PixelLink: Detecting Scene Text via Instance Segmentation

Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/non-text classification and location regression. Regression plays a key role in the acquisition of bounding boxes in these methods, but it is not indispensable because text/non-text prediction can also be considered as a kind of semantic segmentation that contains full location information in itself. However, text instances in scene images often lie very close to each other, making them very difficult to separate via semantic segmentation. Therefore, instance segmentation is needed to address this problem. In this paper, PixelLink, a novel scene text detection algorithm based on instance segmentation, is proposed. Text instances are first segmented out by linking pixels within the same instance together. Text bounding boxes are then extracted directly from the segmentation result without location regression. Experiments show that, compared with regression-based methods, PixelLink can achieve better or comparable performance on several benchmarks, while requiring many fewer training iterations and less training data.Comment: AAAI-201

PixelLink: Detecting Scene Text via Instance Segmentation

Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/non-text classification and location regression. Regression plays a key role in the acquisition of bounding boxes in these methods, but it is not indispensable because text/non-text prediction can also be considered as a kind of semantic segmentation that contains full location information in itself. However, text instances in scene images often lie very close to each other, making them very difficult to separate via semantic segmentation. Therefore, instance segmentation is needed to address this problem. In this paper, PixelLink, a novel scene text detection algorithm based on instance segmentation, is proposed. Text instances are first segmented out by linking pixels within the same instance together. Text bounding boxes are then extracted directly from the segmentation result without location regression. Experiments show that, compared with regression-based methods, PixelLink can achieve better or comparable performance on several benchmarks, while requiring many fewer training iterations and less training data.Comment: AAAI-201

TWIST1 silencing attenuates intracranial aneurysms by inhibiting NF-κB signaling

Purpose: To investigate the effect of Twist family basic helix-loop-helix transcription factor 1 (TWIST1) on intracranial aneurysms.Methods: A rat model of intracranial aneurysm was established by ligating the posterior branches of both the renal and left common carotid arteries. Pathological changes in the intracranial arterial wall were investigated using hematoxylin-eosin staining. TWIST1 expression was assessed using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blot, while vascular smooth muscle cell apoptosis was investigated by terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) staining. Inflammation was evaluated using enzyme-linked immunosorbent assay (ELISA).Results: Rats with intracranial aneurysms had degenerative changes in vessel wall structure. Inintracranial aneurysm rats, TWIST1 was upregulated in arterial wall sections, and TWIST1 knockdown ameliorated the pathological changes to the arterial wall. TWIST1 silencing reduced serum tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) levels and suppressed vascular smooth muscle cell apoptosis in rats with intracranial aneurysms. TWIST1 knockdown increased phospho (p)-inhibitor of nuclear factor-κB (IκB) and decreased IκB and p-p65 in intracranial aneurysm rat arterial wall sections.Conclusion: In intracranial aneurysms, silencing TWIST1 promoted vascular remodeling and suppresses vascular smooth muscle cell apoptosis and inflammation through inactivating NF-κBsignaling, revealing TWIST1 silencing as a potential treatment strategy

Voltage-independent SK-channel dysfunction causes neuronal hyperexcitability in the hippocampus of Fmr1 knock-out mice

Neuronal hyperexcitability is one of the major characteristics of fragile X syndrome (FXS), yet the molecular mechanisms of this critical dysfunction remain poorly understood. Here we report a major role of voltage-independent potassium (

Audit Retendering and Mandatory Auditor Rotation

Building on an auction model, we examine the economic consequences of audit retendering, under which the incumbent auditor in auction possesses both an information advantage and knowledge advantage over outside auditors. Audit retendering allows the firm to retain the incumbent auditor with positive probability, but expect to pay information rent to the incumbent auditor due to his information advantage over outside auditors. In equilibrium, auditor switching (or no switching) under audit retendering conveys additional information to investors, and therefore the informativeness of the audit report under audit retendering is always greater than that under mandatory auditor rotation. We identify conditions under which client firms may benefit from audit retendering. Our findings shed light on the recent European Union Audit Reform, which adopts audit retendering as an alternative to auditor rotation, and have implications to the Public Company Accounting Oversight Board, which are evaluating the proposed mandatory auditor rotation

Ruminal microbe of biohydrogenation of trans-vaccenic acid to stearic acid in vitro

<p>Abstract</p> <p>Background</p> <p>Optimization of the unsaturated fatty acid composition of ruminant milk and meat is desirable. Alteration of the milk and fatty acid profile was previously attempted by the management of ruminal microbial biohydrogenation. The aim of this study was to identify the group of ruminal trans-vaccenic acid (trans-11 C18:1, t-VA) hydrogenating bacteria by combining enrichment studies in vitro.</p> <p>Methods</p> <p>The enrichment culture growing on t-VA was obtained by successive transfers in medium containing t-VA. Fatty acids were detected by gas chromatograph and changes in the microbial composition during enrichment were analyzed by denaturing gradient gel electrophoresis (DGGE). Prominent DGGE bands of the enrichment cultures were identified by 16S rRNA gene sequencing.</p> <p>Results</p> <p>The growth of ruminal t-VA hydrogenating bacteria was monitored through the process of culture transfer according to the accumulation of stearic acid (C18:0, SA) and ratio of the substrate (t-VA) transformed to the product (SA). A significant part of the retrieved 16S rRNA gene sequences was most similar to those of uncultured bacteria. Bacteria corresponding to predominant DGGE bands in t-VA enrichment cultures clustered with t-VA biohydrogenated bacteria within Group B.</p> <p>Conclusions</p> <p>This study provides more insight into the pathway of biohydrogenation. It also may be important to control the production of t-VA, which has metabolic and physiological benefits, through management of ruminal biohydrogenation bacterium.</p

A critical review of online battery remaining useful lifetime prediction methods.

Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods

Influences of tilted thin accretion disks on the optical appearance of hairy black holes in Horndeski gravity

Research on the optical appearance of black holes, both in general relativity and modified gravity, has been in full swing since the Event Horizon Telescope Collaboration announced photos of M87$^{*}$ and Sagittarius A$^{*}$. Nevertheless, limited attention has been given to the impact of tilted accretion disks on black hole images. This paper investigates the $230$ GHz images of non-rotating hairy black holes illuminated by tilted, thin accretion disks in Horndeski gravity with the aid of a ray tracing method. The results indicate that reducing the scalar hair parameter effectively diminishes image luminosity and extends both the critical curve and the inner shadow. This trend facilitates the differentiation between hairy black holes and Schwarzschild black holes. Furthermore, we observe that the inclination of the tilted accretion disk can mimic the observation angle, consequently affecting image brightness and the morphology of the inner shadow. In specific parameter spaces, the disk inclination has the ability to shift the position of the light spot in the images of hairy black holes. This finding may provide potential theoretical evidence for the formation of three flares at different positions in the Sagittarius A$^{*}$ image. Additionally, by examining the images of hairy black holes surrounded by two thin accretion disks, we report the obscuring effect of the accretion environment on the inner shadow of the black hole.Comment: 26 pages, 14 figure