Context Perception Parallel Decoder for Scene Text Recognition

Scene text recognition (STR) methods have struggled to attain high accuracy and fast inference speed. Autoregressive (AR)-based STR model uses the previously recognized characters to decode the next character iteratively. It shows superiority in terms of accuracy. However, the inference speed is slow also due to this iteration. Alternatively, parallel decoding (PD)-based STR model infers all the characters in a single decoding pass. It has advantages in terms of inference speed but worse accuracy, as it is difficult to build a robust recognition context in such a pass. In this paper, we first present an empirical study of AR decoding in STR. In addition to constructing a new AR model with the top accuracy, we find out that the success of AR decoder lies also in providing guidance on visual context perception rather than language modeling as claimed in existing studies. As a consequence, we propose Context Perception Parallel Decoder (CPPD) to decode the character sequence in a single PD pass. CPPD devises a character counting module and a character ordering module. Given a text instance, the former infers the occurrence count of each character, while the latter deduces the character reading order and placeholders. Together with the character prediction task, they construct a context that robustly tells what the character sequence is and where the characters appear, well mimicking the context conveyed by AR decoding. Experiments on both English and Chinese benchmarks demonstrate that CPPD models achieve highly competitive accuracy. Moreover, they run approximately 7x faster than their AR counterparts, and are also among the fastest recognizers. The code will be released soon

The Character of Entropy Production in Rayleigh–Bénard Convection

In this study; the Rayleigh–Bénard convection model was established; and a great number of Bénard cells with different numbered vortexes were acquired by numerical simulation. Additionally; the Bénard cell with two vortexes; which appeared in the steady Bénard fluid with a different Rayleigh number (abbreviated Ra); was found to display the primary characteristics of the system’s entropy production. It was found that two entropy productions; which are calculated using either linear theory or classical thermodynamic theory; are all basically consistent when the system can form a steady Bénard flow in the proper range of the Rayleigh number’s parameters. Furthermore; in a steady Bénard flow; the entropy productions of the system increase alongside the Ra parameters. It was also found that the difference between the two entropy productions is the driving force to drive the system to a steady state. Otherwise; through the distribution of the local entropy production of the Bénard cell; two vortexes are clearly located where there is minimum local entropy production and in the borders around the cell’s areas of larger local entropy production

Amyloid-β Oligomer-Induced Electrophysiological Mechanisms and Electrical Impedance Changes in Neurons

Amyloid plays a critical role in the pathogenesis of Alzheimer’s disease (AD) and can aggregate to form oligomers and fibrils in the brain. There is increasing evidence that highly toxic amyloid-β oligomers (AβOs) lead to tau protein aggregation, hyperphosphorylation, neuroinflammation, neuronal loss, synaptic loss, and dysfunction. Although the effects of AβOs on neurons have been investigated using conventional biochemical experiments, there are no established criteria for electrical evaluation. To this end, we explored electrophysiological changes in mouse hippocampal neurons (HT22) following exposure to AβOs and/or naringenin (Nar, a flavonoid compound) using electrical impedance spectroscopy (EIS). AβO-induced HT22 showed a decreased impedance amplitude and increased phase angle, and the addition of Nar reversed these changes. The characteristic frequency was markedly increased with AβO exposure, which was also reversed by Nar. The AβOs decreased intranuclear and cytoplasmic resistance and increased nucleus resistance and extracellular capacitance. Overall, the innovative construction of the eight-element CPE-equivalent circuit model further reflects that the pseudo-capacitance of the cell membrane and cell nucleus was increased in the AβO-induced group. This study conclusively revealed that AβOs induce cytotoxic effects by disrupting the resistance characteristics of unit membranes. The results further support that EIS is an effective technique for evaluating AβO-induced neuronal damage and microscopic electrical distinctions in the sub-microscopic structure of reactive cells

SVTR: Scene Text Recognition with a Single Visual Model

Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription. This hybrid architecture, although accurate, is complex and less efficient. In this study, we propose a Single Visual model for Scene Text recognition within the patch-wise image tokenization framework, which dispenses with the sequential modeling entirely. The method, termed SVTR, firstly decomposes an image text into small patches named character components. Afterward, hierarchical stages are recurrently carried out by component-level mixing, merging and/or combining. Global and local mixing blocks are devised to perceive the inter-character and intra-character patterns, leading to a multi-grained character component perception. Thus, characters are recognized by a simple linear prediction. Experimental results on both English and Chinese scene text recognition tasks demonstrate the effectiveness of SVTR. SVTR-L (Large) achieves highly competitive accuracy in English and outperforms existing methods by a large margin in Chinese, while running faster. In addition, SVTR-T (Tiny) is an effective and much smaller model, which shows appealing speed at inference. The code is publicly available at https://github.com/PaddlePaddle/PaddleOCR.Comment: Accepted by IJCAI 202

Development and validation of a UPLC–MS/MS assay for the determination of gemcitabine and its L-carnitine ester derivative in rat plasma and its application in oral pharmacokinetics

A simple and rapid UPLC–MS/MS method to simultaneously determine gemcitabine and its L-carnitine ester derivative (2'-deoxy-2', 2'-difluoro-N-((4-amino-4-oxobutanoyl) oxy)-4-(trimethyl amm-onio) butanoate-cytidine, JDR) in rat plasma was developed and validated. The conventional plasma sample preparation method of nucleoside analogues is solid-phase extraction (SPE) which is time-consuming and cost-expensive. In this study, gradient elution with small particles size solid phase was applied to effectively separate gemcitabine and JDR, and protein precipitation pretreatment was adopted to remove plasma protein and extract the analytes with high recovery(>81%). Method validation was performed as per the FDA guidelines, and the standard curves were found to be linear in the range of 5–4000 ng/ml for JDR and 4–4000 ng/ml for gemcitabine, respectively. The lower limit of quantitation (LLOQ) of gemcitabine and JDR was 4 and 5 ng/ml, respectively. The intra-day and inter-day precision and accuracy results were within the acceptable limits. Finally, the developed method was successfully applied to investigate the pharmacokinetic studies of JDR and gemcitabine after oral administration to rats

Additional file 1: of Genome-wide copy number variant analysis for congenital ventricular septal defects in Chinese Han population

Supplemental material. (DOCX 4259 kb