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In the platform economy era, e‐commerce platforms function as distribution channels for sellers and offer online loans to sellers as financing providers. This study focuses on platform finance, where the platform sets a credit line or interest rate to offer either a limited or unlimited loan to capital‐constrained sellers. The sellers may have sufficient initial capital or be capital‐constrained but have access to limited or unlimited loans. We found that the optimal sellers' channel strategy, choosing either a single channel or online‐dominant or offline‐dominant dual channels, depends on the platform's credit line, consumers' channel preferences, and their switching behavior between online and offline channels. Given an unlimited (or limited) loan, the seller's dominated channel's quantity decreases (or increases) with her initial capital. The seller's and platform's preference for the unlimited loan or limited loan depends on the dominant channel and the seller's capital constraint. Loan offerings result in Pareto improvements for both participants, which reveals the value platform finance brings. We compare platform finance with bank finance and find that platform finance increases the sellers' threshold quantity without stockout in each channel and encourages the seller to place a higher quantity in both channels. We also examine the channel substitution effect on the sellers' quantity decisions. Findings provide insights for platforms and sellers to implement optimal financing and channel structures to increase their profits. [ABSTRACT FROM AUTHOR]

This study aimed to explore the research trends of percutaneous endoscopic lumbar discectomy in treating lumbar disc herniation using bibliometrics over the past ten years.Methods: Relevant publications on the clinical application of percutaneous endoscopic lumbar discectomy in lumbar disc herniation were searched in the Web of Science Core Collection. Subsequently, the characteristics of all these articles were collected. Visualizing data of annual publications, journals, cited journals, authors, cited authors, countries, institutions, keywords, and cited references was performed by using CiteSpace (6.1.R6).Results: A total of 642 publications were extracted between 2013 and 2022. The number of publications peaked in the year 2020. The most prolific journal was World Neurosurgery (81), and Spine (597) as the cited journal was the most popular one. China (393) was the most prolific country, followed by South Korea (100). The institution with the most productivity was Tongji University (35). Yue Zhou (20) was the most prolific author, and Sebastian Ruetten (310) was the most cited author. The keyword "interlaminar" was top of research developments with the highest citation burst (8.69). "Lumbar disc herniation", "surgical technique", and "complication" were popular keywords. The surgical procedures and complications of percutaneous endoscopic lumbar discectomy have been the hot topics of recent research.Conclusion: This study summarized the current situation and development trends of percutaneous endoscopic lumbar discectomy clinical research in the form of visualization, and these findings may help researchers explore new directions in the future. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy, the most serious ocular complication of diabetes, imposes a serious economic burden on society. Automatic and objective assessment of vessel changes can effectively manage diabetic retinopathy and prevent blindness. Optical coherence tomography angiography (OCTA) metrics have been confirmed to be used to assess vessel changes. The accuracy and reliability of OCTA metrics are restricted by vessel segmentation methods. In this study, a multi-branch retinal vessel segmentation method is proposed, which is comparable to the segmentation results obtained from the manual segmentation, effectively extracting vessels in low contrast areas and improving the integrity of the extracted vessels. OCTA metrics based on the proposed segmentation method were validated to be reliable for further analysis of the relationship between OCTA metrics and diabetes and the severity of diabetic retinopathy. Changes in vessel morphology are influenced by systemic risk factors. However, there is a lack of analysis of the relationship between OCTA metrics and systemic risk factors. We conducted a cross-sectional study that included 362 eyes of 221 diabetic patients and 1,151 eyes of 587 healthy people. Eight systemic risk factors were confirmed to be closely related to diabetes. After controlling these systemic risk factors, significant OCTA metrics (such as vessel complexity index, vessel diameter index, and mean thickness of retinal nerve fiber layer centered in the macular) were found to be related to diabetic retinopathy and severe diabetic retinopathy. This study provides evidence to support the potential value of OCTA metrics as biomarkers of diabetic retinopathy. [ABSTRACT FROM AUTHOR]

Detecting signet ring cells on histopathologic images is a critical computer-aided diagnostic task that is highly relevant to cancer grading and patients' survival rates. However, the cells are densely distributed and exhibit diverse and complex visual patterns in the image, together with the commonly observed incomplete annotation issue, posing a significant barrier to accurate detection. In this article, we propose to mitigate the detection difficulty from a model reinforcement point of view. Specifically, we devise a Classification Reinforcement Detection Network (CRDet). It is featured by adding a dedicated Classification Reinforcement Branch (CRB) on top of the architecture of Cascade RCNN. The proposed CRB consists of a context pooling module to perform a more robust feature representation by fully making use of context information, and a feature enhancement classifier to generate a superior feature by leveraging the deconvolution and attention mechanism. With the enhanced feature, the small-sized cell can be better characterized and CRDet enjoys a more accurate signet ring cell identification. We validate our proposal on a large-scale real clinical signet ring cell data set. It is shown that CRDet outperforms several popular convolutional neural network-based object detection models on this particular task. [ABSTRACT FROM AUTHOR]

For the study of various retinal diseases, an accurate quantitative analysis of the retinal layer is essential for assessing the severity of the disease and diagnosing the progression of the disease. Optical coherence tomography (OCT) images can clearly show each layer of the retinal structure and detect subtle early lesions, thus providing a gold standard for the diagnosis of retinal diseases. In this article, we propose a coarse-to-fine retinal layer boundary segmentation method based on the embedded residual recurrent network (ERR-Net) and the graph search (GS). Considering the integrity of information transmission and the dependence of features, we first design a novel end-to-end residual recurrent network to roughly segment the retinal layer boundaries. The proposed ERR-Net not only solves the gradient problem brought by the depth but also fully captures the global spatial structure of the image. Second, we employ a GS algorithm for boundary continuous to make the layer boundary segmentation results more accurate. Finally, we evaluate the effectiveness of the proposed method on three publicly available datasets and compare it with the state-of-the-art methods on each dataset. The quantitative results and visual effects show that the proposed method outperforms the state-of-the-art approaches in the segmentation of retinal layer boundaries in OCT images. Moreover, the results also show that the proposed method has good algorithm stability on three datasets with different sizes, characteristics, and segmentation difficulties. [ABSTRACT FROM AUTHOR]

Iatrogenic radial nerve injures are a common complication during the placement of external fixator pins at the lateral aspect of the humeral shaft. This study uses a three-dimensional measurement technique to locate a safe entry point for humeral pins when externally fixating the elbow. Methods: We fixed a guide wire to the radial nerve by a suture string, and used computed tomography (CT) to scan the upper limbs of cadaver specimens. Then, we measured the deviation angles of the radial nerve on the CT scans, and the distance from the radial nerve to the "elbow rotation center" (ERC). Result: The average distance from the radial nerve to the ERC was 87.3 ± 8.5 mm (range: 68–100 mm), 58.3 ± 11.3 mm (range: 32.12–82.84 mm), 106.3 ± 5.8 mm (range: 86.93–115.08 mm), and 113.9 ± 4.8 mm (range: 97.93–120.22 mm) at radial nerve deviation angles of 0°, –30°, 30°, and 45°, respectively. The average radial nerve deviation angle was –37.7° ± 7.7° and 123.9° ± 19.9° at 50 and 150 mm, respectively. Relative to 0°, the distance between the radial nerve and the ERC at radial nerve deviation angles of –30°, 30°, and 45° showed a significant difference (t = 18.20, p < 0.05; Z = 6.07, p < 0.001; Z = 6.40, p < 0.001, respectively). Conclusions: Pins inserted into the proximal humerus should be about 150 mm from the ERC with a radial nerve deviation angle of 30° anteriorly, and 50 mm from the ERC with a deviation angle of 30°–45° posteriorly. [ABSTRACT FROM AUTHOR]

Weakly-supervised semantic segmentation with image-level labels has much significance as it facilitates related practical applications under lightweight manual annotations. Recent researches first infer the visual cues, referred as discriminative regions corresponding to each object category on images using deep convolutional classification networks. Then they expand visual cues to generate initial segmentation masks. Despite the remarkable progress, the segmentation performance still remains unsatisfactory due to the absence of complete visual cues. Using these low-quality visual cues as prior will have the limitation on improving segmentation performance. To overcome this problem, we propose a novel context propagation embedding network, i.e., the CPENet to generate high-quality visual cues, which focuses on learning semantic relationship between each region and its surrounding neighbors and selectively propagates discriminative information to non-discriminative, object related regions. Our methods can provide reliable initial segmentation masks for training subsequent segmentation network to generate final segmentation results. In addition, we refined convolutional block attention module (CBAM) [30] to hierarchically extract more category-aware features by capturing global context related information and further promote the propagation process. Experiments on benchmark demonstrate that our proposed method obtains superior performance over the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Lumbar decompressive surgery is the gold standard treatment for lumbar spinal stenosis. Minimally invasive surgical techniques have been introduced with the aim of reducing the morbidity associated with open surgery. The purpose of the present study was to systematically search the literature and perform a meta-analysis of studies comparing the outcomes between biportal endoscopic technique and microscopic technique for lumbar canal stenosis decompression. A comprehensive search of the PubMed, Google Scholar, Web of Science, Embase and the Cochrane Library databases was performed to identify relevant articles up to 15th of December 2019. Eligible studies were retrieved, data were extracted by two authors independently and risks of bias were assessed. A total of six studies involving 438 patients were selected for review. The results of the pooled analysis indicated similar operative times [mean difference (MD), -3.41; 95% CI, -10.78-3.96; P<0.36], similar complications (MD, 0.70; 95% CI, 0.33-1.46; P=0.34), similar visual analogue scale scores for back and leg pain at the time of the final follow-up and similar Oswestry disability indexes (MD, -0.28; 95% CI, -1.25-0.69; P=0.58) for the two procedures. In conclusion, biportal endoscopic technique is a viable alternative to microscopic technique for lumbar canal stenosis decompression with similar operative time, clinical outcomes and complications. [ABSTRACT FROM AUTHOR]

Fine-grained object classification (FGOC) is a challenging research topic in multimedia computing with machine learning, which faces two pivotal conundrums: focusing attention on the discriminate part regions, and then processing recognition with the part-based features. Existing approaches generally adopt a unidirectional two-step structure, that first locate the discriminate parts and then recognize the part-based features. However, they neglect the truth that part localization and feature recognition can be reinforced in a bidirectional process. In this paper, we propose a novel bidirectional attention-recognition model (BARM) to actualize the bidirectional reinforcement for FGOC. The proposed BARM consists of one attention agent for discriminate part regions proposing and one recognition agent for feature extraction and recognition. Meanwhile, a feedback flow is creatively established to optimize the attention agent directly by recognition agent. Therefore, in BARM the attention agent and the recognition agent can reinforce each other in a bidirectional way and the overall framework can be trained end-to-end without neither object nor parts annotations. Moreover, a novel Multiple Random Erasing data augmentation is proposed, and it exhibits impressive pertinency and superiority for FGOC. Conducted on several extensive FGOC benchmarks, BARM outperforms the present state-of-the-art methods in classification accuracy. Furthermore, BARM exhibits a clear interpretability and keeps consistent with the human perception in visualization experiments. [ABSTRACT FROM AUTHOR]

• An efficient and effective model is proposed for multi-scale scene text detection. • A grouped pyramid module is used to group basic layers for more accurate detection. • The pyramid module includes a top-down partition and a bottom-up group. • PyrBoxes achieves the state-of-the-art performance with a significantly faster speed. • The generality of PyrBoxes is verified by a newly constructed Chinese text dataset. Scene text detection has attracted many researches due to its importance to various applications. However, current approaches could not keep a good balance between accuracy and speed, i.e., a high-performance accuracy but with a low processing speed, or vice-versa. In this paper, we propose a novel model, named PyrBoxes, for efficient and effective multi-scale scene text detection. PyrBoxes consists of an SSD-based backbone that utilizes deep layers with strong semantics to detect texts in various sizes, and a proposed grouped pyramid module that leverages basic layers to append detailed locations into detection. Most existing detectors discard features from the basic layers due to the efficiency issue. We argue these layers contain fine-grained information, which is complementary to high-level semantics. Based on this, the grouped pyramid module combines the basic layers recursively into a detection layer via a top-down partition and a bottom-up group. Extensive experiments on both horizontal and oriented benchmarks, including ICDAR2013 Focused Scene Text, ICDAR2015 Incidental Text and COCO-Text, demonstrate that PyrBoxes achieves state-of-the-art or highly competitive performance compared with baselines, while runs significantly faster at inference. Furthermore, by experimenting on another ChiTVText dataset, PyrBoxes shows great generality to Chinese and long text lines. By visualizing some qualitative results, as expected, PyrBoxes provides more accurate locations and reduces the rate of missed detections, especially for small-sized texts. [ABSTRACT FROM AUTHOR]

As binary code is storage efficient and fast to compute, it has become a trend to compact real-valued data to binary codes for the nearest neighbors (NN) search in a large-scale database. However, the use of binary code for the NN search leads to low retrieval accuracy. To increase the discriminability of the binary codes of existing hash functions, in this paper, we propose a framework of double-bit quantization and index hashing for an effective NN search. The main contributions of our framework are: first, a novel double-bit quantization (DBQ) is designed to assign more bits to each dimension for higher retrieval accuracy; second, a double-bit index hashing (DBIH) is presented to efficiently index binary codes generated by DBQ; and third, a weighted distance measurement for DBQ binary codes is put forward to re-rank the search results from DBIH. The empirical results on three benchmark databases demonstrate the superiority of our framework over existing approaches in terms of both retrieval accuracy and query efficiency. Specifically, we observe an absolute improvement on precision of 10%–25% in most cases and the query speed increases over 30 times compared to traditional binary embedding methods and linear scan, respectively. [ABSTRACT FROM AUTHOR]

In recent years, block compressed sampling (BCS) has emerged as a considerable attractive sampling technology for image acquisition. However, the general BCS approaches ignore the information distribution in the same image sub-block, and may lead to unfair allocation of sampling resources. In this paper, we propose a novel compressed sampling scheme by employing the idea of adaptive partition. In the proposed scheme, images are adaptively partitioned based on their saliency information through clustering, and pixels with similar saliency are gathered in the same sub-blocks. Sampling rates for those blocks, in turn, are computed on the basis of their saliency values, respectively. Therefore the sampling resources are allocated with fairer and more equitable sharing by all sub-blocks. Experimental results show that the proposed scheme has better visual effect and obtains higher image reconstruction accuracy than existing ones. [ABSTRACT FROM AUTHOR]

Uyghur text detection is crucial to a variety of real-world applications, while little researches put their attention on it. In this paper, we develop an effective and efficient region-based convolutional neural network for Uyghur text detection in complex background images. The characteristics of the network include: (1) Three region proposal networks are used to improve the recall, which simultaneously utilize feature maps from different convolutional layers. (2) The overall architecture of our network is in the form of fully convolutional network, and global average pooling is applied to replace the fully connected layers in the classification and bounding box regression layers. (3) To fully utilize the baseline information, Uyghur text lines are detected directly by the network in an end-to-end fashion. Experiment results on benchmark dataset show that our method achieves an F-measure of 0.83 and detection time of 0.6 s for each image in a single K20c GPU, which is much faster than the state-of-the-art methods while keeps competitive accuracy. [ABSTRACT FROM AUTHOR]

The Internet has extended the physical boundary of people's social circles to manage an inordinate number of online friends. It is recognized that only a fraction of these online friends are also known with each other in offline circumstances, i.e., the offline friends. An important type of offline friend, onsite offline friend, is defined and addressed in this paper. We explores the possibility of utilizing users’ online photo sharing-related behaviors and network topologies to analyze and distinguish between online and onsite offline friendships. Different from traditional social science studies which rely on survey-based data, we employ users’ tagged people on the shared Instagram photos as the ground-truth for onsite offline friends. This enables a large-scale and objective analysis and experimental evaluation, which compares between different factors and identifies the features that are key to onsite offline friend identification. [ABSTRACT FROM PUBLISHER]

The proliferation of mobile devices is producing a new wave of mobile visual search applications that enable users to sense their surroundings with smart phones. As the particular challenges of mobile visual search, achieving high recognition bitrate becomes the consistent target of existed related works. In this paper, we explore to holistically exploit the deep learning-based hashing methods for more robust and instant mobile visual search. Firstly, we present a comprehensive survey of the existed deep learning based hashing methods, which showcases their remarkable power of automatic learning highly robust and compact binary code representation for visual search. Furthermore, in order to implement the deep learning hashing on computation and memory constrained mobile device, we investigate the deep learning optimization works to accelerate the computation and reduce the model size. Finally, we demonstrate a case study of deep learning hashing based mobile visual search system. The evaluations show that the proposed system can significantly improve 70% accuracy in MAP than traditional methods, and only needs less than one second computation time on the ordinary mobile phone. Finally, with the comprehensive study, we discuss the open issues and future research directions of deep learning hashing for mobile visual search. [ABSTRACT FROM AUTHOR]