As an important part of mechanical equipment, rolling bearing holds significant importance in the normal operation of machinery. However, the parameter and computation of fault diagnosis approaches based on deep learning technique are huge, and most methods are diagnosed in the cloud, which can lead to time delays and non-real-time. To overcome these issues, in edge computing scenarios, a real-time bearing fault diagnosis network MobiKanViT is proposed in this paper. The network is a lightweight and low-latency vision fault diagnosis network with enhanced discriminative feature learning capability. The Efficient Multi-scale Attention (EMA) module is introduced to enhance the ability of feature recognition. The ordinary convolution module is substituted with the Kolmogorov-Arnold Networks (KAN) convolution module to solve the problem of large parameter and computation. The MobiKanViT is compressed and quantized with depth parameter γ to make the model more lightweight and easy to be deployed on edge devices. Verification experiments were conducted on two sets of experimental equipment, and three mobile phones were selected as mobile edge computing platforms. The experimental results show that a depth parameter of γ = 0.5 and INT8 quantization yield the most effective results for MobiKanViT. When juxtaposed with current large model techniques, the suggested approach decreases memory consumption by an average of 94.6%, while simultaneously boosting inference speed by about 15.87 times. In comparison to existing lightweight models, this new method also improves diagnostic accuracy by an average of 2.6%.

Abstract Background Mosaic loss of the Y chromosome (mLOY) in circulating leukocytes is the most frequently detected age-related chromosomal mosaic event in men. Current mLOY detection approaches use genotyping arrays and employ a phase-based approach that identifies B allele frequency (BAF) deviations in the pseudo-autosomal region (PAR) shared between the X and Y chromosome. As some widely used genotyping arrays lack sufficient probe coverage of the PAR, methods for accurately measuring mLOY utilizing the median log2 R ratio across the male-specific region of Y chromosome (mLRR_Y) are needed for detecting mLOY on these platforms. Results We derived a formula from mLRR_Y to estimate the cellular fraction (CF) of cells with Y loss and validated the approach, finding high alignment with the CF estimation from female data and lab-generated qPCR data (R2 = 0.98). Additionally, we compared the correlation between phase-based BAF and mLRR_Y methods for CF estimation, achieving a high correlation with R2 > 0.80. Conclusion Although mLRR_Y is a noisier metric for mosaic chromosomal alteration detection relative to BAF, we demonstrate mLRR_Y across non-PAR variants can accurately estimate mLOY CF, especially for high CF mLOY.

MOTIVATION: The Division of Cancer Epidemiology and Genetics (DCEG) and the Division of Cancer Prevention (DCP) at the National Cancer Institute (NCI) have recently generated genome-wide association study (GWAS) data for multiple traits in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Genomic Atlas project. The GWAS included 110 000 participants. The dissemination of the genetic association data through a data portal called GWAS Explorer, in a manner that addresses the modern expectations of FAIR reusability by data scientists and engineers, is the main motivation for the development of the open-source JavaScript software development kit (SDK) reported here. RESULTS: The PLCO GWAS Explorer resource relies on a public stateless HTTP application programming interface (API) deployed as the sole backend service for both the landing page's web application and third-party analytical workflows. The core PLCOjs SDK is mapped to each of the API methods, and also to each of the reference graphic visualizations in the GWAS Explorer. A few additional visualization methods extend it. As is the norm with web SDKs, no download or installation is needed and modularization supports targeted code injection for web applications, reactive notebooks (Observable) and node-based web services. AVAILABILITY AND IMPLEMENTATION: code at https://github.com/episphere/plco; project page at https://episphere.github.io/plco.

Text embedded in images provides important semantic information about a scene and its content. Detecting text in an unconstrained environment is a challenging task because of the many fonts, sizes, backgrounds, and alignments of the characters. We present a novel attention model for detecting arbitrary oriented and curved scene text. Inspired by the attention mechanisms in the human visual system, our model utilizes a spatial glimpse network to processes the attended area and deploys a recurrent neural network that aggregates the information over time to determine the attention movement. Combining this with an off-the-shelf region proposal method, the model achieves the state-of-the-art performance on the highly cited ICDAR2013 dataset, and the MSRA-TD500 dataset which contains arbitrary oriented text.

Automatic citation recommendation can be very useful for authoring a paper and is an AI-complete problem due to the challenge of bridging the semantic gap between citation context and the cited paper. It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context. To help with this problem, we propose a novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers. The probability of citing a paper given a citation context is estimated by training a multi-layer neural network. We implement and evaluate our model on the entire CiteSeer dataset, which at the time of this work consists of 10,760,318 citation contexts from 1,017,457 papers. We show that the proposed model significantly outperforms other state-of-the-art models in recall, MAP, MRR, and nDCG.

A prerequisite relation describes a basic relation among concepts in cognition, education and other areas.However, as a semantic relation, it has not been well studied in computational linguistics.We investigate the problem of measuring prerequisite relations among concepts and propose a simple link-based metric, namely reference distance (RefD), that effectively models the relation by measuring how differently two concepts refer to each other.Evaluations on two datasets that include seven domains show that our single metric based method outperforms existing supervised learning based methods.

When we write or prepare to write a research paper, we always have appropriate references in mind. However, there are most likely references we have missed and should have been read and cited. As such a good citation recommendation system would not only improve our paper but, overall, the efficiency and quality of literature search.

Oriented patterns, e.g. fingerprints, consist of smoothly varying flow-like patterns, together with important singular points (i.e. cores and deltas) where the orientation changes abruptly. Gabor filters and anisotropic diffusion methods have been widely used to enhance oriented patterns. However, none of them can well cope with regions of varying curvatures or regions surrounding singular points. By incorporating the ridge curvatures and the singularities into the diffusion model, we propose a new diffusion method to better exploit the global characteristics of oriented patterns. Specifically, we first locate the singular points, and regularize the estimated orientation field by using a singularity driven nonlinear diffusion process. We then enhance the oriented patterns by applying an oriented diffusion process which is driven by the curvature and singularity. Experiments on synthetic data and real fingerprint images validated that the proposed method is capable of consistently enhancing oriented patterns while well preserving the ridge structures in singular regions.

This paper presents a sign language recognition system which consists of three modules: model-based hand tracking, feature extraction, and gesture recognition using a 3-D Hopfield neural network. In the experiments, we illustrate that this system can recognize 15 different gestures accurately.