In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover category. In the recent past, convolutional neural network (CNN)-based HSI classification methods have ...greatly improved performance due to their superior ability to represent features. However, these methods have limited ability to obtain deep semantic features, and as the layer's number increases, computational costs rise significantly. The transformer framework can represent high-level semantic features well. In this article, a spectral-spatial feature tokenization transformer (SSFTT) method is proposed to capture spectral-spatial features and high-level semantic features. First, a spectral-spatial feature extraction module is built to extract low-level features. This module is composed of a 3-D convolution layer and a 2-D convolution layer, which are used to extract the shallow spectral and spatial features. Second, a Gaussian weighted feature tokenizer is introduced for features transformation. Third, the transformed features are input into the transformer encoder module for feature representation and learning. Finally, a linear layer is used to identify the first learnable token to obtain the sample label. Using three standard datasets, experimental analysis confirms that the computation time is less than other deep learning methods and the performance of the classification outperforms several current state-of-the-art methods. The code of this work is available at https://github.com/zgr6010/HSI_SSFTT for the sake of reproducibility.
Generative adversarial network (GANs) is one of the most important research avenues in the field of artificial intelligence, and its outstanding data generation capacity has received wide attention. ...In this paper, we present the recent progress on GANs. First, the basic theory of GANs and the differences among different generative models in recent years were analyzed and summarized. Then, the derived models of GANs are classified and introduced one by one. Third, the training tricks and evaluation metrics were given. Fourth, the applications of GANs were introduced. Finally, the problem, we need to address, and future directions were discussed.
Prescription drugs are instrumental to managing and preventing chronic disease. Recent changes in US prescription drug cost sharing could affect access to them.
To synthesize published evidence on ...the associations among cost-sharing features of prescription drug benefits and use of prescription drugs, use of nonpharmaceutical services, and health outcomes.
We searched PubMed for studies published in English between 1985 and 2006.
Among 923 articles found in the search, we identified 132 articles examining the associations between prescription drug plan cost-containment measures, including co-payments, tiering, or coinsurance (n = 65), pharmacy benefit caps or monthly prescription limits (n = 11), formulary restrictions (n = 41), and reference pricing (n = 16), and salient outcomes, including pharmacy utilization and spending, medical care utilization and spending, and health outcomes.
Increased cost sharing is associated with lower rates of drug treatment, worse adherence among existing users, and more frequent discontinuation of therapy. For each 10% increase in cost sharing, prescription drug spending decreases by 2% to 6%, depending on class of drug and condition of the patient. The reduction in use associated with a benefit cap, which limits either the coverage amount or the number of covered prescriptions, is consistent with other cost-sharing features. For some chronic conditions, higher cost sharing is associated with increased use of medical services, at least for patients with congestive heart failure, lipid disorders, diabetes, and schizophrenia. While low-income groups may be more sensitive to increased cost sharing, there is little evidence to support this contention.
Pharmacy benefit design represents an important public health tool for improving patient treatment and adherence. While increased cost sharing is highly correlated with reductions in pharmacy use, the long-term consequences of benefit changes on health are still uncertain.
Objective:
To investigate the effectiveness and safety of Baduanjin training on the cognitive function in stroke survivors with cognitive impairment.
Design:
A randomized, two-arm parallel controlled ...trial with allocation concealment and assessors blinding.
Setting:
Community centre of Fuzhou city, China.
Subjects:
A total of 48 participants were recruited and randomly allocated into the Baduanjin exercise intervention or control group.
Interventions:
The control group maintained original medication and rehabilitation treatment. The Baduanjin training group received 24-week Baduanjin training with a frequency of three days a week and 40 minutes a day based on original medication and rehabilitation treatment.
Main outcome measures:
The primary outcome was global cognitive function. Secondary outcome measures included the specific domains of cognition (i.e. memory, processing speed, execution, attention and visuospatial ability) and activities daily living.
Results:
In total, 41 (Baduanjin n = 22, control n = 19) participants completed 24-week treatment and data collection. Mean differences between groups at 24-week treatment were statistically significant for global cognitive function (MoCA: 2.54 (0.91 to 4.16)), execution (TMT-A: −42.4 (−75.0 to −9.8); TMT-B: −71.3 (−130.6 to −12.1)), memory (immediate recall: 2.11 (0.49 to 3.73); short-term delayed recognition: 2.47 (0.58 to 4.35) and long-term delayed recognition: 1.68(0.18 to 3.17)), attention (response time of alertness: −245.5 (−387 to −104)) and activities of daily living (modified Barthel Index).
Conclusion:
Regular Baduanjin training is associated with less loss of cognitive function in patients after stroke.
In the convolutional neural network, the precise segmentation of small-scale objects and object boundaries in remote sensing images is a great challenge. As the model gets deeper, low-level features ...with geometric information and high-level features with semantic information cannot be obtained simultaneously. To alleviate this problem, a successive pooling attention network (SPANet) was proposed. The SPANet mainly consists of ResNet50 as the backbone, successive pooling attention module (SPAM), and feature fusion module (FFM). Specifically, the SPANet uses two parallel branches to extract high-level features by ResNet50 and low-level features by the first 11 layers of ResNet50. Then, both the high- and low-level features are fed to the SPAM, which is mainly composed of a successive pooling operator and a self-attention submodule, for further extracting deeper multiscale and salient features. In addition, the low- and high-level features after the SPAM are fused by the FFM to achieve the complementarity of spatial and geometric information. This fusion module alleviates the problem of the accurate segmentation of object edges. Finally, the high-level features and enhanced low-level features of the two branches are fused to obtain the final prediction results. Experiments show that the proposed SPANet achieves a good segmentation effect compared with other models on two remotely sensed datasets.
Image set classification has drawn increasing attention and it has been widely applied to many real-life domains. Due to the existence of multiple images in a set, which contain various view ...appearance changes, image set classification is a rather challenging task. One potential solution is to learn powerful representations from multiple images to decrease the intra-class diversity and enlarge the inter-class separation. In this paper, we propose an optimal discriminative feature and dictionary learning (ODFDL) method, which attempts to learn a feature mapping matrix and a dictionary such that in the mapped feature space the inter-class sparse reconstruction error of data is maximized and the intra-class sparse reconstruction error is minimized. This learning strategy enforces the learned sparse representations from image sets have large inter-class separation and small intra-class scatter. Furthermore, to better exploit the non-linear information of data from different image sets, we also present two non-linear ODFDL methods, termed Kernel-ODFDL and Hierarchy-ODFDL to further improve the classification performance. Experiments on five commonly used image sets exhibit that our approaches are comparable with many state-of-the-arts.
Kernel methods, e.g., composite kernels (CKs) and spatial-spectral kernels (SSKs), have been demonstrated to be an effective way to exploit the spatial-spectral information nonlinearly for improving ...the classification performance of hyperspectral image (HSI). However, these methods are always conducted with square-shaped window or superpixel techniques. Both techniques are likely to misclassify the pixels that lie at the boundaries of class, and thus a small target is always smoothed away. To alleviate these problems, in this paper, we propose a novel patch-based low rank component induced spatial-spectral kernel method, termed LRCISSK, for HSI classification. First, the latent low-rank features of spectra in each cubic patch of HSI are reconstructed by a low rank matrix recovery (LRMR) technique, and then, to further explore more accurate spatial information, they are used to identify a homogeneous neighborhood for the target pixel (i.e., the centroid pixel) adaptively. Finally, the adaptively identified homogenous neighborhood which consists of the latent low-rank spectra is embedded into the spatial-spectral kernel framework. It can easily map the spectra into the nonlinearly complex manifolds and enable a classifier (e.g., support vector machine, SVM) to distinguish them effectively. Experimental results on three real HSI datasets validate that the proposed LRCISSK method can effectively explore the spatial-spectral information and deliver superior performance with at least 1.30% higher OA and 1.03% higher AA on average when compared to other state-of-the-art classifiers.
This letter presents a novel mixed noise (i.e., Gaussian, impulse, stripe noises, or dead lines) reduction method for hyperspectral image (HSI) by utilizing low-rank representation (LRR) on spectral ...difference image. The proposed method is based on the assumption that all spectra in the spectral difference space of HSI lie in the same low-rank subspace. The LRR on the spectral difference space was exploited by nuclear norm of difference image along the spectral dimension. It showed great potential in removing structured sparse noise (e.g., stripes or dead lines located at the same place of each band) and heavy Gaussian noise. To simultaneously solve the proposed model and reduce computational load, alternating direction method of multipliers was utilized to achieve robust reconstruction. The experimental results on both simulated and real HSI data sets validated that the proposed method outperformed many state-of-the-art methods in terms of quantitative assessment and visual quality.
The classification of remote sensing scenes is always a challenging task due to the large range of variation in the data, high spatial resolutions and complex backgrounds. In the analysis and ...interpretation of satellite images, remote sensing scene classification plays an important role. Most methods use CNNs to realize classification; however, common CNNs cannot accurately suppress background information while capturing key local characteristics of satellite images. In this paper, we propose a scene classification algorithm for remote sensing images using the hybrid attention improvement network CrossX in remote sensing scenarios.A new hybrid attention module, consisting of a spatial attention (SA) module and a channel attention (CA) module, is introduced to fully extract salient features of the target. Specifically, the spatial attention network aggregates features along two spatial directions to better understand the spatial relationships in the scene. In addition, a channel attention network using one-dimensional convolution is proposed to extract image features with a focus on capturing dependencies on channels. Distinctive characteristics of different semantic parts can be noticed from the original features, compensating for the lack of semantic information in the spatial dimension, and more efficient feature representations can be obtained by fusing these features. The proposed method has been proven on the following remote sensing scene datasets: UC Merced, AID and NWPU Resiscs 45. ResNet34, as the backbone network, achieves 99.25%, 96.52% and 96.9% classification accuracies on the test sets. The experimental results show that our method outperforms current representative scene classifiers on both AID and NWPU, and its performance on UC Merced is comparable to that of state-of-the- art models. The proposed method focuses on improving the ability of the attention mechanism to extract features and obtain an efficient target feature representation, which can be used in computer vision tasks related to the extraction of features and the classification of remote sensing scenes.
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