GNSS-R technology provides a novel solution for sea ice dynamic monitoring through its all-weather observation capability and low-cost advantages. However, existing methods still face significant challenges in simultaneously achieving high-accuracy detection of ice-water boundaries and precise identification of large-area sea ice distant from transition zones. To address this issue, this paper proposes a novel sea ice detection model incorporating the semi-correlated supervision algorithm of dual neural networks. First, a dual-modal dataset comprising DDM and Differential DDM data is employed to capture the global scattering characteristics of sea ice and local differential features of transition regions, respectively. Second, an innovative dual-branch detection architecture is designed: the Differential DDM detection branch based on YOLOv11 achieves high-sensitivity identification of ice-water boundaries through local feature extraction, while the Efficient Vision Mamba detection branch enhances detection robustness for open water and continuous ice cover via global feature analysis. To further optimize model performance, a semi-correlated supervision correction algorithm is proposed, which dynamically integrates dual-branch detection results through temporal context analysis and bidirectional validation mechanisms, effectively resolving the performance imbalance between transition zones and stable regions in traditional single-model approaches. Experiments using J1-01B and TDS-1 satellites GNSS-R data demonstrate that the model achieves 94.1% accuracy for ice-water boundary detection, 97.3% accuracy for sea ice detection distant from transition zones, with an overall accuracy of 95.7%. This research establishes a high-accuracy, full-coverage technical framework for real-time polar sea ice monitoring, offering significant application value for global climate change studies and polar navigation safety.

We present a novel approach to address the multi-agent sparse contextual linear bandit problem, in which the feature vectors have a high dimension d whereas the reward function depends on only a limited set of features - precisely s0 ≪ d. Furthermore, the learning follows under information-sharing constraints. The proposed method employs Lasso regression for dimension reduction, allowing each agent to independently estimate an approximate set of main dimensions and share that information with others depending on the network’s structure. The information is then aggregated through a specific process and shared with all agents. Each agent then resolves the problem with ridge regression focusing solely on the extracted dimensions. We represent algorithms for both a star-shaped network and a peer-to-peer network. The approaches effectively reduce communication costs while ensuring minimal cumulative regret per agent. Theoretically, we show that our proposed methods have a regret bound of order O(s0 log d + s0 √T) with high probability, where T is the time horizon. To our best knowledge, it is the first algorithm that tackles row-wise distributed data in sparse linear bandits, achieving comparable performance compared to the state-of-the-art single and multi-agent methods. Besides, it is widely applicable to high-dimensional multi-agent problems where efficient feature extraction is critical for minimizing regret. To validate the effectiveness of our approach, we present experimental results on both synthetic and real-world datasets.

Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, due to the inherent unreliability of renewable energy, it is imperative to develop effective algorithms for DER management. In this work, we study the energy-sharing problem in a system consisting of several DERs. Each agent harvests and distributes renewable energy in its neighborhood to optimize the network's performance while minimizing energy waste. We model this problem as a bandit convex optimization problem with constraints, where the constraints correspond to each node's limitations for energy production. We propose a distributed decision-making policy to solve the formulated problem, that achieves ${\mathcal{O}}\left( {{T^{\frac{3}{4}}}} \right)$ regret bound and ${\mathcal{O}}\left( {{T^{\frac{3}{4}}}} \right)$ constraint violations. To reduce the constraint violations, we suggest two decision-making variations. Numerical experiments using a real-world dataset show superior performance of our proposal compared to state-of-the-art methods.

Contextual bandit algorithms appear in several applications, such as online advertisement and recommendation systems like personalized education or personalized medicine. Individually-tailored recommendations boost the performance of the underlying application; nevertheless, providing individual suggestions becomes costly and even implausible as the number of users grows. As such, to efficiently serve the demands of several users in modern applications, it is imperative to identify the underlying users’ clusters, i.e., the groups of users for which a single recommendation might be (near-)optimal. We propose CLUB-HG, a novel algorithm that integrates a game-theoretic approach into clustering inference. Our algorithm achieves Nash equilibrium at each inference step and discovers the underlying clusters. We also provide regret analysis within a standard linear stochastic noise setting. Finally, experiments on synthetic and real-world datasets show the superior performance of our proposed algorithm compared to the state-of-the-art algorithms.

In remote sensing images, the background is complex, the distribution is dense, the target scale varies widely, there are many tiny targets, and the target directions are diverse, which is a challenging detection task. This paper proposes a remote sensing image rotating target detection network (C2-YOLO) that integrates the upsampling feature enhancement module and attention mechanism in response to these problems. The network is based on the YOLOV5 target detection algorithm, and the prediction head is added to enhance the ability of small target detection. The content-Aware ReAssembly of Features(CARAFE) module is introduced to design a new feature fusion module. We also integrate the Coordinate Attention (CA) module to focus on object locations in complex scenes. According to the rotation characteristics, we add an angle loss to the loss function to detect the rotation angle of the object. We conduct experiments on two public datasets, DOTA and HRSC2016. Compared with the original YOLOv5 algorithm, the detection accuracy of our algorithm is improved by 2.99% and 3.52%, which can achieve comparable performance to the state-of-the-art detection methods.