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Recently, RGB–T saliency detection becomes gradually a hot topic due to the fact that RGB–T multi-modal data could overcome the limitation of conventional RGB data in some cases. However, existing RGB–T saliency detection methods usually fail to take both advantages of two modalities and cannot boost performance effectively. Therefore, we achieve RGB–T saliency detection via a novel method, namely cross-modal collaborative propagation (CMCP), which contains a novel saliency propagation mechanism and a novel cross-modal collaborative learning framework relied on the proposed propagation mechanism. More specifically, we firstly propose a novel saliency propagation method and then, respectively, regard two modalities as inputs to generate RGB-induced and thermal-induced propagation mechanisms. To bridge RGB–T modalities, a novel cross-modal collaborative learning framework between RGB-induced and thermal-induced propagation mechanisms is devised to optimize, respectively, two propagation results. In other words, two modalities constantly extract supervision information to help the opposite side to refine propagation result until attaining a stable state. Finally, we integrate two modalities-induced propagation results into a refined saliency map. We compare our model with the state-of-the-art RGB–T and RGB saliency detection algorithms on three benchmark datasets, and experimental results show that the proposed CMCP achieves the significant improvement. [ABSTRACT FROM AUTHOR]

Eye diseases often affect human health. Accurate detection of the optic disc contour is one of the important steps in diagnosing and treating eye diseases. However, the structure of fundus images is complex, and the optic disc region is often disturbed by blood vessels. Considering that the optic disc is usually a saliency region in fundus images, we propose a weakly-supervised optic disc detection method based on the fully convolution neural network (FCN) combined with the weighted low-rank matrix recovery model (WLRR). Firstly, we extract the low-level features of the fundus image and cluster the pixels using the Simple Linear Iterative Clustering (SLIC) algorithm to generate the feature matrix. Secondly, the top-down semantic prior information provided by FCN and bottom-up background prior information of the optic disc region are used to jointly construct the prior information weighting matrix, which more accurately guides the decomposition of the feature matrix into a sparse matrix representing the optic disc and a low-rank matrix representing the background. Experimental results on the DRISHTI-GS dataset and IDRiD dataset show that our method can segment the optic disc region accurately, and its performance is better than existing weakly-supervised optic disc segmentation methods. Graphical abstract of optic disc segmentation [ABSTRACT FROM AUTHOR]

Although recently a series of deep‐learning based methods have promoted the performance of face super resolution (FSR), most of these methods cannot recover essential face attributes accurately, especially to super‐resolve a very low‐resolution (LR) face image (16×16 pixels) to its (8×) high‐resolution (HR) version. To address this issue, a novel alternating optimisation algorithm to estimate facial attributes and restore facial images in a single network is presented. Specifically, two convolutional neural modules (denoted as Restorer and Corrector) are constructed and these two modules are alternated repeatedly to form an end‐to‐end trainable network. The Restorer module reconstructs face images based on estimated facial attributes, while the Corrector module corrects the estimated attributes with the help of restored face image. Since Corrector can utilise information from previous estimated attributes and the FSR image, the estimated attributes are iteratively corrected and gradually approach the ground truth. Moreover, a new attribute transformation scheme is designed to introduce attribution information into Restorer, in which the facial attribute vectors as the control conditions can guide the face image restoration explicitly. By conducting extensive experiments on the well‐known CelebA dataset, it is demonstrated that the proposed method can provide superior FSR performance in both quantitative and qualitative measurements. [ABSTRACT FROM AUTHOR]

Glaucoma is a group of eye conditions, which can seriously damage optic nerves because of an elevated intraocular pressure. Nowadays, glaucoma has become one of the principal causes of blindness that results in irreversible visual loss. Early screening and treatment of glaucoma can prevent further progression of optic nerve degeneration effectively. The vertical cup-to-disc ratio (CDR) is a commonly used measurement for the detection of glaucoma, and therefore accurate segmentation of optic disc (OD) and optic cup (OC) regions in retinal fundus images is of great significance. In this paper, we present a prior shape constraint-based two-layer level set method for OD and OC segmentation in fundus images. This method uses two different layers of one level set function to represent the OD and OC boundaries. In this method, the distance regularized term is designed to guarantee that the distance between the OD and OC varies smoothly. By introducing the prior shape constraint term energy, the final segmentation results of OD and OC are always in the shape of approximate ellipses. In addition, the proposed method has the property of dealing with the intensity inhomogeneity of fundus images through the local fitting energy embedded. Experiments on images from the Baidu Research database demonstrate that the proposed method has superior performance in terms of accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

Yu, X., 2019. On-line ship route planning of cold-chain logistics distribution based on cloud computing. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1132–1137. Coconut Creek (Florida), ISSN 0749-0208. In order to improve the efficiency of cold chain logistics distribution in cloud computing environment, it is necessary to optimize the location of ship route in cold chain logistics distribution. An online planning algorithm of cold chain logistics distribution ship route in cloud computing environment is proposed based on the shortest path optimization control. In the cloud computing environment, the shortest path search model is used to optimize the ship route of cold chain logistics distribution and the location design of cold chain logistics distribution path node. Based on the load balancing model of distribution network, the ship route location and distribution route planning of cold chain logistics under cloud computing environment are carried out. The optimal distribution route control protocol is used to control the cold-chain logistics shipping route in cloud computing environment, and the minimum distribution route is used as the constraint cost. Realize the on-line planning algorithm optimization design of cold chain logistics distribution ship route under cloud computing environment. The simulation results show that under the cloud computing environment, the on-line planning route overhead of cold chain logistics distribution ship route is lower, the balance of distribution network is better, and the output of cold chain logistics distribution is high. Improve the efficiency of cold chain logistics distribution under cloud computing environment. [ABSTRACT FROM AUTHOR]

Yu, X., 2019. On-line ship route planning of cold-chain logistics distribution based on cloud computing. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1132–1137. Coconut Creek (Florida), ISSN 0749-0208. In order to improve the efficiency of cold chain logistics distribution in cloud computing environment, it is necessary to optimize the location of ship route in cold chain logistics distribution. An online planning algorithm of cold chain logistics distribution ship route in cloud computing environment is proposed based on the shortest path optimization control. In the cloud computing environment, the shortest path search model is used to optimize the ship route of cold chain logistics distribution and the location design of cold chain logistics distribution path node. Based on the load balancing model of distribution network, the ship route location and distribution route planning of cold chain logistics under cloud computing environment are carried out. The optimal distribution route control protocol is used to control the cold-chain logistics shipping route in cloud computing environment, and the minimum distribution route is used as the constraint cost. Realize the on-line planning algorithm optimization design of cold chain logistics distribution ship route under cloud computing environment. The simulation results show that under the cloud computing environment, the on-line planning route overhead of cold chain logistics distribution ship route is lower, the balance of distribution network is better, and the output of cold chain logistics distribution is high. Improve the efficiency of cold chain logistics distribution under cloud computing environment. [ABSTRACT FROM AUTHOR]

Detection of suspicious pulmonary nodules from lung CT scans is a crucial task in computer-aided diagnosis (CAD) systems. In recent years, various deep learning-based approaches have been proposed and demonstrated significant potential for addressing this task. However, existing deep convolutional neural networks exhibit limited long-range dependency capabilities and neglect crucial contextual information, resulting in reduced performance on detecting small-size nodules in CT scans. In this work, we propose a novel end-to-end framework called LGDNet for the detection of suspicious pulmonary nodules in lung CT scans by fusing local features and global representations. To overcome the limited long-range dependency capabilities inherent in convolutional operations, a dual-branch module is designed to integrate the convolutional neural network (CNN) branch that extracts local features with the transformer branch that captures global representations. To further address the issue of misalignment between local features and global representations, an attention gate module is proposed in the up-sampling stage to selectively combine misaligned semantic data from both branches, resulting in more accurate detection of small-size nodules. Our experiments on the large-scale LIDC dataset demonstrate that the proposed LGDNet with the dual-branch module and attention gate module could significantly improve the nodule detection sensitivity by achieving a final competition performance metric (CPM) score of 89.49%, outperforming the state-of-the-art nodule detection methods, indicating its potential for clinical applications in the early diagnosis of lung diseases. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation using Magnetic Resonance (MR) Imaging technology plays a significant role in computer-aided brain tumor diagnosis. However, when applying classic segmentation methods, limitations such as inhomogeneous intensity, complex physiological structure and blurred tissues boundaries in brain MR images usually lead to unsatisfactory results. To address these issues, this paper proposes an automatic sparse constrained level set method to realize the brain tumor segmentation in MR images. By studying brain tumor images, this method finds out common characteristics of brain tumors' shape and constructs a sparse representation model. By considering this model as a prior constraint, an energy function based on level set method is constructed. In experiments, the proposed method can achieve an average accuracy of 96.20% for the MR images from the dataset Brats2017 and performs better than the others. With lower false positive rate and stronger robustness, the experimental results show that the proposed method can segment brain tumor from MR image accurately and stably. • Automatically locate brain tumors and generate initial contours in MR images. • Construct a sparse shape constraint and incorporate it into the energy function. • Improve the performance of level set method in segmenting complex brain tumor. [ABSTRACT FROM AUTHOR]

Saliency detection is still very challenging in computer vision and image processing. In this paper, we propose a novel visual saliency detection framework via bagging-based saliency distribution learning (BSDL). Given an input image, we firstly segment it into superpixels as basic units. Then two prior knowledge containing background prior and center prior are integrated to generate an initial prior map, which is used to select training samples from all superpixels to train the BSDL model. Specifically, the BSDL contains two stages: In the first stage, we use bagging-based sampling method to train K saliency classifiers from all training samples. K saliency classifiers are used to predict each superpixel saliency value. In the second stage, we aim to learn a saliency distribution model, whose goal is to infer the relationship between each classifier and each superpixel. i.e., for each superpixel, the BSDL not only trains K saliency classifiers to predict its saliency value, but also infers the reliability of using each saliency classifier to predict its saliency value. As a result, each superpixel's saliency value is determined by its K prediction saliency values and saliency distribution. After the BSDL, we propose a so called foreground consistency saliency optimization framework (FCSO) to further refine saliency map obtained by BSDL. To improve computation efficiency, a prejudgment rule is proposed to evaluate the quality of saliency map obtained by BSDL, which is used to decide whether the FCSO is needed for input image. Experimental results on four public datasets demonstrate the superiority of the proposed method than other state-of-the-art methods. • We propose a novel bagging-based saliency distribution learning framework for visual saliency detection. • We propose a so called foreground consistency saliency optimization framework to further refine saliency map. • An effective prejudgment mechanism is developed to improve computational efficiency. • Experimental results on four datasets indicate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Propagation-based methods have attracted more and more attention in saliency detection. We present a propagation-based saliency detection framework to overcome some drawbacks in existing methods. First, we construct an effective background-based map by defining a method to remove foreground noises in image boundaries. Second, a saliency propagation method is proposed to further refine the background-based map by introducing a feedback regulation mechanism into saliency propagation. Finally, an optimization method is developed to further enhance the contrast between salient object and background. Extensive experiments on five benchmark datasets are carried out, and experimental results show the superiority of the proposed method than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a salient object detection method based on novel graph structure. Given image is segmented into small image regions as basic units, we firstly construct an effective background-based map, each image region's saliency value is determined by its feature contrast with the image boundary. Then, saliency propagation mechanism is used to update all regions' saliency values by introducing a novel graph structure to better exploit the relationship between adjacent image regions. Finally, we propose an optimization method to further highlight salient objects and suppress background noises. Experimental results demonstrate adequately the superiority of proposed approach. [ABSTRACT FROM AUTHOR]

Accurate optic disc (OD) and optic cup (OC) segmentation play a critical role in automatic glaucoma diagnosis. In this paper, we present an automatic segmentation technique regarding the OD and the OC for glaucoma assessment. First, the robust adaptive approach for initializing the level set is designed to increase the performance of contour evolution. Afterwards, in order to handle the complex OD appearance affected by intensity inhomogeneity, pathological changes, and vessel occlusion, a novel model that integrates ample information of OD with the effective local intensity clustering (LIC) model together is presented. For the OC segmentation, to overcome the segmentation challenge as the OC's complex anatomy location, a novel preprocessing method based on structure prior information between the OD and the OC is designed to guide contour evolution in an effective region. Then, a novel implicit region based on modified data term using a richer form of local image clustering information at each point of interest gathered over a multiple-channel feature image space is presented, to enhance the robustness of the variations found in and around the OC region. The presented models symmetrically integrate the information at each point in a single-channel image from a multiple-channel feature image space. Thus, these models correlate with the concept of symmetry. The proposed models are tested on the publicly available DRISHTI-GS database and the experimental results demonstrate that the models outperform state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: Drug repositioning has caught the attention of scholars at home and abroad due to its effective reduction of the development cost and time of new drugs. However, existing drug repositioning methods that are based on computational analysis are limited by sparse data and classic fusion methods; thus, we use autoencoders and adaptive fusion methods to calculate drug repositioning. Results: In this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness. Specifically, a drug is repositioned by fusing drug-disease associations, drug target proteins, drug chemical structures and drug side effects. First, drug feature data integrated by drug target proteins and chemical structures were processed with dimension reduction via a deep autoencoder to characterize feature representations more densely and abstractly. Then, disease similarity was computed using drug-disease association data, while drug similarity was calculated with drug feature and drug-side effect data. Predictions of drug-disease associations were also calculated using a top-k neighbor method that is commonly used in predictive drug repositioning studies. Finally, a predicted matrix for drug-disease associations was acquired after fusing a wide variety of data via adaptive fusion. Based on experimental results, the proposed algorithm achieves a higher precision and recall rate than the DRCFFS, SLAMS and BADR algorithms with the same dataset. Conclusion: The proposed algorithm contributes to investigating the novel uses of drugs, as shown in a case study of Alzheimer's disease. Therefore, the proposed algorithm can provide an auxiliary effect for clinical trials of drug repositioning. [ABSTRACT FROM AUTHOR]

We studied the Ni–Cu-acid multifunctional synergism in NiCu-phyllosilicate catalysts toward 1,4-butynediol hydrogenation to 1,4-butanediol by varying the reduction temperature, which can activate different bimetal and support interactions. Compared with a monometallic Ni phyllosilicate (phy), which only showed one type of metal species when reduced at ∼750 °C, there are three types of metal species for the bimetallic Ni–Cu-phyllosilicate derived catalysts, namely Cuphy, differentiated Ni, and Niphy. Thorough structure–activity/selectivity correlation investigations showed that, although the Ni9Cu1-P catalyst matrix can produce tiny amounts of differentiated Ni0 species under the induction of reduced Cu0 at R250 condition, it could not form Ni–Cu bimetallic interactions for the collaborative hydrogenation of 1,4-butynediol, and the product stays in the semi hydrogenated state. When the reduction temperature is raised to 500 °C, stable Ni–Cu alloy active sites exist, accompanied by the strong metal support interaction and metal acid effect derived from the intimate contact between the extracted metal sites and the surviving functional phyllosilicate support; these functionalities yield a supreme hydrogenation performance of the R500 sample with a 1,4-butanediol yield larger than 91.2%. [ABSTRACT FROM AUTHOR]

Visual object tracking methods based on wireless multimedia sensor network is one of the research hotspots while the present linear method for processing feature vectors often lead to the tracking drift when tracking object with significant nonplanar pose variations through wireless sensor networks. In this article, we propose a novel nonlinear algorithm for tracking significant deformable objects. The proposed tracking scheme has two filters. On one hand, considering that Grassmann manifold is one of entropy manifold in Lie group manifold, which can describe and process the data of appearance feature more accurately, one filter is designed on it, to estimate the object appearance, by making full use of the transformation relationship between the point on manifold and its corresponding point on tangent space. On the other hand, considering that the process of objects imaging is essentially projection transformation process, the other filter is designed on projection transformation (SL(3)) group, describing the geometric deformation of the objects. The two filters execute alternatively to mitigate tracking drift. Extensive experiments prove that the proposed method can realize stable and accurate tracking for targets with significant geometric deformation, even obscured and illumination changes. [ABSTRACT FROM AUTHOR]

Study design: A single-institution, retrospective cohort study. Objective: To compare the accuracy and short-term clinical outcomes of pedicle screw placement between robot-assisted (RA) and freehand (FH) technique in the treatment of adult degenerative scoliosis (ADS). Methods: From February 2018 to October 2019, 97 adult patients with degenerative scoliosis admitted to our department were retrospectively reviewed. Thirty-one patients received robot-assisted pedicle screw placement (RA group), and 66 patients underwent freehand pedicle screw placement (FH group). Patient demographics and short-term clinical outcomes were recorded and compared between two groups. Gertzbein-Robbins grading system was adopted to evaluate the accuracy of pedicle screw placement by means of postoperative CT scan. Short-term clinical outcomes consist of operative time, intraoperative blood loss, length of hospital stay (LOS), radiological parameters, Scoliosis Research Society-22 (SRS-22) scores before the operation, 6 months after operation, adverse events, and revisions. Results: The accuracy of screw placement was higher than that of the FH group (clinically acceptable 98.7% vs. 92.2%; P< 0.001). Intraoperative blood loss of the RA group was less than those in the FH group (499 vs. 573 ml; P < 0.001). Operative time (283.1 vs. 291.9 min; P = 0.31) and length of stay (12.8 vs. 13.7 days; P = 0.36) were compared between RA and FH groups. In terms of radiological parameters, both of groups were improved postoperatively. The SRS-22 scores at 6 months after operation from both groups were better than those before operation. For surgery-related complication, one case had pressure sores in the RA group while two cases developed dural tears in the FH group. No revision was required in both groups. Conclusion: Combined with other surgical correction modalities, robot-assisted pedicle screw fixation is an effective and safe method of treating degenerative scoliosis. Due to its satisfactory surgical outcomes such as higher accuracy and less trauma, it provides a good alternative for clinical practice. Level of evidence: 3. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing. The application of image caption is extensive and significant, for example, the realization of human-computer interaction. This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper highlights some open challenges in the image caption task. [ABSTRACT FROM AUTHOR]

Deep learning methods, with their good performance in semantic representation of different images, have been widely used for saliency detection. Recent saliency detection methods have applied deep learning to obtain high-level features and combined them with hand-crafted low-level features to estimate saliency in the images. However, it is difficult to find the relationship between high-level and low-level features, resulting in incomplete integration framework for saliency detection. In this paper, we novely propose a saliency detection model by integrating high-level and low-level features with joint probability estimation. Firstly, the high-level features from FCN-8S network are used to estimate the probability of each superpixel as foreground or background region. Secondly, low-level features are extracted from each superpixels and clustered via affinity propagation (AP) clustering. The distributions of vectors from different clusters are consequently utilized to calculate the conditional probability of each superpixel as salient object under different assumptions. Thirdly, the joint probability of each superpixel as salient object in foreground or background is computed to compose the saliency map of the whole image. To further improve the uniformity of saliency in the same object region, the structured random forest (SRF) method is used to detect the contour of the image and the saliency of superpixels in homogeneous regions are uniformly merged. The advantage of high-level features in representing semantic regions and that of low-level features in differentiating local details in the image are unified and restrained by the joint probability estimation in the proposed model. Experimental results demonstrate that the proposed method provide better saliency detection performance than the state-of-the-art methods on 5 public databases. [ABSTRACT FROM AUTHOR]

One major branch of bottom-up salient object detection methods is machine learning-based methods which learn to classify salient object(positive) and background(negative) based on various learning algorithms. Generally, each input image corresponds to a training set which is determined by prior knowledge. However, we find that the class-imbalance problem (i.e., positive and negative data are seriously imbalanced in quantity) is inevitable for these methods due to the existence of various factors. Imbalanced training set might fail to make learning process succeed. To solve above problem, we propose a novel bottom-up Salient object detection algorithm based on Class-Imbalance Learning(CILS). For the input image, our goal is to ensure that a robust saliency classifier is well learned even if class-imbalance problem occurs in training set. To this end, we propose a novel over-sampling strategy which concentrates on constructing synthetic samples to make two classes be balanced. As a result, the balanced training data could be better applied to subsequent learning and classification process. Finally, the saliency map obtained by CILS is further refined by a novel optimization framework via foreground consistency and background consistency. Our proposed algorithm and other state-of-the-art algorithms are tested on three datasets. Results demonstrate adequately that our model produces better performance than other compared algorithms. Furthermore, we apply our salient object detection algorithm to underwater object recognition task and recognition accuracy could be further improved by introducing saliency cues. [ABSTRACT FROM AUTHOR]

The industrial Internet connects equipment to the network and utilizes the data generated to assist businesses. Industrial big data is the result of data accumulation; thus, the industrial Internet has to adopt new technologies—namely, software-defined industrial networks (SDIN) —to keep up with these developments. This study suggests a deep differential privacy data protection algorithm based on SDIN. The deep learning model is analyzed and integrated with differential privacy to provide the process framework for the deep differential privacy data protection algorithm. The equivalent model of the generative adversarial network is used to allow the attacker to obtain the optimal fake samples. The balance between dataset availability and privacy protection is achieved by implementing parameter tuning on the deep differential privacy model. The experimental results show that the proposed algorithm has strong industrial data privacy protection and high data availability and can effectively guarantee the privacy security of industrial data. [ABSTRACT FROM AUTHOR]

NiLa intermetallic NPs inside a nanoreactor can lower the activation energy of C 2 H 6 and CO 2 , which facilitates the transport/conversion of C 2 *, then significantly improves the dry reforming selectivity of ethane C-C bond breaking over Ni-La@Si catalysts. [Display omitted] • Dry reforming of ethane to syngas is a novel route to co-utilize CO 2 and unconventional gas to get hydrogen energy. • Spatial-chemical bifunctional confinement strategy was offered by Ni-La intermetallic sites within nanocapsule material. • The nanoreactor raised an outstanding catalytic performance in DRE which is the most stable ever reported. • C 2 * is more likely to polymerize than C 1 *, but can be curbed by functional Ni-La nanoreactors. • Physical confinement from the nanocapsule framework plays a more leading role in the anti-coking formation. CO 2 reforming of light alkanes from unconventional gas resources (shale gas/coalbed gas) presents an attractive route to achieve CO 2 utilization and valuable chemical production. However, it remains challenging in both high stability and product selectivity due to the inevitable strong competition between dry reforming and oxidative dehydrogenation pathways. We report here a La modified Ni@SiO 2 nanocapsule as the thermal-catalyst for CO 2 reforming of ethane, achieving complete ethane conversion and remarkable CO 2 conversion of 86.5 %, with stability for more than 200 h experiencing no carbon deposition. Detailed kinetic study, in situ characterizations and DFT calculation results revealed that the formation of NiLa intermetallic sites improved the adsorption of active oxygen species and activation of C 2 H 6 , which stabilized the key intermediate CH 3 CH 2 O* as well as the subsequent C 2 * cracking to CH x (O)*, leading to high selectivity toward syngas. Meanwhile, the removal of carbon deposits on Ni-La 2 O 3 interfaces are faster than Ni surface. The synergism of spatial confinement structure provided by the nanocapsule with sufficient mechanical strength together with the chemical Ni-La 2 O 3 interface of the enwrapped Ni-La mixed metal (oxide) NPs can timely eliminate C 2 * intermediates, which results in the most effective anti-coking ability during ethane reforming. This work highlights a tangible process towards unconventional (C 2+) gas utilization with fine-tunable nanoreactor catalysts. [ABSTRACT FROM AUTHOR]

6DoF pose estimation has received much attention in recent years. A key challenge is the difficulty of estimating object pose when the target texture is weak. In this work, we present the cross-modal Transformer (CMT-6D), a Transformer-based network suitable for highly accurate workpiece-level object 6D pose estimation from a single RGBD image. Our main insight is to make the surface texture information of RGB images with the geometric feature information of point clouds complement each other through a cross-modal Transformer, enabling accurate estimation of the pose of weakly textured targets. Specifically, the whole framework consists of two parallel Transformer branches, named Point Transformer and Image Transformer. Both parallel transformer networks use a pyramid structured encoder and a multi-layer perceptron structured decoder to extract geometric features of point clouds and texture features of RGB images, respectively. Then, a cross-modal key query strategy is proposed for information exchange between parallel channels. In addition, at the output representation stage, we design a simple and effective 3D keypoint selection algorithm to solve the problem that keypoints are likely to appear in the non-significant region. Finally, to improve the accuracy of attitude estimation and meet real-time requirements, a lightweight pose iterative network based on target feature regression is proposed to correct the initial attitude estimation error. Extensive experiments demonstrate the effectiveness and superiority of our method on LineMOD, Occlusion LineMOD, T-Less, and YCB-Video datasets. We demonstrate that our method can improve the 6D pose estimation performance by comparing with the state-of-the-art. Ablation research and visualization validate the design of CMT-6D. [ABSTRACT FROM AUTHOR]

Study Design: Sequencing and experimental analysis of the expression profile of circular RNAs (circRNAs) in hypertrophic ligamentum flavum (LFH).Objectives: The aim of this study was to identify differentially expressed circRNAs between LFH and nonhypertrophic ligamentum flavum tissues from lumbar spinal stenosis (LSS) patients.Summary Of Background Data: Hypertrophy of the ligamentum flavum (LF) can cause LSS. circRNAs are important in various diseases. However, no circRNA expression patterns related to LF hypertrophy have been reported.Methods: A total of 33 patients with LSS participated in this study. LF tissue samples were obtained when patients underwent decompressive laminectomy during surgery. The expression profile of circRNAs was analyzed by transcriptome high-throughput sequencing and validated with quantitative real-time polymerase chain reaction (PCR). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed for the differentially expressed circRNA-associated genes and related pathways. The connections between circRNAs and microRNAs were explored using Cytoscape. The role of hsa_circ_0052318 on LF cell fibrosis was assessed by analyzing the expression of collagen I and collagen III.Results: The results showed that 2439 circRNAs of 4025 were differentially expressed between the LFH and nonhypertrophic ligamentum flavum tissues, including 1276 upregulated and 1163 downregulated circRNAs. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that these differentially expressed circRNAs functioned in biological processes, cellular components, and molecular functions. Autophagy and mammalian target of rapamycin were the top two signaling pathways affected by these circRNAs. Five circRNAs (hsa_circ_0021604, hsa_circ_0025489, hsa_circ_0002599, hsa_circ_0052318, and hsa_circ_0003609) were confirmed by quantitative real-time PCR. The network indicated a strong relationship between circRNAs and miRNAs. Furthermore, hsa_circ_0052318 overexpression decreased mRNA and protein expression of collagen I and III in LF cells from LFH tissues.Conclusion: This study identified circRNA expression profiles characteristic of hypertrophied LF in LSS patients, and demonstrated that hsa_circ_0052318 may play an important role in the pathogenesis of LF hypertrophy.Level of Evidence: N/A. [ABSTRACT FROM AUTHOR]

Recently, single image super-resolution (SISR) has been widely applied in the fields of multimedia and computer vision communities and obtained remarkable performance. However, most current methods ignore to utilize multi-granularity features of low-resolution (LR) image to further improve the SISR performance. And the channel and spatial features obtained from original LR images are treated equally, resulting in unnecessary computations for abundant uninformative features, thereby hindering the representational ability of super-resolution (SR) models. In this paper, we present a novel Multi-Granularity Pyramid Attention Network (MGPAN) which fully exploits the multi-granularity perception and attention mechanisms to improve the quality of reconstructed images. We design a multi-branch dilated convolution layer with varied kernels corresponding to receptive fields of different sizes to modulate multi-granularity features for adaptively capturing more important information. Moreover, a novel spatial pyramid pooling attention (SPPA) module is constructed to integrate the channel-wise and multi-scale spatial information, which is beneficial to compute the response values from the multi-scale regions of each neuron, and then establish the accurate mapping from low to high dimensional solution space. Besides, for long-short-term information preservation and information flow enhancement, we adopt the short, long, and global skip connection structures to concatenate and fuse the states of each module, which can improve the SR network performance effectively. Extensive experiments on several standard benchmark datasets show that the proposed MGPAN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements. [ABSTRACT FROM AUTHOR]

Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection methods. However, these methods still could not provide convincing results because the nodules are easily confused with calcifications, vessels, or other benign lumps. In this paper, we propose a novel deep convolutional neural network (DCNN) framework for detecting pulmonary nodules in the chest CT image. The framework consists of three cascaded networks: First, a U-net network integrating inception structure and dense skip connection is proposed to segment the region of lung parenchyma from the chest CT image. The inception structure is used to replace the first convolution layer for better feature extraction with respect to multiple receptive fields, while the dense skip connection could reuse these features and transfer them through the network. Secondly, a modified U-net network where all the convolution layers are replaced by dilated convolution is proposed to detect the "suspicious nodules" in the image. The dilated convolution can increase the receptive fields to improve the ability of the network in learning global information of the image. Thirdly, a modified U-net adapting multi-scale pooling and multi-resolution convolution connection is proposed to find the true pulmonary nodule in the image with multiple candidate regions. During the detection, the result of the former step is used as the input of the latter step to follow the "coarse-to-fine" detection process. Moreover, the focal loss, perceptual loss and dice loss were used together to replace the cross-entropy loss to solve the problem of imbalance distribution of positive and negative samples. We apply our method on two public datasets to evaluate its ability in pulmonary nodule detection. Experimental results illustrate that the proposed method outperform the state-of-the-art methods with respect to accuracy, sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) imaging technology has been widely used to assist medical diagnosis in recent years. However, noise during the process of imaging, and data compression during the process of storage and transmission always interrupt the image quality, resulting in unreliable performance of the post-processing steps in the computer assisted diagnosis system (CADs), such as medical image segmentation, feature extraction, and medical image classification. Since the degradation of medical images typically appears as noise and low-resolution blurring, in this paper, we propose a uniform deep convolutional neural network (DCNN) framework to handle the de-noising and super-resolution of the CT image at the same time. The framework consists of two steps: Firstly, a dense-inception network integrating an inception structure and dense skip connection is proposed to estimate the noise level. The inception structure is used to extract the noise and blurring features with respect to multiple receptive fields, while the dense skip connection can reuse those extracted features and transfer them across the network. Secondly, a modified residual-dense network combined with joint loss is proposed to reconstruct the high-resolution image with low noise. The inception block is applied on each skip connection of the dense-residual network so that the structure features of the image are transferred through the network more than the noise and blurring features. Moreover, both the perceptual loss and the mean square error (MSE) loss are used to restrain the network, leading to better performance in the reconstruction of image edges and details. Our proposed network integrates the degradation estimation, noise removal, and image super-resolution in one uniform framework to enhance medical image quality. We apply our method to the Cancer Imaging Archive (TCIA) public dataset to evaluate its ability in medical image quality enhancement. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on de-noising and super-resolution by providing higher peak signal to noise ratio (PSNR) and structure similarity index (SSIM) values. [ABSTRACT FROM AUTHOR]

Objective: Accumulating data show that dysregulation of long noncoding RNAs (lncRNAs) acts a critical role in a variety of malignancies. Among these lncRNAs, small nucleolar RNA host genes (SNHGs) are associated with tumour growth and progression. But, the molecular mechanisms by which SNHG4 contributes to osteosarcoma remain undocumented. Methods: The association between lncRNA SNHG4 expression and clinicopathologic characteristics and prognosis in patients with osteosarcoma was analysed by TCGA RNA‐sequencing data. Cell viability and colony formation abilities were respectively assessed by MTT and colony formation assays. LncRNA SNHG4‐specific binding with miR‐224‐3p was verified by bioinformatic analysis, luciferase gene report, and RNA immunoprecipitation assays. Regulation relationship between SNHG4 and miR‐224‐3p expression was further evaluated by the rescue experiments. Results: The expression level of lncRNA SNHG4 was significantly elevated in osteosarcoma samples and cell lines as compared with the adjacent normal tissues, and SNHG4 high expression was associated with tumour size (TS) and poor prognosis in patients with osteosarcoma. Knockdown of SNHG4 suppressed cell viability and invasive potential, whereas ectopic SNHG4 expression displayed the opposite effects. Moreover, we found that lncRNA SNHG4 acted as a sponge of miR‐224‐3p, and miR‐224‐3p mimic reversed SNHG4 induced tumour‐promoting effects in osteosarcoma cells. The expression of miR‐224‐3p depicted a negative correlation with SNHG4 in osteosarcoma samples and miR‐224‐3p low expression was associated with TS and poor survival in patients with osteosarcoma. Conclusion: Our findings demonstrated that LncRNA SNHG4 promoted tumour growth by sponging miR‐224‐3p and represented a poor prognostic factor in patients with osteosarcoma. [ABSTRACT FROM AUTHOR]

We propose an image-based face swapping algorithm, which can be used to replace the face in the reference image with the same facial shape and features as the input face. First, a face alignment is made based on a group of detected facial landmarks, so that the aligned input face and the reference face are consistent in size and posture. Secondly, an image warping algorithm based on triangulation is presented to adjust the reference face and its background according to the aligned input faces. In order to achieve more accurate face swapping, a face parsing algorithm is introduced to realize the accurate detection of the face-ROIs, and then the face-ROI in the reference image is replaced with the input face-ROI. Finally, a Poisson image editing algorithm is adopted to realize the boundary processing and color correction between the replacement region and the original background, and then the final face swapping result is obtained. In the experiments, we compare our method with other face swapping algorithms and make a qualitative and quantitative analysis to evaluate the reality and the fidelity of the replaced face. The analysis results show that our method has some advantages in the overall performance of swapping effect. [ABSTRACT FROM AUTHOR]

The optic disc is a key anatomical structure in retinal images. The ability to detect optic discs in retinal images plays an important role in automated screening systems. Inspired by the fact that humans can find optic discs in retinal images by observing some local features, we propose a local feature spectrum analysis (LFSA) that eliminates the influence caused by the variable spatial positions of local features. In LFSA, a dictionary of local features is used to reconstruct new optic disc candidate images, and the utilization frequencies of every atom in the dictionary are considered as a type of “spectrum” that can be used for classification. We also employ the sparse dictionary selection approach to construct a compact and representative dictionary. Unlike previous approaches, LFSA does not require the segmentation of vessels, and its method of considering the varying information in the retinal images is both simple and robust, making it well-suited for automated screening systems. Experimental results on the largest publicly available dataset indicate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

• Blind reconstruction of lung CT image suffers from degradations with unknown models. • Reconstruction and degradation estimation can be obtained in one unified framework. • Residual self-attention module improves learning noise information from CT images. • Using transformer cyclically achieves super-resolution and blur estimation meantime. Background and objective: Computer tomography (CT) imaging technology has played significant roles in the diagnosis and treatment of various lung diseases, but the degradations in CT images usually cause the loss of detailed structural information and interrupt the judgement from clinicians. Therefore, reconstructing noise-free, high-resolution CT images with sharp details from degraded ones is of great importance for the computer-assisted diagnosis (CAD) system. However, current image reconstruction methods suffer from unknown parameters of multiple degradations in actual clinical images. Methods: To solve these problems, we propose a unified framework, so called Posterior Information Learning Network (PILN), for blind reconstruction of lung CT images. The framework consists of two stages: Firstly, a noise level learning (NLL) network is proposed to quantify the Gaussian and artifact noise degradations into different levels. Inception-residual modules are designed to extract multi-scale deep features from the noisy image, and residual self-attention structures are proposed to refine deep features to essential representations of noise. Secondly, by taking the estimated noise levels as prior information, a cyclic collaborative super-resolution (CyCoSR) network is proposed to iteratively reconstruct the high-resolution CT image and estimate the blur kernel. Two convolutional modules are designed based on cross-attention transformer structure, named as Reconstructor and Parser. The high-resolution image is restored from the degraded image by the Reconstructor under the guidance of the predicted blur kernel, while the blur kernel is estimated by the Parser according to the reconstructed image and the degraded one. The NLL and CyCoSR networks are formulated as an end-to-end framework to handle multiple degradations simultaneously. Results: The proposed PILN is applied to the Cancer Imaging Archive (TCIA) dataset and the Lung Nodule Analysis 2016 Challenge (LUNA16) dataset to evaluate its ability in reconstructing lung CT images. Compared with the state-of-the-art image reconstruction algorithms, it can provide high-resolution images with less noise and sharper details with respect to quantitative benchmarks. Conclusions: Extensive experimental results demonstrate that our proposed PILN can achieve better performance on blind reconstruction of lung CT images, providing noise-free, detail-sharp and high-resolution images without knowing the parameters of multiple degradation sources. [ABSTRACT FROM AUTHOR]

Hallucinating a photo-realistic frontal face image from a low-resolution (LR) non-frontal face image is beneficial for a series of face-related applications. However, previous efforts either focus on super-resolving high-resolution (HR) face images from nearly frontal LR counterparts or frontalizing non-frontal HR faces. It is necessary to address all these challenges jointly for real-world face images in unconstrained environment. In this paper, we develop a novel Cross-view Information Interaction and Feedback Network (CVIFNet), which simultaneously handles the non-frontal LR face image super-resolution (SR) and frontalization in a unified framework and interacts them with each other to further improve their performance. Specifically, the CVIFNet is composed of two feedback sub-networks for frontal and profile face images. Considering the reliable correspondence between frontal and non-frontal face images can be crucial and contribute to face hallucination in a different manner, we design a cross-view information interaction module (CVIM) to aggregate HR representations of different views produced by the SR and frontalization processes to generate finer face hallucination results. Besides, since 3D rendered facial priors contain rich hierarchical features, such as low-level (e.g., sharp edge and illumination) and perception level (e.g., identity) information, we design an identity-preserving consistency loss based on 3D rendered facial priors, which can ensure that the high-frequency details of frontal face hallucination result are consistent with the profile. Extensive experiments demonstrate the effectiveness and advancement of CVIFNet. • A novel unified framework, named Cross-view Information Interaction and Feedback Network, is proposed to jointly tackle face SR and face frontalization problems. • A novel feedback mechanism is proposed to provide the high-level reconstruction information in top-down feedback flows through feedback connections. • A novel 3D rendered facial priors based identity-preserving consistency loss is proposed to supervise the reconstruction of facial components spatial information. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images. • We propose a multidimensional input to reflect the GGOs or infiltration features. • We propose a directional convolution block to represent the fibrotic-streak features. • We propose a novel region contour loss function to restric irregular boundaries. [ABSTRACT FROM AUTHOR]

Recently, deep-learning based face super-resolution methods have succeeded in hallucinating high-resolution face images from low-resolution inputs. However, since the given face images have tiny resolution and arbitrary characteristics which need to be reconstructed at high magnification factors, existing methods still suffer from large space of possible mapping functions, resulting in the limited performance in producing sharp textures. In this paper, we propose a novel CNN-based Dual Closed-loop Network (DCLNet) to minimize the possible mapping space. To that end, we design two dual learning networks to form the dual closed-loop structure with a primary face super-resolution network, which can provide the primary branch with additional prior constraint to guide the essential facial features restoring. Our work represents the first attempt to introduce multiple dual learning networks into face super-resolution model to constrain the possible mapping space. In addition, a progressive facial prior estimation framework and a new prior-guided feature enhancement module are presented to integrate the facial prior knowledge and guide the face image super-resolution. By generating multiple facial components maps for the activation of essential facial parts, our enhancement module can address the difficulty in learning and integrating strong priors into a face super-resolution model. In this way, the collaboration between the face super-resolution and alignment processes can be enhanced with effect. Extensive experiments are implemented on CelebA and Helen dataset, showing that our proposed method can provide state-of-the-art or even better performance in both quantitative and qualitative measurements. [ABSTRACT FROM AUTHOR]

Recently, single image super-resolution (SISR) has been widely applied in the fields of underwater robot vision and obtained remarkable performance. However, most current methods generally suffered from the problem of a heavy burden on computational resources with large model sizes, which limited their real-world underwater robotic applications. In this paper, we introduce and tackle the super resolution (SR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present a novel lightweight multi-stage information distillation network, named MSIDN, for better balancing performance against applicability, which aggregates the local distilled features from different stages for more powerful feature representation. Moreover, a novel recursive residual feature distillation (RRFD) module is constructed to progressively extract useful features with a modest number of parameters in each stage. We also propose a channel interaction & distillation (CI&D) module that employs channel split operation on the preceding features to produce two-part features and utilizes the inter channel-wise interaction information between them to generate the distilled features, which can effectively extract the useful information of current stage without extra parameters. Besides, we present USR-2K dataset, a collection of over 1.6K samples for large-scale underwater image SR training, and a testset with an additional 400 samples for benchmark evaluation. Extensive experiments on several standard benchmark datasets show that the proposed MSIDN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements. • A information distillation network is proposed for underwater image super-resolution. • A recursive residual module is constructed for informative features distillation. • A channel-wise interaction mechanism is proposed to generate distilled features. • A novel underwater image super-resolution dataset is presented. [ABSTRACT FROM AUTHOR]

Following the development of wireless multimedia sensor networks (WMSN), the coverage of the sensors in the network constitutes one of the key technologies that have a significant influence on the monitoring ability, quality of service, and network lifetime. The application environment of WMSN is always a complex surface, such as a hilly surface, that would likely cause monitoring shadowing problems. In this study, a new coverage-enhancing algorithm is presented to achieve an optimal coverage ratio of WMSN based on three-dimensional (3D) complex surfaces. By aiming at the complex surface, the use of a 3D sensing model, including a sensor monitoring model and a surface map calculation algorithm, is proposed to calculate the WMSN coverage information in an accurate manner. The coverage base map allowed the efficient estimation of the degree of monitoring occlusion efficiently and improved the system's accuracy. To meet the requests of complex 3D surface monitoring tasks for multiple sensors, we propose a modified cuckoo search algorithm that considers the features of the WMSN coverage problem and combines the survival of the fittest, dynamic discovery probability, and the self-adaptation strategy of rotation. The evaluation outcomes demonstrate that the proposed algorithm can describe the 3D covering field but also improve both the coverage quality and efficiency of the WMSN on a complex surface. [ABSTRACT FROM AUTHOR]

Feature selection, which aims to select an optimal feature subset to avoid the “curse of dimensionality,” is an important research topic in many real-world applications. To select informative features from a high-dimensional dataset, we propose a novel unsupervised feature selection algorithm called Double Regularized Matrix Factorization Feature Selection (DRMFFS) in this paper. DRMFFS is based on the feature selection framework of matrix factorization, but extends this framework by introducing double regularizations (i.e., graph regularization and inner product regularization). There are three major contributions to our approach. First, for the sake of preserving the useful underlying geometric structure information of the feature space of the data, we introduce the graph regularization to guide the learning of the feature selection matrix, making it more effective. Second, in order to take into account the correlations among features, an inner product regularization term is imposed on the objective function of matrix factorization. Therefore, the selected features by DRMFFS cannot only represent the original high-dimensional data well but also contain low redundancy. Third, we design an efficient iteratively update algorithm to solve our approach and also prove its convergence. Experiments on six benchmark databases demonstrate that the proposed approach outperforms the state-of-the-art approaches in terms of both the classification and clustering performance. [ABSTRACT FROM AUTHOR]