sensing scene classification, which is a fundamental problem for scene semantic understanding. However, convolution, the most essential operation in CNNs, restricts the development of CNN-based methods for scene classification. Convolution is not efficient enough for high-resolution remote sensing images and limited in extracting discriminative features due to its linearity. Thus, there has been growing interest in improving the convolutional layer. The hardware implementation of the JPEG2000 standard relies on the lifting scheme to perform wavelet transform (WT). Compared with the convolution-based two-channel filter bank method of WT, the lifting scheme is faster, taking up less storage and having the ability of nonlinear transformation. Therefore, the lifting scheme can be regarded as a better alternative implementation for convolution in vanilla CNNs. This paper introduces the lifting scheme into deep learning and addresses the problems that only fixed and finite wavelet bases can be replaced by the lifting scheme, and the parameters cannot be updated through backpropagation. This paper proves that any convolutional layer in vanilla CNNs can be substituted by an equivalent lifting scheme. A lifting scheme-based deep neural network (LSNet) is presented to promote network applications on computational-limited platforms and utilize the nonlinearity of the lifting scheme to enhance performance. LSNet is validated on the CIFAR-100 dataset and the overall accuracies increase by 2.48% and 1.38% in the 1D and 2D experiments respectively. Experimental results on the AID which is one of the newest remote sensing scene dataset demonstrate that 1D LSNet and 2D LSNet achieve 2.05% and 0.45% accuracy improvement compared with the vanilla CNNs respectively. [ABSTRACT FROM AUTHOR]

The classification of synthetic aperture radar (SAR) images is of great importance for rapid scene understanding. Recently, convolutional neural networks (CNNs) have been applied to the classification of single-polarized SAR images. However, it is still difficult due to the random and complex spatial patterns lying in SAR images, especially in the case of finite training data. In this paper, a pattern statistics network (PSNet) is proposed to address this problem. PSNet borrows the idea from the statistics and probability theory and explicitly embeds the random nature of SAR images in the representation learning. In the PSNet, both fluctuation and pattern representations are extracted for SAR images. More specifically, the fluctuation representation does not consider the rigorous relationships between local pixels and only describes the average fluctuation of local pixels. By contrast, the pattern representation is devoted to hierarchically capturing the interactions between local pixels, namely, the spatial patterns of SAR images. The proposed PSNet is evaluated on three real SAR data, including spaceborne and airborne data. The experimental results indicate that the fluctuation representation is useful and PSNet achieves superior performance in comparison with related CNN-based and texture-based methods. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the role of the oral and gut microbiome related to systemic metabolism and clinical parameters in various types of kidney stone disease. Patients and methods: We conducted a case–control study by analyzing 16S rRNA and untargeted metabolomics profiling of 76 fecal, 68 saliva, 73 urine, and 43 serum samples from 76 participants aged 18–75 years old. The participants included 15 patients with uric acid stones, 41 patients with calcium oxalate stones, and 20 healthy controls. Correlations among microbiome, metabolism, and clinical parameters were identified through Spearman’s correlation analysis. (Clinical trial No. ChiCTR2200055316). Results: Patients with uric acid stones exhibited reduced richness and diversity in their microbiome, as well as altered composition in both oral and gut microbiome. Furthermore, their fecal samples showed lower relative abundances of Bacteroides and Lachnospiraceae, while their saliva samples showed higher relative abundances of Porphyromonas and Neisseria. Predicted KEGG metabolism pathways, including amino acid and fatty acid metabolisms, were significantly altered in subjects with uric acid stones. Oral, gut microbiota, and metabolism were also associated with low water intake and urine pH. The area under the curve (AUC) of the specific microbiota and metabolite prediction models was over 0.85. Conclusion: The structure and composition of the oral and gut microbiome in different types of kidney stone disease, the correlations between oral and gut microbiome, and the associations among oral and gut microbiota, systemic metabolism and clinical parameters imply an important role that the oral and gut microbiome may play in kidney stone disease. [ABSTRACT FROM AUTHOR]

The striped flea beetle, Phyllotreta striolata (Fabricius), damages crops in the Brassicaceae. The genetic data for this pest are insufficient to reveal its insecticide resistance mechanisms or to develop molecular markers for resistance monitoring. We used PacBio Iso-Seq technology to sequence the full-length transcriptome of P. striolata. After isoform sequence clustering and removal of redundant transcripts, a total of 41,293 transcripts were obtained, and 35,640 of these were annotated in the database of gene products. Structure analysis uncovered 4,307 alternative splicing events, and 3,836 sequences were recognized as lncRNAs. Transcripts with the complete coding region of important detoxification enzymes were further classified. There were 57 transcripts of P450s distributed in CYP2, CYP3, CYP4, and Mito CYP clades, 29 transcripts of ESTs from 4 functional groups, 17 transcripts of GSTs classified into 5 families, 51 transcripts of ABCs distributed in 6 families, and 19 transcripts of UGTs. Twenty-five lncRNAs were predicted to be regulators of these detoxification genes. Full-length transcriptome sequencing is an efficient method for molecular study of P. striolata and it is also useful for gene function analysis. [ABSTRACT FROM AUTHOR]

Semantic segmentation is an important process of scene recognition with deep learning frameworks achieving state of the art results, thus gaining much attention from the remote sensing community. In this paper, an end-to-end conditional random fields generative adversarial segmentation network is proposed. Three key factors of this algorithm are as follows. First, the network combines generative adversarial network and Bayesian framework to realize the estimation from the prior probability to the posterior probability. Second, the skip connected encoder-decoder network is combined with CRF layer to implement end-to-end network training. Finally, the adversarial loss and the cross-entropy loss guide the training of the segmentation network through back propagation. The experimental results show that our proposed method outperformed FCN in terms of mIoU for 0.0342 and 0.11 on two data sets, respectively. [ABSTRACT FROM AUTHOR]

Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The convolutional neural network (CNN) has shown great potential in many fields; however, transferring this potential to synthetic aperture radar (SAR) image interpretation is still a challenging task. The coherent imaging mechanism causes the SAR signal to present strong fluctuations, and this randomness property calls for many degrees of freedom (DoFs) for the SAR image description. In this paper, a statistics learning network (SLN) based on the quadratic form is presented. The statistical features are expected to be fitted in the SLN for SAR image representation. (i) Relying on the quadratic form in linear algebra theory, a quadratic primitive is developed to comprehensively learn the elementary statistical features. This primitive is an extension to the convolutional primitive that involves both nonlinear and linear transformations and provides more flexibility in feature extraction. (ii) With the aid of this quadratic primitive, the SLN is proposed for the classification task. In the SLN, different types of statistics of SAR images are automatically extracted for representation. Experimental results on three datasets show that the SLN outperforms a standard CNN and traditional texture-based methods and has potential for SAR image classification. [ABSTRACT FROM AUTHOR]

Automatic image registration of optical-to-Synthetic aperture radar (SAR) images is difficult because of the inconsistency of radiometric and geometric properties between the optical image and the SAR image. The intensity-based methods may require many calculations and be ineffective when there are geometric distortions between these two images. The feature-based methods have high requirements on features, and there are certain challenges in feature extraction and matching. A new automatic optical-to-SAR image registration framework is proposed in this paper. First, modified holistically nested edge detection is employed to detect the main contours in both the optical and SAR images. Second, a mesh grid strategy is presented to perform a coarse-to-fine registration. The coarse registration calculates the feature matching and summarizes the preliminary results for the fine registration process. Finally, moving direct linear transformation is introduced to perform a homography warp to alleviate parallax. The experimental results show the effectiveness and accuracy of our proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a component-based multi-layer parallel network is proposed for airplane detection in Synthetic Aperture Radar (SAR) imagery. In response to the problems called sparsity and diversity brought by SAR scattering mechanism, depth characteristics and component structure are utilized in the presented algorithm. Compared with traditional features, the depth characteristics have better description ability to deal with diversity. Component information is contributing in detecting complete targets. The proposed algorithm consists of two parallel networks and a constraint layer. First, the component information is introduced into the network by labeling. Then, the overall target and corresponding components are detected by the trained model. In the following discriminative constraint layer, the maximum probability and prior information are adopted to filter out wrong detection. Experiments for several comparative methods are conducted on TerraSAR-X SAR imagery; the results indicate that the proposed network has a higher accuracy for airplane detection. [ABSTRACT FROM AUTHOR]

The title mol­ecule, C20H20N2O2S, exits in the Z form and the five-membered imidazole ring and the benzene ring of the 4-methoxy­benzyl­idene moiety are almost coplanar. Short intra­molecular contacts [C⋯S = 3.138 (4) Å and C⋯N = 3.060 (4) Å] indicate the presence of weak C—H⋯S and C—H⋯N intra­molecular hydrogen bonds. [ABSTRACT FROM AUTHOR]

The accuracy of registration in image stitching task directly affects the performance of subsequent stages. Traditional registration methods rely heavily on the quality of the features when calculating the homography matrix, resulting in alignment failures in low‐texture or low‐overlap scenes due to extracting insufficient features. On the other hand, existing DNN‐based methods for homography estimation are more robust in multiple scenes but previous work usually employs an overly simple convolutional network structure to directly regress the homography, ignoring the redundant information contained in the feature maps so that their prediction accuracy is inferior to the traditional methods in simple scenarios. To overcome the disadvantages of the two methods, a Multi‐scale structure is proposed to extract feature maps at three scales and design two modules to handle the matrix prediction respectively. The DFA‐T module analyzes semantic information on the high‐level features to accomplish coarse‐grained alignment while the Contextual Correlation module on the bottom level to accomplish more accurate alignment. Experiments demonstrate that this method provides more accurate alignment results than the existing state‐of‐the‐art DNN‐based methods and outperforms traditional algorithms with more stable results in some extreme scenarios. [ABSTRACT FROM AUTHOR]

Image stitching aims to stitch multiple images with overlapping areas into a high-resolution image with natural appearance, no ghosting and no seam, so as to obtain a wide field of vision. And the stitching is required to be as fast as possible. In this article, we discuss the loss of foreground object caused by the seam-cut method for image stitching. Moreover, we propose an improved seam-cut method based on superpixel to solve this problem. The proposed method uses the matching information of feature points and superpixel segmentation to divide the scene into foreground and background. By adjusting the penalty value of foreground superpixels in the superpixel-based seam-cut method, the foreground object can avoid the cutting of optimal seam. The experimental results demonstrate that our method will not cause the ghosting of foreground object or the loss of foreground object in the stitching result in the scene with large parallax. In this method, the foreground object will be preserved completely. [ABSTRACT FROM AUTHOR]

In the field of multi-target tracking, the widely embraced tracking-by-detection paradigm has rapidly progressed with the refinement of detectors and matching techniques. However, the paradigm of joint detection and tracking is relatively limited, and it is difficult to model complex scenes, such as the complexities introduced by camera motion and occlusion. In this work, a hierarchical joint detection and tracking framework is proposed, namely MSPNet. From a temporal concern, a motion-guided feature aggregation module is proposed to address the complexities of multi-frame variations. From a spatial concern, an occlusion-aware head and hierarchical spatial association are proposed to handle the challenges of occlusion. Extensive experiments on MOT challenging benchmarks demonstrate that the MSPNet can effectively reduce false negatives and improve the accuracy of tracking while outperforming a wide range of existing methods. • An enhanced MOT model through motion-guided spatial perception is proposed, namely MSPNet, which optimizes detection and tracking jointly. • To capture temporal variations and ensure spatial motion consistency of instances, a motion-guided cross-temporal feature aggregation module (MFA) is proposed. • To handle the cases of occlusion, occlusion-aware head (OAH) and spatial hierarchical association (SHA) are proposed. The OAH and SHA improves the tracking performance of the network for challenging samples, specifically occluded targets. • Incorporating the power of MFA, OAH, and SHA, the proposed MSPNet, demonstrates notable improvements compared to previous models. [ABSTRACT FROM AUTHOR]

Recently, object detection in natural images has made a breakthrough, but it is still challenging in oriented ship detection for remote sensing imagery. Considering some limitations in this task, such as uncertain ship orientation, unspecific features for locating and classification in the complex optical environment, and multiplicative speckle interference of synthetic aperture radar (SAR), we propose an oriented ship detector based on the pairwise branch detection head and adaptive SAR feature enhancement. The details are as follows: (1) Firstly, the ships with arbitrary directions are described with a rotated ground truth, and an oriented region proposal network (ORPN) is designed to study the transformation from the horizontal region of interest to the rotated region of interest. The ORPN effectively improved the quality of the candidate area while only introducing a few parameters. (2) In view of the existing algorithms that tend to perform classification and regression prediction on the same output feature, this paper proposes a pairwise detection head (PBH) to design parallel branches to decouple classification and locating tasks, so that each branch can learn more task-specific features. (3) Inspired by the ratio-of-average detector in traditional SAR image processing, the SAR edge enhancement (SEE) module is proposed, which adaptively enhances edge pixels, and the threshold of the edge is learned by the channel-shared adaptive thresholds block. Experiments were carried out on both optical and SAR datasets. In the optical dataset, PBH combined with ORPN improved recall by 5.03%, and in the SAR dataset, the overall method achieved a maximum F1 score improvement of 6.07%; these results imply the validity of our method. [ABSTRACT FROM AUTHOR]

Recently, deep-learning methods have yielded rapid progress for object detection in synthetic aperture radar (SAR) imagery. It is still a great challenge to detect ships in SAR imagery due to ships' small size and confusable detail feature. This article proposes a novel anchor-free detection method composed of two modules to deal with these problems. First, for the lack of detailed information on small ships, we suggest an adaptive feature-encoding module (AFE), which gradually fuses deep semantic features into shallow layers and realizes the adaptive learning of the spatial fusion weights. Thus, it can effectively enhance the external semantics and improve the representation ability of small targets. Next, for the foreground–background imbalance, the Gaussian-guided detection head (GDH) is introduced according to the idea of soft sampling and exploits Gaussian prior to assigning different weights to the detected bounding boxes at different locations in the training optimization. Moreover, the proposed Gauss-ness can down-weight the predicted scores of bounding boxes far from the object center. Finally, the effect of the detector composed of the two modules is verified on the two SAR ship datasets. The results demonstrate that our method can effectively improve the detection performance of small ships in datasets. [ABSTRACT FROM AUTHOR]

The New Chinese Diabetes Risk Score (NCDRS) is one of the recommended tools for screening undiagnosed type 2 diabetes in China. However, its performance in detecting undiagnosed diabetes needs to be verified in different community populations. Also, it is unknown whether NCDRS can be used in detecting prediabetes. In the present study, we aimed to evaluate the performance of NCDRS in detecting undiagnosed diabetes and prediabetes among the community residents in eastern China. We applied NCDRS in 7675 community residents aged 18-65 years old in Jiangsu Province. The results showed that the participants with undiagnosed diabetes reported the highest NCDRS value, followed by those with prediabetes (P < 0.001). The best cut-off points of NCDRS for detecting undiagnosed diabetes and prediabetes were 27 (with a sensitivity of 78.0% and a specificity of 57.7%) and 27 (with a sensitivity of 66.0% and a specificity of 62.9%). The AUCs of NCDRS for identifying undiagnosed diabetes and prediabetes were 0.749 (95% CI: 0.739~0.759) and 0.694 (95% CI: 0.683~0.705). These results demonstrate the excellent performance of NCDRS in screening undiagnosed diabetes in the community population in eastern China and further provide evidence for using NCDRS in detecting prediabetes. [ABSTRACT FROM AUTHOR]

Waardenburg syndrome (WS) is an autosomal dominant inherited non-syndromic type of hereditary hearing loss characterized by varying combinations of sensorineural hearing loss and abnormal pigmentation of the hair, skin, and inner ear. WS is classified into four subtypes (WS1–WS4) based on additional symptoms. WS2 is characterized by the absence of additional symptoms. Recently, we identified a SOX10 missense mutation c.422T > C (p.L141P) associated with WS2. We performed functional assays and found the mutant loses DNA-binding capacity, shows aberrant cytoplasmic and nuclear localization, and fails to interact with PAX3. Therefore, the mutant cannot transactivate the MITF promoter effectively, inhibiting melanin synthesis and leading to WS2. Our study confirmed haploinsufficiency as the underlying pathogenesis for WS2. [ABSTRACT FROM AUTHOR]

Bartter syndrome type IV, characterized by salt-losing nephropathies and sensorineural deafness, is caused by mutations of BSND or simultaneous mutations of both CLCNKA and CLCNKB . GJB2 is the primary causative gene for non-syndromic sensorineural deafness and associated with several syndromic sensorineural deafness. Owing to the rarity of Bartter syndrome, only a few mutations have been reported in the abovementioned causative genes. To investigate the underlying mutations in a Chinese patient with Bartter syndrome type IV, genetic analysis of BSND , CLCNKA , CLCNKB and GJB2 were performed by polymerase chain reaction and direct sequencing. Finally, double homozygous mutations c.22C > T (p.Arg8Trp) and c.127G > A (Val43Ile) were detected in exon 1 of BSND . Intriguingly, compound heterozygous mutations c.235delC (p.Leu79CysfsX3) and c.109G > A (p.Val37Ile) were also revealed in exon 2 of GJB2 in the same patient. No pathogenic mutations were found in CLCNKA and CLCNKB . Our results indicated that the homozygous mutation c.22C > T was the key genetic reason for the proband, and a digenic effect of BSND and GJB2 might contributed to sensorineural deafness. To our knowledge, it was the first report showing that the GJB2 gene mutations were detected in Bartter syndrome. [ABSTRACT FROM AUTHOR]

• A novel framework of image stitching with large parallax based on disparity feature. • Stereo disparity estimation free from camera calibration. • Plane clustering and alignment to avoid artifacts due to discontinuous content. Image stitching aims to warp and align two or more images with overlapping areas to generate a panorama with a larger field of view. Due to the wide baseline or the abrupt depth changes caused by the foreground objects, the phenomenon that adjacent pixels or regions in one image are not adjacent in another image happens. It is difficult to avoid severe parallax artifacts and get good alignment results when stitching such images. In this paper, focusing on the images with large parallax, we design a multi-plane alignment algorithm guided by the disparity map. The concept of average tolerable parallax is proposed to help distinguish one background plane and multiple foreground objects from the image, which are robust to small parallax. In order to obtain the reliable disparity map of the image pairs got from any camera with unknown camera parameters, we propose an optimal homography estimation method based on the relative projection biases. This helps to satisfy the common epipolar line constraint when applying stereo matching modules. Experimental results demonstrate that our algorithm provides accurate stitching results on images with large parallax, and outperforms other existing state-of-the-art methods both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Abstract: Waardenburg syndrome type IV (WS4) is a rare genetic disorder, characterized by auditory–pigmentary abnormalities and Hirschsprung disease. Mutations of the EDNRB gene, EDN3 gene, or SOX10 gene are responsible for WS4. In the present study, we reported a case of a Chinese patient with clinical features of WS4. In addition, the three genes mentioned above were sequenced in order to identify whether mutations are responsible for the case. We revealed a novel nonsense mutation, c.1063C>T (p.Q355*), in the last coding exon of SOX10. The same mutation was not found in three unaffected family members or 100 unrelated controls. Then, the function and mechanism of the mutation were investigated in vitro. We found both wild-type (WT) and mutant SOX10 p.Q355* were detected at the expected size and their expression levels are equivalent. The mutant protein also localized in the nucleus and retained the DNA-binding activity as WT counterpart; however, it lost its transactivation capability on the MITF promoter and acted as a dominant-negative repressor impairing function of the WT SOX10. [Copyright &y& Elsevier]

Due to device limitations, small networks are necessary for some real-world scenarios, such as satellites and micro-robots. Therefore, the development of a network with both good performance and small size is an important area of research. Deep networks can learn well from large amounts of data, while manifold networks have outstanding feature representation at small sizes. In this paper, we propose an approach that exploits the advantages of deep networks and shallow Grassmannian manifold networks. Inspired by knowledge distillation, we use the information learned from convolutional neural networks to guide the training of the manifold networks. Our approach leads to a reduction in model size, which addresses the problem of deploying deep learning on resource-limited embedded devices. Finally, a series of experiments were conducted on four remote sensing scene classification datasets. The method in this paper improved the classification accuracy by 2.31% and 1.73% on the UC Merced Land Use and SIRIWHU datasets, respectively, and the experimental results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

To disperse the unreacted overhead gas phase into the liquid in an agitated reactor without gas outlet, a gas-inducing impeller is usually employed. To determine the lowest critical rotating speed, the gas-induction mechanism was reconsidered by constituting a mechanical energy conservation equation between the gas inlet orifice in the gas phase and the gas outlet orifice in the liquid phase under a certain rotational speed. According to this model, the critical speed of the gas-inducing impeller could be basically determined by the submersion depth and the radial position of the gas outlet, and a novel design was proposed by introduction of six short pipes stretched radially from the axis of the impeller. The final design of the gas-inducing impeller was obtained by an optimal combination of the gas-inducing pipes, the blades of the impeller and the baffle dimension. [ABSTRACT FROM AUTHOR]

Few-shot object detection is a recently emerging branch in the field of computer vision. Recent research studies have proposed several effective methods for object detection with few samples. However, their performances are limited when applied to remote sensing images. In this article, we specifically analyze the characteristics of remote sensing images and propose a few-shot fine-tuning network with a shared attention module (SAM) to adapt to detecting remote sensing objects, which have large size variations. In our SAM, multi-attention maps are computed in the base training stage and shared with the feature extractor in the few-shot fine-tuning stage as prior knowledge to help better locate novel class objects with few samples. Moreover, we design a new few-shot fine-tuning stage with a balanced fine-tuning strategy (BFS), which helps in mitigating the severe imbalance between the number of novel class samples and base class samples caused by the few-shot settings to improve the classification accuracy. We have conducted experiments on two remote sensing datasets (NWPU VHR-10 and DIOR), and the excellent results demonstrate that our method makes full use of the advantages of few-shot learning and the characteristics of remote sensing images to enhance the few-shot detection performance. [ABSTRACT FROM AUTHOR]

Abstract: High magnetic field superconducting magnet technology has been developed in the recent years for all kinds of applications in China. The superconducting magnets on the basis of the conduction-cooled high (HTS) and lower temperature superconductor (LTS) through GM cryocooler are designed, fabricated and operated for the magnetic separator, superconducting magnet energy storage system (SMES), material processing, gyrotron, electromagnetic launcher, space anti-matter detection, magnetic surgery system (MSS), heavy ion accelerator dipole magnet and test bed for characteristics of superconducting material in Institute of Electrical Engineering, Chinese Academy of Sciences (IEECAS). The EAST superconducting Tokamak is being fabricated in Institute of Plasma Physics, Chinese Academy of Sciences. In the paper, we report the successful development of high magnetic field superconducting magnet technology in China. Some new research projects, such as 40T hybrid magnet, 25T high magnetic field superconducting magnet, split-pair magnets for the pallation Neutron Source, high temperature superconducting coils for MSS and MRI are introduced. [Copyright &y& Elsevier]

The title compound, C19H22N3O7, is a nefidipine analogue. The crystal packing is stabilized by intermolecular N—H...O hydrogen bonds. [ABSTRACT FROM AUTHOR]

The design of backbones is of great significance for enhancing the location and classification precision in the remote sensing target detection task. Recently, various approaches have been proposed on altering the feature extraction density in the backbones to enlarge the receptive field, make features prominent, and reduce computational complexity, such as dilated convolution and deformable convolution. Among them, one of the most widely used methods is strided convolution, but it loses the information about adjacent feature points which leads to the omission of some useful features and the decrease of detection precision. This paper proposes a novel sparse density feature extraction method based on the relationship between the lifting scheme and convolution, which improves the detection precision while keeping the computational complexity almost the same as the strided convolution. Experimental results on remote sensing target detection indicate that our proposed method improves both detection performance and network efficiency. [ABSTRACT FROM AUTHOR]

As the application scenarios of remote sensing imagery (RSI) become richer, the task of ship detection from an overhead perspective is of great significance. Compared with traditional methods, the use of deep learning ideas has more prospects. However, the Convolutional Neural Network (CNN) has poor resistance to sample differences in detection tasks, and the huge differences in the image environment, background, and quality of RSIs affect the performance for target detection tasks; on the other hand, upsampling or pooling operations result in the loss of detailed information in the features, and the CNN with outstanding results are often accompanied by a high computation and a large amount of memory storage. Considering the characteristics of ship targets in RSIs, this study proposes a detection framework combining an image enhancement module with a dense feature reuse module: (1) drawing on the ideas of the generative adversarial network (GAN), we designed an image enhancement module driven by object characteristics, which improves the quality of the ship target in the images while augmenting the training set; (2) the intensive feature extraction module was designed to integrate low-level location information and high-level semantic information of different resolutions while minimizing the computation, which can improve the efficiency of feature reuse in the network; (3) we introduced the receptive field expansion module to obtain a wider range of deep semantic information and enhance the ability to extract features of targets were at different sizes. Experiments were carried out on two types of ship datasets, optical RSI and Synthetic Aperture Radar (SAR) images. The proposed framework was implemented on classic detection networks such as You Only Look Once (YOLO) and Mask-RCNN. The experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between different color channels and the two-dimensional entropy are calculated. In the frequency domain, the statistical characteristics of the two-dimensional entropy and the mutual information of the filtered subband images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The implementation is available on github https://github.com/jacob6/ENIQA. [ABSTRACT FROM AUTHOR]