Invariant Feature Research Articles

Multi-source domain transfer learning with small sample learning for thermal runaway diagnosis of lithium-ion battery

Data-driven thermal runaway diagnosis based on small amounts of thermal runaway data often struggles to produce satisfactory accuracy. However, in actual application scenarios, obtaining real thermal runaway data has a high cost. To this end, we propose a diagnostic method for multi-source domain transfer learning with few-shot learning (MDTL-FSL), which combines the ideas of small sample learning and adversarial learning. Use multiple different but related thermal runaway case data to obtain common diagnostic knowledge to achieve thermal runaway diagnosis. First of all, in order to avoid negative migration of the algorithm model caused by large differences in data distribution between multi-source domains, a data distribution difference measurement method under segmented pressure differences is proposed. This measurement method is simple and effective from multi-source domains. Select the source domain with a small difference in data distribution from the target domain. Secondly, the adversarial learning idea is integrated into multi-source domain transfer learning to learn temporal invariant features. Then, due to the small number of samples in each source domain in the multi-source domain under the thermal runaway scenario, a source domain reorganization mechanism was designed to find the decision boundary based on meta-learning ideas to achieve small-sample learning in the multi-source domain. Finally, we used cells produced by different battery manufacturers to trigger thermal runaway. By comparison and verification with other data-driven algorithms, the results show that the MDTL-FSL model proposed in this article has higher accuracy. At the same time, we use batteries of different types and capacities to trigger thermal runaway under different working conditions. The MDTL-FSL algorithm can issue early warnings before thermal runaway occurs, thereby effectively ensuring the safe operation of the energy storage system.

Clip-GCN: an adaptive detection model for multimodal emergent fake news domains

Emergent news is characterized by few labels, and news detection methods that rely on a large number of labels are difficult to apply to learned features for emerging events and are ineffective in coping with less labeled emergent news detection. To address the challenge of limited labeled data, this study first establishes a scenario for detecting breaking news, ensuring that the domain of detecting events is distinct from the domain of historical events. Secondly, we propose the Clip-GCN multimodal fake news detection model. The model utilizes the Clip pre-training model to perform joint semantic feature extraction of image-text information, with text information as the supervisory signal, which solves the problem of semantic interaction between modalities. Meanwhile, considering the domain attributes of news, the model is trained to extract inter-domain invariant features through Adversarial Neural Network ideation, and intra-domain knowledge information is utilized through graph convolutional networks (GCN) to detect emergent news. Through an extensive number of experiments on Chinese and English datasets from two major social media platforms, Weibo and Twitter, it is demonstrated that the model proposed in this paper can accurately screen multimodal emergent news on social media with an average accuracy of 88.7%. The contribution of this study lies not only in the improvement of model performance but also in the proposal of a solution for the challenges posed by limited labels and multimodal breaking news. This provides robust support for research in related fields.

LGD: A fast place recognition method based on the fusion of local and global descriptors

Place recognition, which is also known as loop detection, has been one of the main problems in the field of robot navigation, and it is crucial in realizing high-performance simultaneous localization and mapping (SLAM). Most of the existing methods directly construct local or global descriptors with point clouds, ignoring the offset of different visits in the same place. To overcome the current limitations, this study emphasizes the importance of point cloud features in improving the environment recognition precision and proposes an innovative framework that combines local features that are spatially invariant and prominent descriptive global features as a representation of a laser scanner frame. The feature center of the laser point cloud is obtained by computing local feature points with spatial invariance, which improves the offset between the original point cloud and the feature center. Furthermore, a coarse-to-fine hierarchical recognition strategy is developed to improve the efficiency of place recognition. An evaluation with different offsets and synthesis is conducted on the publicly available KITTI and self-developed CHDloop datasets. The experimental results show that the proposed method can improve time efficiency by more than 18% compared to the SC and ISC, and there are also significant improvements in the recall rate and accuracy in slight lane-change scenarios. Moreover, the proposed method can obtain rich lane-change loops in moderate and large lane-change scenarios where the SC and ISC fail to obtain effective loops. The proposed method can be used as a lightweight and interpretable approach for place recognition, which not only can be quickly deployed in any SLAM framework but can also efficiently handle more complex place recognition issues in urban environments.

Application of facial expression recognition technology based on feature fusion in teaching

With the development of artificial intelligence technology, the digital transformation of student-oriented education becomes particularly important. How to promote real-time interaction between teachers and students in the classroom is an urgent issue which is needed to pay attention to. Based on the facial expression features of students in a classroom, this paper analyzes the changes in angles between facial expression feature points using Dlib. Additionally, this paper proposes a novel algorithm for extracting variable scale template edge trend features. The algorithm adaptively processes the template based on the edge trend features of expression feature points, and use the proposed template slope normalization algorithm to achieve multi-scale template edge trend extraction. Then, DNN are used to recognize different listening expressions. The experimental results show that the proposed algorithm has faster recognition speed and better robustness when applied to classroom expression recognition. By identifying students’ class status to remind teachers to adjust their class progress, the goal of improving classroom learning effectiveness is achieved

Design of Privacy Protection System for Target Search Based on Selection Algorithm and Sustainable Iteration Index

The data self-destruct method based on privacy cloud proposed in this paper can realize the automatic destruction of cloud data after expiration, and based on this method, a self-destruct image protection system is implemented. This project intends to combine the adaptive scale invariant Feature conversion (SIFT) extraction of ciphertext images with binary SIFT to ensure the confidentiality of the images uploaded to the server in the ciphertext space. The security protection of data is realized using Paillier homomorphic encryption technology. The feature of SIFT is extracted from the encrypted region, and it is expressed in binary form to reduce the amount of computation and memory overhead. The experimental results show that this method can effectively prevent the automatic destruction of images after expiration, so as to ensure that the user's personal information is not leaked.

Frontal hypometabolism in the diagnosis of progressive supranuclear palsy clinical variants.

Frontal hypometabolism on FDG-PET is observed in progressive supranuclear palsy (PSP), although it is unclear whether it is a feature of all PSP clinical variants and hence whether it is a useful diagnostic feature. We aimed to compare the frequency, severity, and pattern of frontal hypometabolism across PSP variants and determine whether frontal hypometabolism is related to clinical dysfunction. Frontal hypometabolism in prefrontal, premotor, and sensorimotor cortices was visually graded on a 0-3 scale using CortexID Z-score images in 137 PSP patients. Frontal asymmetry was recorded. Severity scores were used to categorize patients as premotor-predominant, prefrontal-predominant, sensorimotor-predominant, mixed-predominance, or no regional predominance. Frontal ratings were compared across PSP clinical variants, and Spearman correlations were used to assess relationships with the Frontal Assessment Battery (FAB). 97% showed evidence of frontal hypometabolism which was most common (100%) in the speech-language (PSP-SL), corticobasal (PSP-CBS), and frontal (PSP-F) variants and least common in the progressive gait freezing (PSP-PGF) variant (73%). PSP-SL and PSP-CBS showed more severe hypometabolism than Richardson's syndrome (PSP-RS), Parkinsonism (PSP-P), and PSP-PGF. A premotor-predominant pattern was most common in PSP-SL and PSP-CBS, with more mixed patterns in the other variants. Hypometabolism was most commonly asymmetric in PSP-SL, PSP-P, PSP-F and PSP-CBS. Worse hypometabolism in nearly all frontal regions correlated with worse scores on the FAB. Frontal hypometabolism is a common finding in PSP, although it varies in severity and pattern across PSP variants and will likely be the most diagnostically useful in PSP-SL and PSP-CBS.

Fault transfer diagnosis of rolling bearings across different devices via multi-domain information fusion and multi-kernel maximum mean discrepancy

The current deep learning-based intelligent diagnosis algorithms depend on large amounts of well-labeled data, but they may not perform well in engineering practice where the fault data available from a single device is limited. The significant difference in data distribution between different devices makes it more difficult to transfer diagnosis across devices. Therefore, aiming at the problem that the great difference in the data distribution of source and target domains in cross-device transfer diagnosis leads to the low diagnosis accuracy, this paper investigates a deep transfer network model based on multi-domain information fusion and multi-kernel maximum mean discrepancy (MK-MMD) for fault diagnosis of rolling bearings across different devices. Firstly, to address the problem that single-domain information is difficult to adequately characterize rolling bearing health status information of different devices, a multi-domain information feature extractor based on multi-attention mechanism is constructed to capture the important features of vibration signals in different transform domains. Secondly, the extracted multi-domain features are input into the bidirectional gated recurrent unit (BiGRU) network for feature fusion, and MK-MMD is used to narrow the distribution distance between the source domain and target domain to obtain domain invariant features, and then achieve the transfer diagnosis of rolling bearing faults between different devices. Finally, the method investigated is verified by rolling bearing fault transfer diagnosis tests between different devices, and the results show that the suggested method can adapt to the feature distribution between different domains, and improve the transfer diagnosis accuracy between different devices.

Research on trajectory control of multi‐degree‐of‐freedom industrial robot based on visual image

AbstractIn order to improve the trajectory control effect of multi‐degree‐of‐freedom industrial robots, this paper combines visual image technology to conduct research on trajectory control of multi‐degree‐of‐freedom industrial robots. Aiming at the problem of video segmentation under sudden illumination changes, this paper uses a Gaussian mixture model based on the global illumination function to adopt a variety of illumination invariant features, and proposes a scene segmentation algorithm suitable for sudden illumination changes. Moreover, this paper compares and verifies the algorithm from the subjective and objective perspectives through experiments, which shows that the algorithm in this paper can segment the scene more accurately even in the environment of sudden changes in illumination. In addition, the results of the accuracy test and the trajectory control test show that the research method of the multi‐degree‐of‐freedom industrial robot trajectory control based on the visual image proposed in this paper can effectively improve the trajectory control effect of the robot.

ACCURACY ASSESSMENT OF THE EFFECT OF DIFFERENT FEATURE DESCRIPTORS ON THE AUTOMATIC CO-REGISTRATION OF OVERLAPPING IMAGES

This research seeks to assess the effect of different selected feature descriptors on the accuracy of an automatic image registration scheme. Three different feature descriptors were selected based on their peculiar characteristics, and implemented in the process of developing the image registration scheme. These feature descriptors (Modified Harris and Stephens corner detector (MHCD), the Scale Invariant Feature Transform (SIFT) and the Speeded Up Robust Feature (SURF)) were used to automatically extract the conjugate points common to the overlapping image pairs used for the registration. Random Sampling Consensus (RANSAC) algorithm was used to exclude outliers and to fit the matched correspondences, Sum of Absolute Differences (SAD) which is a correlation-based feature matching metric was used for the feature match, while projective transformation function was used for the computation of the transformation matrix (T). The obtained overall result proved that the SURF algorithm outperforms the other two feature descriptors with an accuracy measure of -0.0009 pixels, while SIFT with a cumulative signed distance of 0.0328 pixels also proved to be more accurate than MHCD with a cumulative signed distance of 0.0457 pixels. The findings affirmed the importance of choosing the right feature descriptor in the overall accuracy of an automatic image registration scheme.

A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN

In recent years, the defect image classification method based on deep transfer learning has been widely explored and researched, and the task of source and target domains with the same painting defect image class has been solved successfully. However, in real applications, due to the complexity and uncertainty of ship painting conditions, it is very likely that there are unknown classes of painting defects, and the traditional deep learning model cannot identify a few classes, which leads to model overfitting and reduces its generalization ability. In this paper, a zero-shot Image classification method for ship painting defects based on IDATLWGAN is proposed to identify new unknown classes of defects in the target domain. The method is based on a deep convolutional neural network combined with adversarial transfer learning. First, a preprocessed ship painting defect dataset is used as input for the domain-invariant feature extractor. Then, the domain invariant feature extractor takes domain invariant features from the source and target domains. Finally, Defect discriminators and domain alignment discriminators are employed to classify the known categories of unlabeled defects and unknown categories of unlabeled defects in the target domain and to further reduce the distance between the edge distributions of the source and target domains. The experimental results show that the proposed model in this paper extracts a better distribution of invariant features in the source and target domains compared to other existing transfer learning models. It can successfully complete the migration task and accurately recognize the painting defects of known categories and new unknown categories, which is a perfect combination of intelligent algorithms and engineering practice.

LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection.

Compared with visible light images, thermal infrared images have poor resolution, low contrast, signal-to-noise ratio, blurred visual effects, and less information. Thermal infrared sports target detection methods relying on traditional convolutional networks capture the rich semantics in high-level features but blur the spatial details. The differences in physical information content and spatial distribution of high and low features are ignored, resulting in a mismatch between the region of interest and the target. To address these issues, we propose a local attention-guided Swin-transformer thermal infrared sports object detection method (LAGSwin) to encode sports objects' spatial transformation and orientation information. On the one hand, Swin-transformer guided by local attention is adopted to enrich the semantic knowledge of low-level features by embedding local focus from high-level features and generating high-quality anchors while increasing the embedding of contextual information. On the other hand, an active rotation filter is employed to encode orientation information, resulting in orientation-sensitive and invariant features to reduce the inconsistency between classification and localization regression. A bidirectional criss-cross fusion strategy is adopted in the feature fusion stage to enable better interaction and embedding features of different resolutions. At last, the evaluation and verification of multiple open-source sports target datasets prove that the proposed LAGSwin detection framework has good robustness and generalization ability.

Molecular and phenotypic characteristics of Bardet-Biedl syndrome in Chinese patients

BackgroundBardet-Biedl syndrome (BBS) is a type of non-motile ciliopathy. To date, 26 genes have been reported to be associated with BBS. However, BBS is genetically heterogeneous, with significant clinical overlap with other ciliopathies, which complicates diagnosis. Disability and mortality rates are high in BBS patients; therefore, it is urgent to improve our understanding of BBS. Thus, our study aimed to describe the genotypic and phenotypic spectra of BBS in China and to elucidate genotype–phenotype correlations.MethodsTwenty Chinese patients diagnosed with BBS were enrolled in this study. We compared the phenotypes of Chinese BBS patients in this study with those from other countries to analyze the phenotypic differences across patients worldwide. In addition, genotype–phenotype correlations were described for our cohort. We also summarized all previously reported cases of BBS in Chinese patients (71 patients) and identified common and specific genetic variants in the Chinese population.ResultsTwenty-eight variants, of which 10 are novel, in 5 different BBS-associated genes were identified in 20 Chinese BBS patients. By comparing the phenotypes of BBSome-coding genes (BBS2,7,9) with those of chaperonin-coding genes (BBS10,12), we found that patients with mutations in BBS10 and 12 had an earlier age of onset (1.10 Vs. 2.20, p < 0.01) and diagnosis (4.64 Vs. 13.17, p < 0.01), whereas patients with mutations in BBS2, 7, and 9 had a higher body mass index (28.35 Vs. 24.21, p < 0.05) and more vision problems (p < 0.05). Furthermore, in 91 Chinese BBS patients, mutations were predominant in BBS2 (28.89%) and BBS7 (15.56%), and the most frequent variants were in BBS2: c.534 + 1G > T (10/182 alleles) and BBS7: c.1002delT (7/182 alleles), marking a difference from the genotypic spectra of BBS reported abroad.ConclusionsWe recruited 20 Chinese patients with BBS for genetic and phenotypic analyses, and identified common clinical manifestations, pathogenic genes, and variants. We also described the phenotypic differences across patients worldwide and among different BBS-associated genes. This study involved the largest cohort of Chinese patients with BBS, and provides new insights into the distinctive clinical features of specific pathogenic variants.

Domain generalization for retinal vessel segmentation via Hessian-based vector field

Blessed by vast amounts of data, learning-based methods have achieved remarkable performance in countless tasks in computer vision and medical image analysis. Although these deep models can simulate highly nonlinear mapping functions, they are not robust with regard to the domain shift of input data. This is a significant concern that impedes the large-scale deployment of deep models in medical images since they have inherent variation in data distribution due to the lack of imaging standardization. Therefore, researchers have explored many domain generalization (DG) methods to alleviate this problem. In this work, we introduce a Hessian-based vector field that can effectively model the tubular shape of vessels, which is an invariant feature for data across various distributions. The vector field serves as a good embedding feature to take advantage of the self-attention mechanism in a vision transformer. We design paralleled transformer blocks that stress the local features with different scales. Furthermore, we present a novel data augmentation method that introduces perturbations in image style while the vessel structure remains unchanged. In experiments conducted on public datasets of different modalities, we show that our model achieves superior generalizability compared with the existing algorithms. Our code and trained model are publicly available at https://github.com/MedICL-VU/Vector-Field-Transformer.

Abstract LB393: Exosome based multiomics combined with cfDNA methylation reveals complementary signatures in blood based liquid biopsy that carry promise for minimally invasive colorectal cancer screening

Abstract Introduction: Colorectal cancer (CRC) ranks second in cancer morbidity, with approximately 60% of patients being diagnosed at an advanced stage. Here, we apply a multiomic strategy jointly leveraging plasma cell-free DNA (cfDNA) and multiple extracellular vesicle (EV)-derived analytes to enhance CRC diagnostic accuracy. Materials and Methods: Plasma was collected from 96 individuals, 48 CRC patients (stage II-IV) and 48 CRC-negative healthy controls. EVs and cfDNA were isolated from the same plasma sample to look at complementary information within the sample. We developed an EV RNA sequencing platform targeting mRNAs and long non-coding RNAs and sequenced to a depth of 50M reads per sample; cfDNA methylome profiling was sequenced to an equivalent depth. The Olink platform profiled proteins both directly from plasma and from isolated EVs. Expression of EV-derived RNA, splice variants, and proteins in addition to cfDNA methylation patterns were analyzed using Bio-Techne’s multiomic platform. Machine learning-based feature selection algorithms identified biomarker signatures from each analyte. Receiver-operator characteristic curves (ROC) were generated utilizing leave-one-out cross-validation of naïve Bayes classifier models to compute the area under the curve (AUC). Individual signatures were integrated to generate a multiomic classifier. Results: Differential gene expression (DEx) analysis identified ~1000 DEx genes with significant enrichment of CRC-relevant pathways. Splice variant analysis identified several genes previously implicated in CRC with differential isoform usage. Over 1.7 million differentially methylated bases were detected between CRC-positive and CRC-negative cohorts. Segmentation of the genome using out-of-bag reference data yielded ~1100 genomic segments, many of which overlap previously reported CRC biomarkers and identified unique information about the CRC status. Proteomic analysis identified several differentially abundant markers with high discriminatory power. Following feature selection analysis, AUCs obtained from top 10 features of RNA expression, splice variants, cfDNA methylation, and protein abundance with an integrative analysis identified a multi-analyte signature, with an overall AUC of 0.99. Conclusion: While biomarker signatures obtained from each analyte in this study resulted in effective separation of CRC status, the multiomic signature further improved the discriminatory power—underscoring the complementary nature of the signals. Thus, a multiomic biomarker discovery strategy leveraging cfDNA and EV cargo holds tremendous promise for minimally invasive screening and potential detection of early-stage cancers, which is currently under further investigation. Citation Format: Kyle Manning, Dulaney Miller, Yang Yang, Jeff Cole, Shuran Xing, Christopher Benway, Christian Ray, Sudipto Chakrabortty, Brian Haynes, Seth Yu, Johan Skog. Exosome based multiomics combined with cfDNA methylation reveals complementary signatures in blood based liquid biopsy that carry promise for minimally invasive colorectal cancer screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB393.

Empirical Evaluation of Microgrid Fault Identification Models from a Statistical Perspective

Microgrid fault identification models are developed via integration of extensive data collection, pre-processing of collected data, current &amp; voltage segmentation, feature representation, identification of variant feature sets, their classification &amp; post-processing operations. Existing models that perform microgrid fault identification are either highly complex, or cannot be applied for heterogeneous fault types. Moreover, these models also showcase large variance in terms of their qualitative &amp; quantitative performance levels. Due to these issues, it is difficult for researchers to identify optimum models for their performance-specific deployment use cases. To overcome these issues, a detailed review of different microgrid fault detection &amp; mitigation models in needed, which can evaluate their performance in terms of qualitative &amp; quantitative parameters. Thus, this text initially discusses characteristics of some of the recently proposed microgrid fault detection models in terms of their functional nuances, application specific advantages, deployment specific limitations, and context-specific future research scopes. After referring this discussion, it was observed that linear models that incorporate pattern recognition are highly useful for fault pre-emption and mitigation purposes. This text also compares these models in terms of their accuracy of detection, delay needed for fault identification, computational complexity, deployment cost, and scalability metrics.

Cross noise level PET denoising with continuous adversarial domain generalization

Objective. Performing positron emission tomography (PET) denoising within the image space proves effective in reducing the variance in PET images. In recent years, deep learning has demonstrated superior denoising performance, but models trained on a specific noise level typically fail to generalize well on different noise levels, due to inherent distribution shifts between inputs. The distribution shift usually results in bias in the denoised images. Our goal is to tackle such a problem using a domain generalization technique. Approach. We propose to utilize the domain generalization technique with a novel feature space continuous discriminator (CD) for adversarial training, using the fraction of events as a continuous domain label. The core idea is to enforce the extraction of noise-level invariant features. Thus minimizing the distribution divergence of latent feature representation for different continuous noise levels, and making the model general for arbitrary noise levels. We created three sets of 10%, 13%–22% (uniformly randomly selected), or 25% fractions of events from 97 18F-MK6240 tau PET studies of 60 subjects. For each set, we generated 20 noise realizations. Training, validation, and testing were implemented using 1400, 120, and 420 pairs of 3D image volumes from the same or different sets. We used 3D UNet as the baseline and implemented CD to the continuous noise level training data of 13%–22% set. Main results. The proposed CD improves the denoising performance of our model trained in a 13%–22% fraction set for testing in both 10% and 25% fraction sets, measured by bias and standard deviation using full-count images as references. In addition, our CD method can improve the SSIM and PSNR consistently for Alzheimer-related regions and the whole brain. Significance. To our knowledge, this is the first attempt to alleviate the performance degradation in cross-noise level denoising from the perspective of domain generalization. Our study is also a pioneer work of continuous domain generalization to utilize continuously changing source domains.

Causal inference for out‐of‐distribution recognition via sample balancing

AbstractImage classification algorithms are commonly based on the Independent and Identically Distribution (i.i.d.) assumption, but in practice, the Out‐Of‐Distribution (OOD) problem widely exists, that is, the contexts of images in the model predicting are usually unseen during training. In this case, existing models trained under the i.i.d. assumption are limiting generalisation. Causal inference is an important method to learn the causal associations which are invariant across different environments, thus improving the generalisation ability of the model. However, existing methods usually require partitioning of the environment to learn invariant features, which mostly have imbalance problems due to the lack of constraints. In this paper, we propose a balanced causal learning framework (BCL), starting from how to divide the dataset in a balanced way and the balance of training after the division, which automatically generates fine‐grained balanced data partitions in an unsupervised manner and balances the training difficulty of different classes, thereby enhancing the generalisation ability of models in different environments. Experiments on the OOD datasets NICO and NICO++ demonstrate that BCL achieves stable predictions on OOD data, and we also find that models using BCL focus more accurately on the foreground of images compared with the existing causal inference method, which effectively improves the generalisation ability.

DDAGCN: an unsupervised cross-domain identification method for tie rod bolt loosening in a rod-fastening rotor system under different working conditions

The cross-domain diagnosis of tie rod bolt loosening is essential for guaranteeing the healthy operation of rod-fastening rotor (RFR) systems. The unsupervised domain adaptation (UDA) method effectively alleviates the impact of domain discrepancy and has been applied for cross-domain diagnosis. Traditional UDA methods mainly focus on the marginal and conditional distributions with fixed weights to adapt the domain distribution discrepancy. However, the fixed distribution combination cannot satisfy the requirement of feature domain alignment under different working conditions, and the relative importance of the two distributions cannot be evaluated quantitatively. This paper proposes an improved dynamic distribution adaptive graph convolutional network (DDAGCN) for the cross-domain diagnosis of tie rod bolt loosening under different working conditions. This method can quantitatively evaluate the relative significance of each distribution in representing the distribution discrepancy. First, it combines the convolutional neural network and the graph convolutional network to extract the features in the graph structure by using the connection relationship between nodes, and realizes the full extraction of neighbourhood information of nodes. Then, the dynamic distribution adaptive alignment strategy is introduced to construct the dynamic linear combination of marginal and conditional distributions, so as to measure the distribution discrepancy between domains. Meanwhile, the domain adversarial module is combined to further reduce the domain gap and finally realize feature alignment. The extracted domain invariant features can effectively enhance the generalization ability and fault identification ability of the model. The case of the public bearing dataset verifies that the effectiveness and generalization ability of the proposed method for cross-domain fault diagnosis under different working conditions is superior to other compared methods. In addition, the identification ability of the proposed method for the degree of tie rod bolt loosening is verified by the self-made bolt loosening dataset of the RFR system.

Autosomal Dominant Spastic Paraplegia With Dysregulation of Bowel Function Associated With Heterozygous AP4S1 Gene Mutation: Case Report.

The aim of our study was to examine the genetic variants already described in hereditary spastic paraplegia in a family where 2 members had spasticity, dysregulation of sphincter function, and dyspraxia in the proband. The study included 2 members of a non-consanguineous family with spastic gait, sphincter abnormalities, and neuropsychological characteristics. Whole-exome sequencing was used in the proband and his mother, both diagnosed with hereditary spastic paraplegia, to identify the underlying genetic cause. We identified a heterozygous variant already known in AP4S1 NM_007077.3: c.289C>T p. (Arg97*) in both patients. The AP4S1 gene on the 14q12 chromosome is responsible for directing proteins from the trans-Golgi network to the endosomal-lysosomal system. Homozygous AP4S1 mutations can cause a severe autosomal recessive phenotype with spasticity and intellectual disability in infants (SPG52). Interpretation: For the first time, a heterozygous pathogenic variant of the AP4S1 gene was observed in symptomatic individuals with hereditary spastic paraplegia. The clinical features of this heterozygous variant of the AP4S1 gene have little overlap with the severe clinical recessive features of SPG52. In this study, we delineated a heterozygous AP4S1 phenotype characterized by spasticity, dysregulation of sphincter functions, and developmental coordination disorder characteristics. Our results provided arguments for heterozygous variant associations in AP4S1 with hereditary spastic paraplegia and expanded the clinical spectrum of A4-related diseases.

Contrastive Unfolding Deraining Network.

Due to the fact that the degradation of image quality caused by rain usually affects outdoor vision tasks, image deraining becomes more and more important. Focusing on the single image deraining (SID) task, in this article, we propose a novel Contrastive Unfolding DEraining Network (CUDEN), which combines the traditional iterative algorithm and deep network, exhibiting excellent performance and nice interpretability. CUDEN transforms the challenge of locating rain streaks into discovering rain features and defines the relationship between the image and feature domains in terms of mapping pairs. To obtain the mapping pairs efficiently, we propose a dynamic multidomain translation (DMT) module for decomposing the original mapping into sub-mappings. To enhance the feature extraction capability of networks, we also propose a new serial multireceptive field fusion (SMF) block, which extracts complex and variable rain features with convolution kernels of different receptive fields. Moreover, we are the first to introduce contrastive learning to the SID task and combine it with perceptual loss to propose a new contrastive perceptual loss (CPL), which is quite generalized and greatly helpful in identifying the appropriate gradient descent direction during training. Extensive experiments on synthetic and real-world datasets demonstrate that our proposed CUDEN outperforms the state-of-the-art (SOTA) deraining networks.