Aggregated Text Transformer for Scene Text Detection

This paper explores the multi-scale aggregation strategy for scene text detection in natural images. We present the Aggregated Text TRansformer(ATTR), which is designed to represent texts in scene images with a multi-scale self-attention mechanism. Starting from the image pyramid with multiple resolutions, the features are first extracted at different scales with shared weight and then fed into an encoder-decoder architecture of Transformer. The multi-scale image representations are robust and contain rich information on text contents of various sizes. The text Transformer aggregates these features to learn the interaction across different scales and improve text representation. The proposed method detects scene texts by representing each text instance as an individual binary mask, which is tolerant of curve texts and regions with dense instances. Extensive experiments on public scene text detection datasets demonstrate the effectiveness of the proposed framework

(Z)-Ethyl 2,4-diphenyl-3-(propyl­amino)­but-2-enoate

The title compound, C21H25NO2, adopts a Z conformation about the C=C double bond. The mol­ecular structure is stabilized by an intra­molecular N—H⋯O hydrogen bond and the dihedral angle between the aromatic ring planes is 76.04 (12)°. The atoms of the ethyl substituent are disordered over two sets of sites in a 0.60 (2):0.40 (2) ratio

Ternary Compression for Communication-Efficient Federated Learning

Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of privacy and security with the growing use of smart mobile devices and IoT devices. Federated learning provides a potential solution to privacy-preserving and secure machine learning, by means of jointly training a global model without uploading data distributed on multiple devices to a central server. However, most existing work on federated learning adopts machine learning models with full-precision weights, and almost all these models contain a large number of redundant parameters that do not need to be transmitted to the server, consuming an excessive amount of communication costs. To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor. A convergence proof of the quantization factor and the unbiasedness of FTTQ is given. In addition, we propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Empirical experiments are conducted to train widely used deep learning models on publicly available datasets, and our results demonstrate the effectiveness of FTTQ and T-FedAvg compared with the canonical federated learning algorithms in reducing communication costs and maintaining the learning performance

Progressive Scene Text Erasing with Self-Supervision

Scene text erasing seeks to erase text contents from scene images and current state-of-the-art text erasing models are trained on large-scale synthetic data. Although data synthetic engines can provide vast amounts of annotated training samples, there are differences between synthetic and real-world data. In this paper, we employ self-supervision for feature representation on unlabeled real-world scene text images. A novel pretext task is designed to keep consistent among text stroke masks of image variants. We design the Progressive Erasing Network in order to remove residual texts. The scene text is erased progressively by leveraging the intermediate generated results which provide the foundation for subsequent higher quality results. Experiments show that our method significantly improves the generalization of the text erasing task and achieves state-of-the-art performance on public benchmarks

DDT: Dual-branch Deformable Transformer for Image Denoising

Transformer is beneficial for image denoising tasks since it can model long-range dependencies to overcome the limitations presented by inductive convolutional biases. However, directly applying the transformer structure to remove noise is challenging because its complexity grows quadratically with the spatial resolution. In this paper, we propose an efficient Dual-branch Deformable Transformer (DDT) denoising network which captures both local and global interactions in parallel. We divide features with a fixed patch size and a fixed number of patches in local and global branches, respectively. In addition, we apply deformable attention operation in both branches, which helps the network focus on more important regions and further reduces computational complexity. We conduct extensive experiments on real-world and synthetic denoising tasks, and the proposed DDT achieves state-of-the-art performance with significantly fewer computational costs.Comment: The code is avaliable at: https://github.com/Merenguelkl/DD

Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. IV. Hβ\beta Time Lags and Implications for Super-Eddington Accretion

We have completed two years of photometric and spectroscopic monitoring of a large number of active galactic nuclei (AGNs) with very high accretion rates. In this paper, we report on the result of the second phase of the campaign, during 2013--2014, and the measurements of five new H$\beta$ time lags out of eight monitored AGNs. All five objects were identified as super-Eddington accreting massive black holes (SEAMBHs). The highest measured accretion rates for the objects in this campaign are $\dot{\mathscr{M}}\gtrsim 200$, where $\dot{\mathscr{M}}= \dot{M}_{\bullet}/L_{\rm Edd}c^{-2}$, $\dot{M}_{\bullet}$ is the mass accretion rates, $L_{\rm Edd}$ is the Eddington luminosity and $c$ is the speed of light. We find that the H$\beta$ time lags in SEAMBHs are significantly shorter than those measured in sub-Eddington AGNs, and the deviations increase with increasing accretion rates. Thus, the relationship between broad-line region size ($R_{_{\rm H\beta}}$) and optical luminosity at 5100\AA, $R_{_{\rm H\beta}}-L_{5100}$, requires accretion rate as an additional parameter. We propose that much of the effect may be due to the strong anisotropy of the emitted slim-disk radiation. Scaling $R_{_{\rm H\beta}}$ by the gravitational radius of the black hole, we define a new radius-mass parameter ($Y$) and show that it saturates at a critical accretion rate of $\dot{\mathscr{M}}_c=6\sim 30$, indicating a transition from thin to slim accretion disk and a saturated luminosity of the slim disks. The parameter $Y$ is a very useful probe for understanding the various types of accretion onto massive black holes. We briefly comment on implications to the general population of super-Eddington AGNs in the universe and applications to cosmology.Comment: 53 pages, 12 figures, 7 tables, accepted for publication in The Astrophysical Journa

Anti-inflammatory and anti-oxidative effects of corilagin in a rat model of acute cholestasis

BACKGROUND: Nowadays, treatments for cholestasis remain largely nonspecific and often ineffective. Recent studies showed that inflammatory injuries and oxidative stress occur in the liver with cholestasis. In this study, we would use corilagin to treat the animal model of acute cholestasis in order to define the activity to interfere with inflammation-related and oxidative stress pathway in cholestatic pathogenesis. METHODS: Rats were administrated with alpha-naphthylisothiocyanate to establish model of cholestasis and divided into corilagin, ursodeoxycholic acid, dexamethasone, model and normal groups with treatment of related agent. At 24h, 48h and 72h time points after administration, living condition, serum markers of liver damage, pathological changes of hepatic tissue, nuclear factor (NF)-kappaB, myeloperoxidase (MPO), malondialdehyde (MDA), superoxide dismutase (SOD) and nitric oxide (NO) were examined and observed. RESULTS: Compared to model group, corilagin had remarkable effect on living condition, pathological manifestation of liver tissue, total bilirubin, direct bilirubin, (P<0.01), but no effect on alanine aminotransferase (ALT) and aspartate aminotransferase (AST). With corilagin intervention, levels of MPO, MDA and translocation of NF-κB were notably decreased, and levels of SOD and NO were markedly increased (P<0.05 or P<0.01). CONCLUSIONS: It is shown that corilagin is a potential component to relieve cholestasis through inflammation-related and oxidation-related pathway