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To identify various oil well working conditions more accurately and practically from massive image data collected by multiple measured information sources of sucker-rod pumping wells, this paper proposes a working condition recognition method with three key aspects: curvelet pooling optimization technology, multi-source attention mechanism fusion feature extraction technology, and multi-source semi-supervised classification deep learning. Specifically: (a) Curvelet pooling optimization technology. We introduce the second-generation curvelet transform into the ResNet-50 pooling layer and adopt a collaborative learning pooling strategy of low-frequency and high-frequency information from the raw data decomposed via curvelet transform instead of max-pooling. This enhances the neural network’s capability to capture detailed features of complex image data. (b) Multi-source attention mechanism fusion feature extraction technology. We selected two information sources: measured ground dynamometer cards and measured electrical power cards. The multi-head self-attention mechanism enables interactive complementarity between curvelet-decomposed image data from each information source, while achieving dynamic weighted fusion of the interactive complementary data via the adaptive attention mechanism. This process yields optimal global feature representations of multi-source fused data. (c) Multi-source semi-supervised classification deep learning. By integrating multi-source fused feature data with a semi-supervised classification algorithm based on the dual strategy of dynamic adjustment of pseudo-label confidence and self-adaptive class fairness regularization, the method leverages abundant multi-source unlabeled samples to improve model classification performance and generalization ability under limited labeled training samples. This further enhances the accuracy and practicality of condition recognition. Experimental data were collected from a high-pressure, low-permeability, thin oil reservoir block in an oilfield in China. Extensive experiments demonstrate that the proposed method efficiently processes measured information source data in the sucker-rod pumping production system, improves the performance of traditional deep learning frameworks, explores the intrinsic correlations among multiple measured information source data of oil wells, and utilizes massive unlabeled working condition data to enhance the working condition recognition effect and engineering practicability with a minimal number of labeled samples. Code is available at https://github.com/Yoick/AMMFFECP .

Graph neural networks (GNN) have achieved remarkable success in various domains, yet incomplete node attribute data can significantly impair their performance. Graph completion learning (GCL) methods have been developed to address this issue, aiming to reconstruct missing node attributes based on existing structural relationships. However, the accuracy of these reconstructions is highly dependent on the quality of the initial graph structure, which often contains errors and inaccuracies. This paper proposes the evolving graph structure (EGS) framework for semi-supervised node classification with missing attributes. EGS dynamically reconstructs the attributes of the nodes and updates the graph structure through an alternating optimization approach. Specifically, we introduce a Dirichlet Energy function with dual constraints to formulate the objective function, which jointly optimizes node structure relationships and attribute reconstruction. Extensive experiments on five benchmark datasets, with different missing rates, and with seven GNN variants demonstrate the effectiveness of EGS, achieving state-of-the-art performance compared to existing GCL methods.

The complex and variable operating conditions of sucker-rod pumping wells pose a significant challenge for the timely and accurate identification of oil well operating conditions. Effective deep learning based on measured multi-source data obtained from the sucker-rod pumping well production site offers a promising solution to the challenge. However, existing deep learning-based operating condition recognition methods are constrained by several factors: the limitations of traditional operating condition recognition methods based on single-source and multi-source data, the need for large amounts of labeled data for training, and the high robustness requirement for recognizing complex and variable data. Therefore, we propose a semi-supervised class-incremental sucker-rod pumping well operating condition recognition method based on measured multi-source data distillation. Firstly, we select measured ground dynamometer cards and measured electrical power cards as information sources, and construct the graph neural network teacher models for data sources, and dynamically fuse the prediction probability of each teacher model through the Squeeze-and-Excitation attention mechanism. Then, we introduce a multi-source data distillation loss. It uses Kullback-Leibler (KL) divergence to measure the difference between the output logic of the teacher and student models. This helps reduce the forgetting of old operating condition category knowledge during class-incremental learning. Finally, we employ a multi-source semi-supervised graph classification method based on enhanced label propagation, which improves the label propagation method through a logistic regression classifier. This method can deeply explore the potential relationship between labeled and unlabeled samples, so as to further enhance the classification performance. Extensive experimental results show that the proposed method achieves superior recognition performance and enhanced engineering practicality in real-world class-incremental oil extraction production scenarios with complex and variable operating conditions.

The paraventricular thalamus is a relay station connecting brainstem and hypothalamic signals that represent internal states with the limbic forebrain that performs associative functions in emotional contexts. Zhu et al. found that paraventricular thalamic neurons represent multiple salient features of sensory stimuli, including reward, aversiveness, novelty, and surprise. The nucleus thus provides context-dependent salience encoding. The thalamus gates sensory information and contributes to the sleep-wake cycle through its interactions with the cerebral cortex. Ren et al. recorded from neurons in the paraventricular thalamus and observed that both population and single-neuron activity were tightly coupled with wakefulness. Science , this issue p. 423 , p. 429 Neurons in the paraventricular thalamic nucleus are both necessary and sufficient for maintaining arousal. Clinical observations indicate that the paramedian region of the thalamus is a critical node for controlling wakefulness. However, the specific nucleus and neural circuitry for this function remain unknown. Using in vivo fiber photometry or multichannel electrophysiological recordings in mice, we found that glutamatergic neurons of the paraventricular thalamus (PVT) exhibited high activities during wakefulness. Suppression of PVT neuronal activity caused a reduction in wakefulness, whereas activation of PVT neurons induced a transition from sleep to wakefulness and an acceleration of emergence from general anesthesia. Moreover, our findings indicate that the PVT–nucleus accumbens projections and hypocretin neurons in the lateral hypothalamus to PVT glutamatergic neurons’ projections are the effector pathways for wakefulness control. These results demonstrate that the PVT is a key wakefulness-controlling nucleus in the thalamus.

Co-training is a major multi-view learning paradigm that alternately trains two classifiers on two distinct views and maximizes the mutual agreement on the two-view unlabeled data. Traditional co-training algorithms usually train a learner on each view separately and then force the learners to be consistent across views. Although many co-trainings have been developed, it is quite possible that a learner will receive erroneous labels for unlabeled data when the other learner has only mediocre accuracy. This usually happens in the first rounds of co-training, when there are only a few labeled examples. As a result, co-training algorithms often have unstable performance. In this paper, Hessian-regularized co-training is proposed to overcome these limitations. Specifically, each Hessian is obtained from a particular view of examples; Hessian regularization is then integrated into the learner training process of each view by penalizing the regression function along the potential manifold. Hessian can properly exploit the local structure of the underlying data manifold. Hessian regularization significantly boosts the generalizability of a classifier, especially when there are a small number of labeled examples and a large number of unlabeled examples. To evaluate the proposed method, extensive experiments were conducted on the unstructured social activity attribute (USAA) dataset for social activity recognition. Our results demonstrate that the proposed method outperforms baseline methods, including the traditional co-training and LapCo algorithms.

As the most common form of dementia, Alzheimer’s disease (AD) is characterized by progressive cognitive impairments and constitutes a major social burden. Currently, the invasiveness and high costs of tests have limited the early detection and intervention of the disease. As a unique window of the brain, retinal changes can reflect the pathology of the brain. In this review, we summarize current understanding of retinal structures in AD, mild cognitive impairment (MCI) and preclinical AD, focusing on neurodegeneration and microvascular changes measured using optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) technologies. The literature suggests that the impairment of retinal microvascular network and neural microstructure exists in AD, MCI and even preclinical AD. These findings provide valuable insights into a better understanding of disease pathogenesis and demonstrate that retinal changes are potential biomarkers for early diagnosis of AD and monitoring of disease progression.

At present, nonparametric subspace classifiers, such as collaborative representation-based classification (CRC) and sparse representation-based classification (SRC), are widely used in many pattern-classification and -recognition tasks. Meanwhile, the spatial pyramid matching (SPM) scheme, which considers spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper, we first introduce the spatial pyramid matching scheme to remote-sensing (RS)-image scene-classification tasks to improve performance. Then, we propose a weighted spatial pyramid matching collaborative-representation-based classification method, combining the CRC method with the weighted spatial pyramid matching scheme. The proposed method is capable of learning the weights of different subregions in representing an image. Finally, extensive experiments on several benchmark remote-sensing-image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm when compared with state-of-the-art approaches.

In recent years, the application of deep learning has achieved a huge leap in the performance of remote sensing image super-resolution (SR). However, most of the existing SR methods employ bicubic downsampling of high-resolution (HR) images to obtain low-resolution (LR) images and use the obtained LR and HR images as training pairs. This supervised method that uses ideal kernel (bicubic) downsampled images to train the network will significantly degrade performance when used in realistic LR remote sensing images, usually resulting in blurry images. The main reason is that the degradation process of real remote sensing images is more complicated. The training data cannot reflect the SR problem of real remote sensing images. Inspired by the self-supervised methods, this paper proposes a cross-dimension attention guided self-supervised remote sensing single-image super-resolution method (CASSISR). It does not require pre-training on a dataset, only utilizes the internal information reproducibility of a single image, and uses the lower-resolution image downsampled from the input image to train the cross-dimension attention network (CDAN). The cross-dimension attention module (CDAM) selectively captures more useful internal duplicate information by modeling the interdependence of channel and spatial features and jointly learning their weights. The proposed CASSISR adapts well to real remote sensing image SR tasks. A large number of experiments show that CASSISR has achieved superior performance to current state-of-the-art methods.

Support vector data description (SVDD) has been widely applied to batch process fault detection. However, it often performs poorly, especially when incipient faults occur, because it only considers the shallow data feature and omits the probabilistic information of features. In order to provide better monitoring performance on incipient faults in batch processes, an improved SVDD method, called deep probabilistic SVDD (DPSVDD), is proposed in this work by integrating the convolutional autoencoder and the probability-related monitoring indices. For mining the hidden data features effectively, a deep convolutional features extraction network is designed by a convolutional autoencoder, where the encoder outputs and the reconstruction errors are used as the monitor features. Furthermore, the probability distribution changes of these features are evaluated by the Kullback-Leibler (KL) divergence so that the probability-related monitoring indices are developed for indicating the process status. The applications to the benchmark penicillin fermentation process demonstrate that the proposed method has a better monitoring performance on the incipient faults in comparison to the traditional SVDD methods.

Deep learning has recently attracted extensive attention and developed significantly in remote sensing image super-resolution. Although remote sensing images are composed of various scenes, most existing methods consider each part equally. These methods ignore the salient objects (e.g., buildings, airplanes, and vehicles) that have more complex structures and require more attention in recovery processing. This paper proposes a saliency-guided remote sensing image super-resolution (SG-GAN) method to alleviate the above issue while maintaining the merits of GAN-based methods for the generation of perceptual-pleasant details. More specifically, we exploit the salient maps of images to guide the recovery in two aspects: On the one hand, the saliency detection network in SG-GAN learns more high-resolution saliency maps to provide additional structure priors. On the other hand, the well-designed saliency loss imposes a second-order restriction on the super-resolution process, which helps SG-GAN concentrate more on the salient objects of remote sensing images. Experimental results show that SG-GAN achieves competitive PSNR and SSIM compared with the advanced super-resolution methods. Visual results demonstrate our superiority in restoring structures while generating remote sensing super-resolution images.