Robust Representation Learning for Unreliable Partial Label Learning

Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. However, this idealistic assumption may not always hold due to potential annotation inaccuracies, meaning the ground-truth may not be present in the candidate label set. This is known as Unreliable Partial Label Learning (UPLL) that introduces an additional complexity due to the inherent unreliability and ambiguity of partial labels, often resulting in a sub-optimal performance with existing methods. To address this challenge, we propose the Unreliability-Robust Representation Learning framework (URRL) that leverages unreliability-robust contrastive learning to help the model fortify against unreliable partial labels effectively. Concurrently, we propose a dual strategy that combines KNN-based candidate label set correction and consistency-regularization-based label disambiguation to refine label quality and enhance the ability of representation learning within the URRL framework. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art PLL methods on various datasets with diverse degrees of unreliability and ambiguity. Furthermore, we provide a theoretical analysis of our approach from the perspective of the expectation maximization (EM) algorithm. Upon acceptance, we pledge to make the code publicly accessible

Learning From Biased Soft Labels

Knowledge distillation has been widely adopted in a variety of tasks and has achieved remarkable successes. Since its inception, many researchers have been intrigued by the dark knowledge hidden in the outputs of the teacher model. Recently, a study has demonstrated that knowledge distillation and label smoothing can be unified as learning from soft labels. Consequently, how to measure the effectiveness of the soft labels becomes an important question. Most existing theories have stringent constraints on the teacher model or data distribution, and many assumptions imply that the soft labels are close to the ground-truth labels. This paper studies whether biased soft labels are still effective. We present two more comprehensive indicators to measure the effectiveness of such soft labels. Based on the two indicators, we give sufficient conditions to ensure biased soft label based learners are classifier-consistent and ERM learnable. The theory is applied to three weakly-supervised frameworks. Experimental results validate that biased soft labels can also teach good students, which corroborates the soundness of the theory

A Novel Fractional-Order PID Controller for Integrated Pressurized Water Reactor Based on Wavelet Kernel Neural Network Algorithm

This paper presents a novel wavelet kernel neural network (WKNN) with wavelet kernel function. It is applicable in online learning with adaptive parameters and is applied on parameters tuning of fractional-order PID (FOPID) controller, which could handle time delay problem of the complex control system. Combining the wavelet function and the kernel function, the wavelet kernel function is adopted and validated the availability for neural network. Compared to the conservative wavelet neural network, the most innovative character of the WKNN is its rapid convergence and high precision in parameters updating process. Furthermore, the integrated pressurized water reactor (IPWR) system is established by RELAP5, and a novel control strategy combining WKNN and fuzzy logic rule is proposed for shortening controlling time and utilizing the experiential knowledge sufficiently. Finally, experiment results verify that the control strategy and controller proposed have the practicability and reliability in actual complicated system

Metagenomic insights into the composition and function of the gut microbiota of mice infected with Toxoplasma gondii

IntroductionDespite Toxoplasma gondii infection leading to dysbiosis and enteritis, the function of gut microbiota in toxoplasmosis has not been explored.MethodsHere, shotgun metagenomics was employed to characterize the composition and function of mouse microbial community during acute and chronic T. gondii infection, respectively.ResultsThe results revealed that the diversity of gut bacteria was decreased immediately after T. gondii infection, and was increased with the duration of infection. In addition, T. gondii infection led to gut microbiota dysbiosis both in acute and chronic infection periods. Therein, several signatures, including depression of Firmicutes to Bacteroidetes ratio and infection-enriched Proteobacteria, were observed in the chronic period, which may contribute to aggravated gut inflammation and disease severity. Functional analysis showed that a large amount of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and carbohydrate-active enzymes (CAZy) family displayed distinct variation in abundance between infected and healthy mice. The lipopolysaccharide biosynthesis related pathways were activated in the chronic infection period, which might lead to immune system imbalance and involve in intestinal inflammation. Moreover, microbial and functional spectrums were more disordered in chronic than acute infection periods, thus implying gut microbiota was more likely to participate in disease process in the chronically infected mice, even exacerbated immunologic derangement and disease progression.DiscussionOur data indicate that the gut microbiota plays a potentially important role in protecting mice from T. gondii infection, and contributes to better understand the association between gut microbiota and toxoplasmosis

Expression and distribution of PPP2R5C gene in leukemia

<p>Abstract</p> <p>Background</p> <p>Recently, we clarified at the molecular level novel chromosomal translocation t(14;14)(q11;q32) in a case of Sézary syndrome, which caused a rearrangement from TRAJ7 to the <it>PPP2R5C </it>gene. <it>PPP2R5C </it>is one of the regulatory B subunits of protein phosphatase 2A (PP2A). It plays a crucial role in cell proliferation, differentiation, and transformation. To characterize the expression and distribution of five different transcript variants of the <it>PPP2R5C </it>gene in leukemia, we analyzed the expression level of <it>PPP2R5C </it>in peripheral blood mononuclear cells from 77 patients with <it>de novo </it>leukemia, 26 patients with leukemia in complete remission (CR), and 20 healthy individuals by real-time PCR and identified the different variants of <it>PPP2R5C </it>by RT-PCR.</p> <p>Findings</p> <p>Significantly higher expression of <it>PPP2R5C </it>was found in AML, CML, T-ALL, and B-CLL groups in comparison with healthy controls. High expression of <it>PPP2R5C </it>was detected in the B-ALL group; however, no significant difference was found compared with the healthy group. The expression level of <it>PPP2R5C </it>in the CML-CR group decreased significantly compared with that in the <it>de novo </it>CML group and was not significantly different from the level in the healthy group. By using different primer pairs that covered different exons, five transcript variants of <it>PPP2R5C </it>could be identified. All variants could be detected in healthy samples as well as in all the leukemia samples, and similar frequencies and distributions of <it>PPP2R5C </it>were indicated.</p> <p>Conclusions</p> <p>Overexpression of <it>PPP2R5C </it>in T-cell malignancy as well as in myeloid leukemia cells might relate to its proliferation and differentiation. Investigation of the effect of target inhibition of this gene might be beneficial to further characterization of molecular mechanisms and targeted therapy in leukemia.</p