A New Technique for Multispectral and Panchromatic Image Fusion

AbstractIn this paper, a technique is presented for the fusion of Panchromatic (PAN) and low spatial resolution multispectral (MS) images to get high spatial resolution of the latter. In this technique, we apply PCA transformation to the MS image to obtain the principal component (PC) images. A NSCT transformation to PAN and each PC images for N level of decomposition. We use FOCC as criterion to select PC. And then, we use the relative entropy as criterion to reconstruct high-frequency detailed images. Finally, we apply inverse NSCT to selected PC's low-frequency approximate image and reconstructed high- frequency detailed images to obtain high spatial resolution MS image. The experimental results obtained by applying the proposed image fusion method indicate some improvements in the fusion performance

Prompt-Based Graph Convolution Adversarial Meta-Learning for Few-Shot Text Classification

Deep learning techniques have demonstrated significant advancements in the task of text classification. Regrettably, the majority of these techniques necessitate a substantial corpus of annotated data to achieve optimal performance. Meta-learning has yielded intriguing outcomes in few-shot learning tasks, showcasing its potential in advancing the field. However, the current meta-learning methodologies are susceptible to overfitting due to the mismatch between a small number of samples and the complexity of the model. To mitigate this concern, we propose a Prompt-based Graph Convolutional Adversarial (PGCA) meta-learning framework, aiming to improve the adaptability of complex models in a few-shot scenario. Firstly, leveraging prompt learning, we generate embedding representations that bridge the downstream tasks. Then, we design a meta-knowledge extractor based on a graph convolutional neural network (GCN) to capture inter-class dependencies through instance-level interactions. We also integrate the adversarial network architecture into a meta-learning framework to extend sample diversity through adversarial training and improve the ability of the model to adapt to new tasks. Specifically, we mitigate the impact of extreme samples by introducing external knowledge to construct a list of class prototype extensions. Finally, we conduct a series of experiments on four public datasets to demonstrate the effectiveness of our proposed method

Dynamic Spatio-Temporal Graph Fusion Convolutional Network for Urban Traffic Prediction

Urban traffic prediction is essential for intelligent transportation systems. However, traffic data often exhibit highly complex spatio-temporal correlations, posing challenges for accurate forecasting. Graph neural networks have demonstrated an outstanding ability in capturing spatial correlations and are now extensively applied to traffic prediction. However, many graph-based methods neglect the dynamic spatial features between road segments and the continuity of spatial features across adjacent time steps, leading to subpar predictive performance. This paper proposes a Dynamic Spatio-Temporal Graph Fusion Convolutional Network (DSTGFCN) to enhance the accuracy of traffic prediction. Specifically, we designed a dynamic graph fusion module without prior road spatial information, which extracts dynamic spatial information among roads from observed data. Subsequently, we fused the dynamic spatial features of the current time step and adjacent time steps to generate a dynamic graph for each time step. The graph convolutional gated recurrent network was employed to model the spatio-temporal correlations jointly. Additionally, residual connections were added to the model to enhance the ability to extract long-term temporal relationships. Finally, we conducted experiments on six publicly available traffic datasets, and the results demonstrated that DSTGFCN outperforms the baseline models with state-of-the-art predictive performance

Deep Reinforcement Learning-Based Resource Allocation for Cellular Vehicular Network Mode 3 with Underlay Approach

Vehicle-to-vehicle (V2V) communication has attracted increasing attention since it can improve road safety and traffic efficiency. In the underlay approach of mode 3, the V2V links need to reuse the spectrum resources preoccupied with vehicle-to-infrastructure (V2I) links, which will interfere with the V2I links. Therefore, how to allocate wireless resources flexibly and improve the throughput of the V2I links while meeting the low latency requirements of the V2V links needs to be determined. This paper proposes a V2V resource allocation framework based on deep reinforcement learning. The base station (BS) uses a double deep Q network to allocate resources intelligently. In particular, to reduce the signaling overhead for the BS to acquire channel state information (CSI) in mode 3, the BS optimizes the resource allocation strategy based on partial CSI in the framework of this article. The simulation results indicate that the proposed scheme can meet the low latency requirements of V2V links while increasing the capacity of the V2I links compared with the other methods. In addition, the proposed partial CSI design has comparable performance to complete CSI

Resource Allocation in V2X Communications Based on Multi-Agent Reinforcement Learning with Attention Mechanism

In this paper, we study the joint optimization problem of the spectrum and power allocation for multiple vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) users in cellular vehicle-to-everything (C-V2X) communication, aiming to maximize the sum rate of V2I links while satisfying the low latency requirements of V2V links. However, channel state information (CSI) is hard to obtain accurately due to the mobility of vehicles. In addition, the effective sensing of state information among vehicles becomes difficult in an environment with complex and diverse information, which is detrimental to vehicles collaborating for resource allocation. Thus, we propose a framework of multi-agent deep reinforcement learning based on attention mechanism (AMARL) to improve the V2X communication performance. Specifically, for vehicle mobility, we model the problem as a multi-agent reinforcement learning process, where each V2V link is regarded an agent and all agents jointly intercommunicate with the environment. Each agent allocates spectrum and power through its deep Q network (DQN). To enhance effective intercommunication and the sense of collaboration among vehicles, we introduce an attention mechanism to focus on more relevant information, which in turn reduces the signaling overhead and optimizes their communication performance more explicitly. Experimental results show that the proposed AMARL-based approach can satisfy the requirements of a high rate for V2I links and low latency for V2V links. It also has an excellent adaptability to environmental change

Local Feature Enhancement for Nested Entity Recognition Using a Convolutional Block Attention Module

Named entity recognition involves two main types: nested named entity recognition and flat named entity recognition. The span-based approach treats nested entities and flat entities uniformly by classifying entities on a span representation. However, the span-based approach ignores the local features within the entities and the relative position features between the head and tail tokens, which affects the performance of entity recognition. To address these issues, we propose a nested entity recognition model using a convolutional block attention module and rotary position embedding for local features and relative position features enhancement. Specifically, we apply rotary position embedding to the sentence representation and capture the semantic information between the head and tail tokens using a biaffine attention mechanism. Meanwhile, the convolution module captures the local features within the entity to generate the span representation. Finally, the two parts of the representation are fused for entity classification. Extensive experiments were conducted on five widely used benchmark datasets to demonstrate the effectiveness of our proposed model

Best traffic carrier frequency number and cell wireless utilization research

AbstractIn order to solve the problems of unreasonable cell carrier frequency configuration (CCFC) and insufficient wireless resources utilization in GSM network, the conception of the best CCFC number is introduced. By the statistical analysis of the large number of traffic data, find out the relationship between the CCFC and the busy traffic congestion rate, and then obtain the best carrier frequency configuration number in each traffic interval. According to the best carrier frequency configuration number, the best cell wireless utilization rate is obtained. This standard reflects the demand number of cell actual carrier frequency configuration, which provides a theoretical basis for the CCFC and the current network cell wireless utilization in the future

Adipose Tissue SIRT1 Regulates Insulin Sensitizing and Anti-Inflammatory Effects of Berberine

Berberine (BBR), which is an active component of Coptis chinensis Franch, has been reported to improve glucose metabolism and insulin resistance in animal and human studies, predominantly via activation of the 5'-adenosine monophosphate kinase (AMPK) pathway and suppression of the inflammation response. However, the mechanisms underlying the effects of BBR on AMPK and inflammation remain unclear. In this present study, we found that BBR upregulated SIRT1 expression in 3T3L-1 adipocytes and adipose tissue. Inhibition of SIRT1 blunted the BBR-induced increase in glucose consumption and uptake in adipocytes. The BBR-induced activation of the AMPK pathway and AKT phosphorylation in adipocytes and adipose tissue were also attenuated by inhibition or knockout of Sirt1. The BBR-induced improvement of systemic insulin sensitivity was impaired by Sirt1 knockout in HFD-induced obese mice. The suppressing effects of BBR on systemic and local inflammatory responses, such as serum concentrations and expression of inflammatory cytokines, phosphorylation of c-Jun N-terminal kinase (JNK) and IKKβ, and the accumulation of F4/80-positive macrophages in adipose tissue were also attenuated in Sirt1 knockout mice. The BBR-induced decrease in PGC-1α acetylation was reversed by inhibition or knockout of Sirt1 in adipocytes and adipose tissue. Together, these results indicate that adipose tissue SIRT1 is a key regulator of the insulin sensitizing and anti-inflammatory effects of BBR, which contributes to the improvement of metabolic dysregulation