Pit Evolution Around the Fusion Line of a NiCrMoV Steel Welded Joint Caused by Galvanic and Stress-Assisted Coupling Corrosion

The corrosion of NiCrMoV steel welded joints is performed in an aqueous solution of 3.5 wt% NaCl at 180 °C in a container at a pressure of 0.8 MPa. The specimens in the shape of cylindrical tensile rods are immersed in the aqueous solution under the action of tensile stress in a range of 0 to 0.9 of the yield stress of the base metal. The experimental results suggest that there is macro-galvanic corrosion in the welded joint with the coarse-grained heat affected zone (CGHAZ) as anode due to the highest corrosion susceptibility of the CGHAZ. The CGHAZ has the highest positive current density in the welded joints as measured by the scanning vibrating electrode technique. The two-parameter Weibull distribution function, which is represented by the Weibull modulus and characteristic strength, is used to analyze the distribution of the depth of pits at different immersion times. Both the Weibull modulus and characteristic strength are calculated, and found to be dependent on the applied tensile stress. The values of the characteristic pit depth and the average pit depth reveal that there are two mechanisms controlling the corrosion of the NiCrMoV steel welded joints; one is galvanic corrosion, and the other is stress-assisted corrosion

DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops

Recommendation models trained on the user feedback collected from deployed recommendation systems are commonly biased. User feedback is considerably affected by the exposure mechanism, as users only provide feedback on the items exposed to them and passively ignore the unexposed items, thus producing numerous false negative samples. Inevitably, biases caused by such user feedback are inherited by new models and amplified via feedback loops. Moreover, the presence of false negative samples makes negative sampling difficult and introduces spurious information in the user preference modeling process of the model. Recent work has investigated the negative impact of feedback loops and unknown exposure mechanisms on recommendation quality and user experience, essentially treating them as independent factors and ignoring their cross-effects. To address these issues, we deeply analyze the data exposure mechanism from the perspective of data iteration and feedback loops with the Missing Not At Random (\textbf{MNAR}) assumption, theoretically demonstrating the existence of an available stabilization factor in the transformation of the exposure mechanism under the feedback loops. We further propose Dynamic Personalized Ranking (\textbf{DPR}), an unbiased algorithm that uses dynamic re-weighting to mitigate the cross-effects of exposure mechanisms and feedback loops without additional information. Furthermore, we design a plugin named Universal Anti-False Negative (\textbf{UFN}) to mitigate the negative impact of the false negative problem. We demonstrate theoretically that our approach mitigates the negative effects of feedback loops and unknown exposure mechanisms. Experimental results on real-world datasets demonstrate that models using DPR can better handle bias accumulation and the universality of UFN in mainstream loss methods

LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification

Extreme Multi-label text Classification (XMC) is a task of finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant success in XMC. However, the existing methods (e.g., AttentionXML and X-Transformer etc) still suffer from 1) combining several models to train and predict for one dataset, and 2) sampling negative labels statically during the process of training label ranking model, which reduces both the efficiency and accuracy of the model. To address the above problems, we proposed LightXML, which adopts end-to-end training and dynamic negative labels sampling. In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels. Through these networks, negative labels are sampled dynamically during label ranking part training by feeding with the same text representation. Extensive experiments show that LightXML outperforms state-of-the-art methods in five extreme multi-label datasets with much smaller model size and lower computational complexity. In particular, on the Amazon dataset with 670K labels, LightXML can reduce the model size up to 72% compared to AttentionXML

Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition

The teacher-free online Knowledge Distillation (KD) aims to train an ensemble of multiple student models collaboratively and distill knowledge from each other. Although existing online KD methods achieve desirable performance, they often focus on class probabilities as the core knowledge type, ignoring the valuable feature representational information. We present a Mutual Contrastive Learning (MCL) framework for online KD. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributions among a cohort of networks in an online manner. Our MCL can aggregate cross-network embedding information and maximize the lower bound to the mutual information between two networks. This enables each network to learn extra contrastive knowledge from others, leading to better feature representations, thus improving the performance of visual recognition tasks. Beyond the final layer, we extend MCL to intermediate layers and perform an adaptive layer-matching mechanism trained by meta-optimization. Experiments on image classification and transfer learning to visual recognition tasks show that layer-wise MCL can lead to consistent performance gains against state-of-the-art online KD approaches. The superiority demonstrates that layer-wise MCL can guide the network to generate better feature representations. Our code is publicly avaliable at https://github.com/winycg/L-MCL.Comment: 18 pages, accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI-2023

Label-Free Proteomics Reveals Decreased Expression of CD18 and AKNA in Peripheral CD4+ T Cells from Patients with Vogt-Koyanagi-Harada Syndrome

Vogt-Koyanagi-Harada (VKH) syndrome is a systemic autoimmune disease. CD4+ T cells have been shown to be involved in autoimmune diseases including VKH syndrome. To screen aberrantly expressed membrane proteins in CD4+ T cell from patients with active VKH syndrome, blood samples were taken from five patients with active VKH syndrome and five healthy individuals. A label-free quantitative proteomic strategy was used to identify the differently expressed proteins between the two groups. The results revealed that the expression of 102 peptides was significantly altered (p<0.05) between two groups and matched amino acid sequences of proteins deposited in the international protein index (ipi.HUMAN.v3.36.fasta). The identified peptides corresponded to 64 proteins, in which 30 showed more than a 1.5-fold difference between the two groups. The decreased expression of CD18 and AKNA transcription factor (AKNA), both being three-fold lower than controls in expression identified by the label-free method, was further confirmed in an additional group of five active VKH patients and six normal individuals using the Western blot technique. A significantly decreased expression of CD18 and AKNA suggests a role for both proteins in the pathogenesis of this syndrome

No Association of PTPN22 Polymorphisms with Susceptibility to Ocular Behcet's Disease in Two Chinese Han Populations

Background: Behcet’s disease is known as a recurrent, multisystem inflammation and immune-related disease. Protein tyrosine phosphatase non-receptor 22 (PTPN22) is a key negative regulator of T lymphocytes and polymorphisms of the PTPN22 gene have been shown to be associated with various immune-related diseases. The present study was performed to assess the association between PTPN22 polymorphisms and Behcet’s disease in two Chinese Han populations. Methodology/Principal Findings: A total of 516 patients with ocular Behcet’s disease and 690 healthy controls from two Chinese Han populations were genotyped by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method for three single nucleotide polymorphisms (SNPs). Hardy-Weinberg equilibrium was tested using the x 2 test. Genotype frequencies were estimated through direct counting. Allele and genotype frequencies were compared between patients and controls using logistic regression analysis. The results revealed that there was no association between the tested three PTPN22 SNPs (rs2488457, rs1310182 and rs3789604) and ocular Behcet’s disease (p.0.05). Categorization analysis according to the clinical features did not show any association of these three polymorphisms with these parameters (p.0.05). Conclusions/Significance: The investigated PTPN22 gene polymorphisms (rs2488457, rs1310182 and rs3789604) were not associated with ocular Behcet’s disease in two Chinese Han populations, and showed that it may be different from othe