An early fault feature extraction method for rolling bearings based on variational mode decomposition and random decrement technique

The early fault characteristics of rolling bearing are weak, and the background noise is so strong that it is difficult to diagnose. In order to solve the above problems, an early fault feature extraction method for rolling bearings based on variational mode decomposition and random decrement technique was proposed. The variational mode decomposition was used to decompose the collected vibration signals, and the component with the larger correlation coefficient was selected as the fault component. Then the fault component was processed by random decrement technology, and the Hilbert envelope spectrum of the fault component was made. According to the proposed method, the early fault characteristic of outer ring of rolling bearing was extracted. Compared with the method based on EMD, the proposed method is more effective in extracting the early fault characteristics of rolling bearings

A light Higgs scalar in the NMSSM confronted with the latest LHC Higgs data

In the Next-to-Minimal Supersymemtric Standard Model (NMSSM), one of the neutral Higgs scalars (CP-even or CP-odd) may be lighter than half of the SM-like Higgs boson. In this case, the SM-like Higgs boson h can decay into such a light scalar pair and consequently the diphoton and ZZ signal rates at the LHC will be suppressed. In this work, we examine the constraints of the latest LHC Higgs data on such a possibility. We perform a comprehensive scan over the parameter space of the NMSSM by considering various experimental constraints and find that the LHC Higgs data can readily constrain the parameter space and the properties of the light scalar, e.g., at 3 $\sigma$ level this light scalar should be highly singlet dominant and the branching ratio of the SM-like Higgs boson decay into the scalar pair should be less than about 30%. Also we investigate the detection of this scalar at various colliders. Through a detailed Monte Carlo simulation we find that under the constraints of the current Higgs data this light scalar can be accessible at the LHC-14 with an integrated luminosity over 300 fb$^{-1}$.Comment: Accepted by JHE

Class-Balanced and Reinforced Active Learning on Graphs

Graph neural networks (GNNs) have demonstrated significant success in various applications, such as node classification, link prediction, and graph classification. Active learning for GNNs aims to query the valuable samples from the unlabeled data for annotation to maximize the GNNs' performance at a lower cost. However, most existing algorithms for reinforced active learning in GNNs may lead to a highly imbalanced class distribution, especially in highly skewed class scenarios. GNNs trained with class-imbalanced labeled data are susceptible to bias toward majority classes, and the lower performance of minority classes may lead to a decline in overall performance. To tackle this issue, we propose a novel class-balanced and reinforced active learning framework for GNNs, namely, GCBR. It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes. GCBR designs class-balance-aware states, as well as a reward function that achieves trade-off between model performance and class balance. The reinforcement learning algorithm Advantage Actor-Critic (A2C) is employed to learn an optimal policy stably and efficiently. We further upgrade GCBR to GCBR++ by introducing a punishment mechanism to obtain a more class-balanced labeled set. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed approaches, achieving superior performance over state-of-the-art baselines

nnDetection for Intracranial Aneurysms Detection and Localization

Intracranial aneurysms are a commonly occurring and life-threatening condition, affecting approximately 3.2% of the general population. Consequently, detecting these aneurysms plays a crucial role in their management. Lesion detection involves the simultaneous localization and categorization of abnormalities within medical images. In this study, we employed the nnDetection framework, a self-configuring framework specifically designed for 3D medical object detection, to detect and localize the 3D coordinates of aneurysms effectively. To capture and extract diverse features associated with aneurysms, we utilized TOF-MRA and structural MRI, both obtained from the ADAM dataset. The performance of our proposed deep learning model was assessed through the utilization of free-response receiver operative characteristics for evaluation purposes. The model's weights and 3D prediction of the bounding box of TOF-MRA are publicly available at https://github.com/orouskhani/AneurysmDetection.Comment: 6 pages, 4 figure

Highly sensitive electrochemical sensing platform for the detection of L-dopa based on electropolymerizing glutathione disulfide and multi-walled carbon nanotube-modified electrodes

Afacile sensing platformfor the detection of L-dopa has been developed by electropolymerizing glutathione disulfide (PGSSG) on the surface of glass carbon electrodes (GCE) which were modified by multi-walled carbon nanotubes (MWCNTs). The electrochemical behaviour of the proposed electrodes were investigated via cyclic voltammetry (CV) and differential pulse voltammetry DPV). The morphology of the PGSSG and PGSSG/MWCNTs were characterized by scanning electron microscopy (SEM). Under the optimized experimental conditions, the sensing platform showed the linear response to L-Dopa in a range from 1.0 × 10–6 to 1.2 × 10–3 M with a detection limit of 3.3 × 10–7M (S/N = 3). Moreover, with the merits of high sensitivity and selectivity, good stability and reproducibility, the sensor was successfully applied for the determination of L-dopa in a real sample.Keywords: L-dopa, glutathione disulfide, multi-walled carbon nanotubes, electropolymerization, electrochemical determinatio

The cellular distribution of Na+/H+ exchanger regulatory factor 1 is determined by the PDZ-I domain and regulates the malignant progression of breast cancer

The oncogenic role of ectopic expression of Na+/H+ exchanger regulatory factor 1 (NHERF1) was recently suggested. Here, we show that NHERF1 was upregulated in high grades compared with low grades. Increased NHERF1 expression was correlated with poor prognosis and poor survival. NHERF1 expression was higher in the nucleus of cancer cells than in contiguous non- mammary epithelial cells. A novel mutation, namely NHERF1 Y24S, was identified in human breast cancer tissues and shown to correspond to a conserved residue in the PDZ-I domain of NHERF1. Truncation and mutation of the PDZ-I domain of NHERF1 increased the nuclear distribution of the NHERF1 protein, and this redistribution was associated with the malignant phenotype of breast cancer cells, including growth, migration, and adhesion. The present results suggest a role for NHERF1 in the progression of breast cancer mediated by the nuclear distribution of the NHERF1 protein, as determined by the truncation or key site mutation of the PDZ-I domain

Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition

Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represented by a prototype vector because there are generally a large number of samples in the class that have miscellaneous semantics. To solve the problem, we propose MeTNet, which generates prototype vectors for entity types only but not O-class. We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type. The margin plays as a radius and controls a region with adaptive size in the low-dimensional space. Based on the regions, we propose a new inference procedure to predict the label of a query instance. We conduct extensive experiments in both in-domain and cross-domain settings to show the superiority of MeTNet over other state-of-the-art methods. In particular, we release a Chinese few-shot NER dataset FEW-COMM extracted from a well-known e-commerce platform. To the best of our knowledge, this is the first Chinese few-shot NER dataset. All the datasets and codes are provided at https://github.com/hccngu/MeTNet

Exchanging-based Multimodal Fusion with Transformer

We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from one modality to the other. However, most of them project inputs of multimodalities into different low-dimensional spaces and cannot be applied to the sequential input data. To solve these issues, in this paper, we propose a novel exchanging-based multimodal fusion model MuSE for text-vision fusion based on Transformer. We first use two encoders to separately map multimodal inputs into different low-dimensional spaces. Then we employ two decoders to regularize the embeddings and pull them into the same space. The two decoders capture the correlations between texts and images with the image captioning task and the text-to-image generation task, respectively. Further, based on the regularized embeddings, we present CrossTransformer, which uses two Transformer encoders with shared parameters as the backbone model to exchange knowledge between multimodalities. Specifically, CrossTransformer first learns the global contextual information of the inputs in the shallow layers. After that, it performs inter-modal exchange by selecting a proportion of tokens in one modality and replacing their embeddings with the average of embeddings in the other modality. We conduct extensive experiments to evaluate the performance of MuSE on the Multimodal Named Entity Recognition task and the Multimodal Sentiment Analysis task. Our results show the superiority of MuSE against other competitors. Our code and data are provided at https://github.com/RecklessRonan/MuSE