TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network

Reading text from images remains challenging due to multi-orientation, perspective distortion and especially the curved nature of irregular text. Most of existing approaches attempt to solve the problem in two or multiple stages, which is considered to be the bottleneck to optimize the overall performance. To address this issue, we propose an end-to-end trainable network architecture, named TextNet, which is able to simultaneously localize and recognize irregular text from images. Specifically, we develop a scale-aware attention mechanism to learn multi-scale image features as a backbone network, sharing fully convolutional features and computation for localization and recognition. In text detection branch, we directly generate text proposals in quadrangles, covering oriented, perspective and curved text regions. To preserve text features for recognition, we introduce a perspective RoI transform layer, which can align quadrangle proposals into small feature maps. Furthermore, in order to extract effective features for recognition, we propose to encode the aligned RoI features by RNN into context information, combining spatial attention mechanism to generate text sequences. This overall pipeline is capable of handling both regular and irregular cases. Finally, text localization and recognition tasks can be jointly trained in an end-to-end fashion with designed multi-task loss. Experiments on standard benchmarks show that the proposed TextNet can achieve state-of-the-art performance, and outperform existing approaches on irregular datasets by a large margin.Comment: Asian conference on computer vision, 2018, oral presentatio

Dynamic Rheological Studies of Poly(p-phenyleneterephthalamide) and Carbon Nanotube Blends in Sulfuric Acid

We have studied the dynamic scanning of liquid-crystalline (LC) poly(p-phenyleneterephthalamide) sulfuric acid (PPTA-H2SO4) solution, and its blend with single-walled carbon nanotubes (SWNTs), by using a flat plate rotational rheometer. The effects of weight concentration and molecular weight of PPTA, as well as operating temperature, on dynamic viscoelasticity of the PPTA-H2SO4 LC solution system are discussed. The transition from a biphasic system to a single-phase LC occurs in the weight concentration range of SWNTs from 0.1% to 0.2%, in which complex viscosity reaches the maximum at 0.2 wt% and the minimum at 0.1 wt%, respectively, of SWNTs. With increasing SWNT weight concentration, the endothermic peak temperature increases from 73.6 to 79.9 °C. The PPTA/SWNT/H2SO4 solution is in its plateau zone and storage modulus (G′) is a dominant factor within the frequency (ω) range of 0.1–10 rad/s. As ω increases, the G′ rises slightly, in direct proportion to the ω. The loss modulus (G″) does not rise as a function of ω when ω < 1 s−1, then when ω > 1 s−1 G″ increases faster than G′, yet not in any proportion to the ω

Mendelian randomization and Bayesian model averaging of autoimmune diseases and Long COVID

BackgroundFollowing COVID-19, reports suggest Long COVID and autoimmune diseases (AIDs) in infected individuals. However, bidirectional causal effects between Long COVID and AIDs, which may help to prevent diseases, have not been fully investigated.MethodsSummary-level data from genome-wide association studies (GWAS) of Long COVID (N = 52615) and AIDs including inflammatory bowel disease (IBD) (N = 377277), Crohn’s disease (CD) (N = 361508), ulcerative colitis (UC) (N = 376564), etc. were employed. Bidirectional causal effects were gauged between AIDs and Long COVID by exploiting Mendelian randomization (MR) and Bayesian model averaging (BMA).ResultsThe evidence of causal effects of IBD (OR = 1.06, 95% CI = 1.00–1.11, p = 3.13E-02), CD (OR = 1.10, 95% CI = 1.01–1.19, p = 2.21E-02) and UC (OR = 1.08, 95% CI = 1.03–1.13, p = 2.35E-03) on Long COVID was found. In MR-BMA, UC was estimated as the highest-ranked causal factor (MIP = 0.488, MACE = 0.035), followed by IBD and CD.ConclusionThis MR study found that IBD, CD and UC had causal effects on Long COVID, which suggests a necessity to screen high-risk populations

SIMULATION AND OPTIMIZATION OF COMBINED CYCLE GAS TURBINE POWER PLANTS

Ph.DDOCTOR OF PHILOSOPHY (FOE

Long-term economic planning of combined cooling heating and power systems considering energy storage and demand response

10.1016/j.apenergy.2020.115819Applied Energy27911581