A convolutional neural network based Chinese text detection algorithm via text structure modeling

Text detection in natural scene environment plays an important role in many computer vision applications. While existing text detection methods are focused on English characters, there is strong application demands on text detection in other languages, such as Chinese. As Chinese characters are much more complex than English characters, innovative and more efficient text detection techniques are required for Chinese texts. In this paper, we present a novel text detection algorithm for Chinese characters based on a specific designed convolutional neural network (CNN). The CNN model contains a text structure component detector layer, a spatial pyramid layer and a multi-input-layer deep belief network (DBN). The CNN is pretrained via a convolutional sparse auto-encoder (CSAE) in an unsupervised way, which is specifically designed for extracting complex features from Chinese characters. In particular, the text structure component detectors enhance the accuracy and uniqueness of feature descriptors by extracting multiple text structure components in various ways. The spatial pyramid layer is then introduced to enhance the scale invariability of the CNN model for detecting texts in multiple scales. Finally, the multi-input-layer DBN is used as the fully connected layers in the CNN model to ensure that features from multiple scales are comparable. A multilingual text detection dataset, in which texts in Chinese, English and digits are labeled separately, is set up to evaluate the proposed text detection algorithm. The proposed algorithm shows a significant 10% performance improvement over the baseline CNN algorithms. In addition the proposed algorithm is evaluated over a public multilingual image benchmark and achieves state-of-the-art results for text detection under multiple languages. Furthermore a simplified version of the proposed algorithm with only general components is compared to existing general text detection algorithms on the ICDAR 2011 and 2013 datasets, showing comparable detection performance to the existing algorithms

Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition

While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their potential usage. In this paper, we propose an efficient NeRF-based framework that enables real-time synthesizing of talking portraits and faster convergence by leveraging the recent success of grid-based NeRF. Our key insight is to decompose the inherently high-dimensional talking portrait representation into three low-dimensional feature grids. Specifically, a Decomposed Audio-spatial Encoding Module models the dynamic head with a 3D spatial grid and a 2D audio grid. The torso is handled with another 2D grid in a lightweight Pseudo-3D Deformable Module. Both modules focus on efficiency under the premise of good rendering quality. Extensive experiments demonstrate that our method can generate realistic and audio-lips synchronized talking portrait videos, while also being highly efficient compared to previous methods.Comment: Project page: https://me.kiui.moe/radnerf

Molecular Basis of Efficient Replication and Pathogenicity of H9N2 Avian Influenza Viruses in Mice

H9N2 subtype avian influenza viruses (AIVs) have shown expanded host range and can infect mammals, such as humans and swine. To date the mechanisms of mammalian adaptation and interspecies transmission of H9N2 AIVs remain poorly understood. To explore the molecular basis determining mammalian adaptation of H9N2 AIVs, we compared two avian field H9N2 isolates in a mouse model: one (A/chicken/Guangdong/TS/2004, TS) is nonpathogenic, another one (A/chicken/Guangdong/V/2008, V) is lethal with efficient replication in mouse brains. In order to determine the basis of the differences in pathogenicity and brain tropism between these two viruses, recombinants with a single gene from the TS (or V) virus in the background of the V (or TS) virus were generated using reverse genetics and evaluated in a mouse model. The results showed that the PB2 gene is the major factor determining the virulence in the mouse model although other genes also have variable impacts on virus replication and pathogenicity. Further studies using PB2 chimeric viruses and mutated viruses with a single amino acid substitution at position 627 [glutamic acid (E) to lysine, (K)] in PB2 revealed that PB2 627K is critical for pathogenicity and viral replication of H9N2 viruses in mouse brains. All together, these results indicate that the PB2 gene and especially position 627 determine virus replication and pathogenicity in mice. This study provides insights into the molecular basis of mammalian adaptation and interspecies transmission of H9N2 AIVs

Molecular basis of ligand recognition and activation of human V2 vasopressin receptor.

Vasopressin type 2 receptor (V2R) belongs to the vasopressin (VP)/oxytocin (OT) receptor subfamily of G protein-coupled receptors (GPCRs), which comprises at least four closely related receptor subtypes: V1aR, V1bR, V2R, and OTR. These receptors are activated by arginine vasopressin (AVP) and OT, two endogenous nine-amino acid neurohypophysial hormones, which are thought to mediate a biologically conserved role in social behavior and sexual reproduction. V2R is mainly expressed in the renal collecting duct principal cells and mediates the antidiuretic action of AVP by accelerating water reabsorption, thereby playing a vital role in controlling water homeostasis. Moreover, numerous gain-of-function and loss-of-function mutations of V2R have been identified and are closely associated with human diseases, including nephrogenic syndrome of inappropriate diuresis (NSIAD) and X-linked congenital nephrogenic diabetes insipidus (NDI). Thus, V2R has attracted intense interest as a drug target. However, due to a lack of structural information, how AVP recognizes and activates V2R remains elusive, which hampers the V2R-targeted drug design. Here, we determined a 2.6 Å resolution cryo-EM structure of the full-length, G s -coupled human V2R bound to AVP (Fig. 1a; Supplementary information, Table S1). The G s protein was engineered based on mini-G s that was used in the crystal structure determination of the G s -coupled adenosine A 2A receptor (A 2A R) to stabilize the V2R–G s protein complex (Supplementary information, Data S1). The final structure of the AVP–V2R–G s complex contains all residues of AVP (residues 1–9), the Gα s Ras-like domain, Gβγ subunits, Nb35, scFv16, and the V2R residues from T31 to L339 8.57 (superscripts refer to Ballesteros–Weinstein numbering). The majority of amino acid side chains, including AVP, transmembrane domain (TMD), all flexible intracellular loops (ICLs) and extracellular loops (ECLs) except for ICL3 and G185–G188 in ECL2, were well resolved in the model, refined against the EM density map (Fig. 1a; Supplementary information, Figs. S1–3). The complex structure can provide detailed information on the binding interface between AVP and helix bundle of the receptor, as well as the receptor–G s interface

Invariant Synthesis for Incomplete Verification Engines

We propose a framework for synthesizing inductive invariants for incomplete verification engines, which soundly reduce logical problems in undecidable theories to decidable theories. Our framework is based on the counter-example guided inductive synthesis principle (CEGIS) and allows verification engines to communicate non-provability information to guide invariant synthesis. We show precisely how the verification engine can compute such non-provability information and how to build effective learning algorithms when invariants are expressed as Boolean combinations of a fixed set of predicates. Moreover, we evaluate our framework in two verification settings, one in which verification engines need to handle quantified formulas and one in which verification engines have to reason about heap properties expressed in an expressive but undecidable separation logic. Our experiments show that our invariant synthesis framework based on non-provability information can both effectively synthesize inductive invariants and adequately strengthen contracts across a large suite of programs

iTRAQ-Based Differential Proteomic Analysis Reveals the Pathways Associated with Tigecycline Resistance in Acinetobacter baumannii

Background/Aims: Acinetobacter baumannii is an aerobic and Gram-negative bacterial pathogen with high morbidity and mortality. It remains a serious public health problem arising from its multidrug-resistant and extensive antibiotic resistance spectrum. Methods: In the present study, iTRAQ coupled with 2D LC-MS/MS was used to evaluate the proteome in standard Acinetobacter baumannii standard strains and tigecycline-resistant strains. Results: A total of 3639 proteins were identified and 961 proteins were identified to be differentially expressed in tigecycline-resistant Acinetobacter baumannii strains compared to the standard strains. 506 (52.6%) proteins were up-regulated and 455 (47.4%) proteins were down-regulated. Based on the GO enrichment analysis and KEGG pathway analysis, we concluded that most differentially expressed proteins were associated with stress responses, cellular component organization, proteins synthesis, degradation and function. Moreover, β-lactam resistance, the longevity regulating pathway and other related pathways were also involved in the regulation of tigecycline-resistant Acinetobacter baumannii. The differential expression of key proteins were evaluated by transcript analysis using quantitative RT-PCR. Conclusion: These results may provide new insights into the mechanisms of drug resistance in Acinetobacter baumannii