Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning

The recognition ability of human beings is developed in a progressive way. Usually, children learn to discriminate various objects from coarse to fine-grained with limited supervision. Inspired by this learning process, we propose a simple yet effective model for the Few-Shot Fine-Grained (FSFG) recognition, which tries to tackle the challenging fine-grained recognition task using meta-learning. The proposed method, named Pairwise Alignment Bilinear Network (PABN), is an end-to-end deep neural network. Unlike traditional deep bilinear networks for fine-grained classification, which adopt the self-bilinear pooling to capture the subtle features of images, the proposed model uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric. In order to match base image features with query image features, we design feature alignment losses before the proposed pairwise bilinear pooling. Experiment results on four fine-grained classification datasets and one generic few-shot dataset demonstrate that the proposed model outperforms both the state-ofthe-art few-shot fine-grained and general few-shot methods.Comment: ICME 2019 Ora

Rail Infrastructure Defect Detection Through Video Analytics

University of Technology Sydney. Faculty of Engineering and Information Technology.Compared with the traditional railway infrastructure maintenance process, which relies on manual inspection by professional maintenance engineers, inspection through automatic video analytics will significantly improve the working efficiency and eliminate the potential safety concern by reducing physical contact between maintenance engineers and infrastructure facilities. However, the defect does not always have a stable appearance and involves many uncertainties exposed in the clutter environments. On the other hand, various brands of the same devices are used widely on the railway, which shows diverse physical models. Therefore, it creates many challenges to the existing computer vision algorithms for defect detection. In this thesis, two key challenges are abstracted about video/image analytics using computer vision techniques for railway infrastructure defect detection, resulting from the fine-grained defect recognition and the limited labelled learning (few-shot learning). This thesis summarizes the works that have been conducted on utilizing different methods to solve the two challenges. The first challenge is fine-grained defect recognition. For railway infrastructure defect inspection, damaged or worn equipment defects are usually found in some small parts. That is, the differences between the defective ones and standard ones are fine-grained. Finding these subtle defects is a fine-grained recognition problem. This thesis proposes a bilinear CNNs model to tackle the defect detection problem, which effectively captures the invariant representation of the dataset and learns high-order discriminative features for fine-grained defect recognition. Another challenge is the limited labelled data. In many scenarios, how to obtain abundant labelled samples is laborious. For example, in industrial defect detection, most defects exist only in a few common categories, while most other categories only contain a small portion of defects. Moreover, annotating a large-scale dataset of defects is labour-intensive, which requires high expertise in railway maintenance. Thus, how to obtain an effective model with sparse labelled samples remains an open problem. To address this issue, this thesis proposes a framework to simultaneously reduce the intra-class variance and enlarge the inter-class discrimination for both fine-grained defect recognition and general fine-grained recognition under the few-shot setting. Three models are designed according to this framework, and comprehensive experimental analyses are provided to validate the effectiveness of the models. This thesis further studies the few-shot learning problem by mining the unlabelled information to boost the few-shot learner for defect/general object recognition and proposes a Poisson Transfer Model to maximize the value of the extra unlabelled data through robust classifier construction and self-supervised representation learning

Channel-Wise Contrastive Learning for Learning with Noisy Labels

In real-world datasets, noisy labels are pervasive. The challenge of learning with noisy labels (LNL) is to train a classifier that discerns the actual classes from given instances. For this, the model must identify features indicative of the authentic labels. While research indicates that genuine label information is embedded in the learned features of even inaccurately labeled data, it's often intertwined with noise, complicating its direct application. Addressing this, we introduce channel-wise contrastive learning (CWCL). This method distinguishes authentic label information from noise by undertaking contrastive learning across diverse channels. Unlike conventional instance-wise contrastive learning (IWCL), CWCL tends to yield more nuanced and resilient features aligned with the authentic labels. Our strategy is twofold: firstly, using CWCL to extract pertinent features to identify cleanly labeled samples, and secondly, progressively fine-tuning using these samples. Evaluations on several benchmark datasets validate our method's superiority over existing approaches

Masked Cross-image Encoding for Few-shot Segmentation

Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes using only a limited number of annotated images. The key challenge in FSS is to classify the labels of query pixels using class prototypes learned from the few labeled support exemplars. Prior approaches to FSS have typically focused on learning class-wise descriptors independently from support images, thereby ignoring the rich contextual information and mutual dependencies among support-query features. To address this limitation, we propose a joint learning method termed Masked Cross-Image Encoding (MCE), which is designed to capture common visual properties that describe object details and to learn bidirectional inter-image dependencies that enhance feature interaction. MCE is more than a visual representation enrichment module; it also considers cross-image mutual dependencies and implicit guidance. Experiments on FSS benchmarks PASCAL-$5^i$ and COCO-$20^i$ demonstrate the advanced meta-learning ability of the proposed method.Comment: conferenc

Genotyping of Salmonella enterica serovar Typhi strains isolated from 1959 to 2006 in China and analysis of genetic diversity by genomic microarray

Aim To determine the genotype of Salmonella enterica serovar Typhi (S. Typhi) strains in China and analyze their genetic diversity. Methods We collected S. Typhi strains from 1959 to 2006 in five highly endemic Chinese provinces and chose 40 representative strains. Multilocus sequence typing was used to determine the genotypes or sequence types (ST) and microarray-based comparative genomic hybridization (M-CGH) to investigate the differences in gene content among these strains. Results Forty representative S. Typhi strains belonged to 4 sequence types (ST1, ST2, ST890, and ST892). The predominant S. Typhi genotype (31/40) was ST2 and it had a diverse geographic distribution. We discovered two novel STs – ST890 and ST892. M-CGH showed that 69 genes in these two novel STs were divergent from S. Typhi Ty2, which belongs to ST1. In addition, 5 representative Typhi strains of ST2 isolated from Guizhou province showed differences in divergent genes. Conclusion We determined two novel sequence types, ST890 and ST892, and found that ST2 was the most prevalent genotype of S. Typhi in China. Genetic diversity was present even within a highly clonal bacterial population

Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels

In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean data. These algorithms often incorporate sophisticated techniques, such as noise modeling, label correction, and co-training. In this study, we demonstrate that a simple baseline using cross-entropy loss, combined with widely used regularization strategies like learning rate decay, model weights average, and data augmentations, can outperform state-of-the-art methods. Our findings suggest that employing a combination of regularization strategies can be more effective than intricate algorithms in tackling the challenges of learning with noisy labels. While some of these regularization strategies have been utilized in previous noisy label learning research, their full potential has not been thoroughly explored. Our results encourage a reevaluation of benchmarks for learning with noisy labels and prompt reconsideration of the role of specialized learning algorithms designed for training with noisy labels

Identification and characterization of class 1 integrons among Pseudomonas aeruginosa isolates from patients in Zhenjiang, China

SummaryObjectivesThe role of integrons in the spread of antibiotic resistance has been well established. The aim of this study was to investigate the resistance profiles of Pseudomonas aeruginosa isolated from patients in Zhenjiang to 13 antibiotics, and to identify the structure and dissemination of class 1 integrons.MethodsThe Kirby–Bauer disk diffusion assay was used to determine the rate of P. aeruginosa resistance. Class 1 integrons from multidrug-resistant isolates were amplified by PCR, and their PCR products were sequenced. We also analyzed the integron structures containing the same gene cassettes by restriction fragment length polymorphism (RFLP). Isolates were genotyped by pulsed-field gel electrophoresis (PFGE).ResultsThe resistance rates were between 29.6% and 90.1%. The prevalence of class 1 integrons was 38.0%. These integrons included five gene cassettes (aadB, aac6-II, blaPSE-1, dfrA17, and aadA5). The dfrA17 and aadA5 gene cassettes were found most often.ConclusionsClass 1 integrons were found to be widespread in P. aeruginosa isolated from clinical samples in the Zhenjiang area of China. The antibiotic resistance rates in class 1 integron-positive strains of P. aeruginosa were noticeably higher than those in class 1 integron-negative strains. PFGE showed that particular clones were circulating among patients

Enhanced HMGB1 Expression May Contribute to Th17 Cells Activation in Rheumatoid Arthritis

Rheumatoid arthritis(RA) is a common autoimmune disease associated with Th17 cells, but what about the effect of high-mobility group box chromosomal protein 1 (HMGB1) and the relationship between Th17-associated factors and HMGB1 in RA remains unknown. In the present study, we investigated the mRNA levels of HMGB1, RORγt, and IL-17 in peripheral blood mononuclear cells (PBMCs) from patients with rheumatoid arthritis by quantitative real-time PCR (RT-qPCR), and the concentrations of HMGB1, IL-17, and IL-23 in plasma were detected by ELISA. And then, the effect of HMGB1 on Th17 cells differentiation was analyzed in vitro. Our clinical studies showed that the mRNAs of HMGB1, RORγt, and IL-17 in patients were higher than that in health control (P < 0.05), especially in active RA patients (P < 0.05). The plasma HMGB1, IL-17, and IL-23 in RA patients were also higher than that in health control (P < 0.05); there was a positive correlation between the expression levels of HMGB1 and the amount of CRP, ERS, and RF in plasma. In vitro, the IL-17-produced CD4+T cells were increased with 100 ng/mL rHMGB1 for 12h, which indicated that the increased HMGB1 might contribute to Th17 cells activation in RA patients

RPS23RG1 modulates tau phosphorylation and axon outgrowth through regulating p35 proteasomal degradation

Tau蛋白病（Tauopathies）是由过度磷酸化的tau蛋白聚集形成神经纤维缠结为特征的一类神经退行性疾病，包括阿尔茨海默病（Alzheimer’s disease, AD）、进行性核上性麻痹（Progressive superanuclear palsy, PSP）、额颞叶痴呆（Frontotemporal dementia, FTD）等。随着全球社会结构的老龄化，tau蛋白病患者比率迅速增加，给个人和社会带来巨大的经济及精神负担。厦门大学神经科学研究所张云武教授团队最新发现RPS23RG1（RR1）的胞内羧基端区域能够与Cdk5激酶的激活蛋白p35的氨基端相互作用，介导p35的膜定位并影响其泛素化降解，从而调控在tau蛋白异常磷酸化过程中发挥重要作用的Cdk5激酶的活性。团队研究表明RPS23RG1通过其胞内羧基端与p35相互作用，介导p35膜结合和降解，从而抑制Cdk5活性，平衡tau磷酸化水平，促进轴突生长。此外，RPS23RG1的跨膜区与腺苷酸环化酶AC相互作用，抑制GSK3-β活性，同样控制tau过度磷酸化。提示RPS23RG1是改善tau过度磷酸化水平及治疗tau蛋白病的潜在靶点。 厦门大学医学院神经科学研究所博士后赵东栋为该研究第一作者，张云武教授为通讯作者。【Abstract】Tauopathies are a group of neurodegenerative diseases characterized by hyperphosphorylation of the microtubule-binding protein, tau, and typically feature axon impairment and synaptic dysfunction. Cyclin-dependent kinase5 (Cdk5) is a major tau kinase and its activity requires p35 or p25 regulatory subunits. P35 is subjected to rapid proteasomal degradation in its membrane-bound form and is cleaved by calpain under stress to a stable p25 form, leading to aberrant Cdk5 activation and tau hyperphosphorylation. The type Ib transmembrane protein RPS23RG1 has been implicated in Alzheimer’s disease (AD). However, physiological and pathological roles for RPS23RG1 in AD and other tauopathies are largely unclear. Herein, we observed retarded axon outgrowth, elevated p35 and p25 protein levels, and increased tau phosphorylation at major Cdk5 phosphorylation sites in Rps23rg1 knockout (KO) mice. Both downregulation of p35 and the Cdk5 inhibitor roscovitine attenuated tau hyperphosphorylation and axon outgrowth impairment in Rps23rg1 KO neurons. Interestingly, interactions between the RPS23RG1 carboxyl-terminus and p35 amino-terminus promoted p35 membrane distribution and proteasomal degradation. Moreover, P301L tau transgenic (Tg) mice showed increased tau hyperphosphorylation with reduced RPS23RG1 levels and impaired axon outgrowth. Overexpression of RPS23RG1 markedly attenuated tau hyperphosphorylation and axon outgrowth defects in P301L tau Tg neurons. Our results demonstrate the involvement of RPS23RG1 in tauopathy disorders, and implicate a role for RPS23RG1 in inhibiting tau hyperphosphorylation through homeostatic p35 degradation and suppression of Cdk5 activation. Reduced RPS23RG1 levels in tauopathy trigger aberrant Cdk5-p35 activation, consequent tau hyperphosphorylation, and axon outgrowth impairment, suggesting that RPS23RG1 may be a potential therapeutic target in tauopathy disorders.This work was supported by grants from National Key Research and Development Program of China (2016YFC1305903 and 2018YFC2000400 to Y-wZ), National Natural Science Foundation of China (81771377, U1705285, 91332112, and 81225008 to Y-wZ), Fundamental Research Funds for the Central Universities (20720180049 to Y-wZ), the Fujian Provincial Health Commission-Education Department Joint Tackling Plan (WKJ2016-2-18 to F-rL), and Postdoctoral Science Foundation of China (2020M671948 to DZ)