Generating Text Sequence Images for Recognition

Recently, methods based on deep learning have dominated the field of text recognition. With a large number of training data, most of them can achieve the state-of-the-art performances. However, it is hard to harvest and label sufficient text sequence images from the real scenes. To mitigate this issue, several methods to synthesize text sequence images were proposed, yet they usually need complicated preceding or follow-up steps. In this work, we present a method which is able to generate infinite training data without any auxiliary pre/post-process. We tackle the generation task as an image-to-image translation one and utilize conditional adversarial networks to produce realistic text sequence images in the light of the semantic ones. Some evaluation metrics are involved to assess our method and the results demonstrate that the caliber of the data is satisfactory. The code and dataset will be publicly available soon

Unified Chinese License Plate Detection and Recognition with High Efficiency

Recently, deep learning-based methods have reached an excellent performance on License Plate (LP) detection and recognition tasks. However, it is still challenging to build a robust model for Chinese LPs since there are not enough large and representative datasets. In this work, we propose a new dataset named Chinese Road Plate Dataset (CRPD) that contains multi-objective Chinese LP images as a supplement to the existing public benchmarks. The images are mainly captured with electronic monitoring systems with detailed annotations. To our knowledge, CRPD is the largest public multi-objective Chinese LP dataset with annotations of vertices. With CRPD, a unified detection and recognition network with high efficiency is presented as the baseline. The network is end-to-end trainable with totally real-time inference efficiency (30 fps with 640p). The experiments on several public benchmarks demonstrate that our method has reached competitive performance. The code and dataset will be publicly available at https://github.com/yxgong0/CRPD

Focus-Enhanced Scene Text Recognition with Deformable Convolutions

Recently, scene text recognition methods based on deep learning have sprung up in computer vision area. The existing methods achieved great performances, but the recognition of irregular text is still challenging due to the various shapes and distorted patterns. Consider that at the time of reading words in the real world, normally we will not rectify it in our mind but adjust our focus and visual fields. Similarly, through utilizing deformable convolutional layers whose geometric structures are adjustable, we present an enhanced recognition network without the steps of rectification to deal with irregular text in this work. A number of experiments have been applied, where the results on public benchmarks demonstrate the effectiveness of our proposed components and shows that our method has reached satisfactory performances. The code will be publicly available at https://github.com/Alpaca07/dtr soon

Facile preparation of eco-friendly, flexible starch-based materials with ionic conductivity and strain-responsiveness

This work demonstrates a facile and “green” method to prepare eco-friendly, flexible, transparent, and ionically conductive starch-based materials, which have great potential for personal health-monitoring applications such as disposable electrodes. This method relies on the use of the CaCl2 solution and enables both the efficient disorganization and amorphization of high-amylose starch granules with low energy consumption and the reinforcement of the starch chain network by starch–metal cation complexation. Specifically, the method involves a simple mixing of a high-amylose starch with the CaCl2 solution followed by heating the mixture at 80 °C for 5 min. The whole process is completely environmentally benign, without any waste liquid or bioproducts generated. These resulting materials displayed tunable mechanical strength (500–1300 kPa), elongation at break (15–32%), Young’s modulus (4–9 MPa), toughness (0.05–0.26 MJ/m3), and suitable electrical resistivity (3.7–9.2 Ω·m). Moreover, the developed materials were responsive to external stimuli such as strain and liquids, satisfying the requirements for wearable sensor applications. Besides, composed of only starch, CaCl2, and water, the materials are much cheaper and eco-friendly (can be consumed by fish) compared with other polymer-based conductive hydrogels

Development and validation of platelet-to-albumin ratio as a clinical predictor for diffuse large B-cell lymphoma

IntroductionDiffuse large B-cell lymphoma (DLBCL) is the most common subtypes of lymphoma. Clinical biomarkers are still required for DLBCL patients to identify high-risk patients. Therefore, we developed and validated the platelet-to-albumin (PTA) ratio as a predictor for DLBCL patients.MethodsA group of 749 patients was randomly divided into a training set (600 patients) and an internal validation set (149 cases). The independent cohort of 110 patients was enrolled from the other hospital as an external validation set. Penalized smoothing spline (PS) Cox regression models were used to explore the non-linear relationship between the PTA ratio and overall survival (OS) as well as progression-free survival (PFS), respectively.ResultsA U-shaped relation between the PTA ratio and PFS was identified in the training set. The PTA ratio less than 2.7 or greater than 8.6 was associated with the shorter PFS. Additionally, the PTA ratio had an additional prognostic value to the well-established predictors. What’s more, the U-shaped pattern of the PTA ratio and PFS was respectively validated in the two validation sets.DiscussionA U-shaped association between the PTA ratio and PFS was found in patients with DLBCLs. The PTA ratio can be used as a biomarker, and may suggest abnormalities of both host nutritional aspect and systemic inflammation in DLBCL

Self-Assembled Porous-Reinforcement Microstructure-Based Flexible Triboelectric Patch for Remote Healthcare.

Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However, wearable sensors with low power consumption and high sensitivity to both weak physiological signals and large mechanical stimuli remain challenges. Here, a flexible triboelectric patch (FTEP) based on porous-reinforcement microstructures for remote health monitoring has been reported. The porous-reinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge. The mechanical properties of the FTEP can be regulated by the concentrations of silicone rubber dilution. For pressure sensing, its sensitivity can be effectively improved fivefold compared to the device with a solid dielectric layer, reaching 5.93 kPa-1 under a pressure range of 0-5 kPa. In addition, the FTEP has a wide detection range up to 50 kPa with a sensitivity of 0.21 kPa-1. The porous microstructure makes the FTEP ultra-sensitive to external pressure, and the reinforcements endow the device with a greater deformation limit in a wide detection range. Finally, a novel concept of the wearable Internet of Healthcare (IoH) system for real-time physiological signal monitoring has been proposed, which could provide real-time physiological information for ambulatory personalized healthcare monitoring

Solar Ring Mission: Building a Panorama of the Sun and Inner-heliosphere

Solar Ring (SOR) is a proposed space science mission to monitor and study the Sun and inner heliosphere from a full 360{\deg} perspective in the ecliptic plane. It will deploy three 120{\deg}-separated spacecraft on the 1-AU orbit. The first spacecraft, S1, locates 30{\deg} upstream of the Earth, the second, S2, 90{\deg} downstream, and the third, S3, completes the configuration. This design with necessary science instruments, e.g., the Doppler-velocity and vector magnetic field imager, wide-angle coronagraph, and in-situ instruments, will allow us to establish many unprecedented capabilities: (1) provide simultaneous Doppler-velocity observations of the whole solar surface to understand the deep interior, (2) provide vector magnetograms of the whole photosphere - the inner boundary of the solar atmosphere and heliosphere, (3) provide the information of the whole lifetime evolution of solar featured structures, and (4) provide the whole view of solar transients and space weather in the inner heliosphere. With these capabilities, Solar Ring mission aims to address outstanding questions about the origin of solar cycle, the origin of solar eruptions and the origin of extreme space weather events. The successful accomplishment of the mission will construct a panorama of the Sun and inner-heliosphere, and therefore advance our understanding of the star and the space environment that holds our life.Comment: 41 pages, 6 figures, 1 table, to be published in Advances in Space Researc