As the marine economy continues to expand, the importance of efficient and reliable marine data collection has become increasingly evident. This paper investigates a multi-unmanned aerial vehicle (UAV)-assisted marine data collection network system, where multiple UAVs are deployed within a designated area to collect data from water buoy sensors (WBSs) and act as relays to offload the collected data to a central ship. The primary objective is to minimize the total system energy consumption, subject to constraints on access relationships, power scheduling, and movement trajectories. The formulated optimization problem is non-convex and highly complex due to the coupling of multiple variables. To address this challenge, we propose an alternating optimization algorithm that jointly optimizes the trajectories of multiple UAVs, the access selection of WBSs, the trajectory of the ship, the data offloading decisions, and the UAV transmit power scheduling in an iterative manner. The algorithm leverages techniques such as successive convex approximation (SCA) and greedy strategies to efficiently solve the decomposed sub-problems. Simulation results demonstrate that the proposed approach achieves significant performance improvements compared to several benchmark algorithms, highlighting its effectiveness in enhancing energy efficiency and system robustness in dynamic marine environments.

Accurate lung inflammation segmentation is essential for clinical decision-making, yet remains challenging due to the large variability in lesion appearance and location across different lung regions. Existing CNN-based models excel at local feature extraction, but they struggle to capture long-range dependencies and complex spatial relationships, such as those between the left and right lung lobes. Transformer-based models, while effective in modeling long-range dependencies, incur high computational costs and often fail to capture irregular anatomical relationships due to their reliance on Euclidean positional encodings. To overcome these challenges, we propose a novel Channel-Region Adaptive Unet (CRA-Unet) for accurate lung inflammation segmentation. Specifically, we design a Channel-Region Adaptive (CRA) layer that expands the recalibration process of the Squeeze-Excitation layer to include not only the channel dimension but also the height and width dimensions, enabling dynamical element-wise feature adjustment within different regions of interest across all three dimensions—channel, height, and width. Additionally, we propose a region-adaptive positional encoding strategy that learns dynamic weights for spatial locations, allowing the model to capture both intra-region and inter-region spatial relationships. Unlike traditional Euclidean positional encodings, which assume regular and grid-like spatial structures, our strategy can adapt to the irregular and asymmetric spatial relationships commonly found in anatomical structures such as the lungs. Experimental results on several datasets demonstrate that our CRA-Unet achieves state-of-the-art segmentation performance while maintaining high computational efficiency.

This paper introduces a lightweight Semantic-guided Mutually Reinforcing network (SMR-Net) for the tasks of cross-modal image fusion and salient object detection (SOD). The core concept of SMR-Net is to leverage semantics for directing the mutual reinforcing between image fusion and SOD. Specifically, a Progressive Cross-modal Interaction (PCI) image fusion subnetwork is designed to exploit local interactions via convolution operations and extend to global interactions utilizing spatial and channel attention mechanisms. Subsequently, a cross-modal Bit-Plane Slicing-based SOD subnetwork (BPS) is developed by incorporating the fused image as a third modality. This component employs bit-plane slicing and the deformable convolution technique to effectively extract irregular semantic information embedded in fusion features. The refined semantic information then guides the feature extraction process of the source modalities in a reweighted fashion. By cascading these two subnetworks, BPS leverages final semantic results to direct PCI towards focusing more on semantic information. Ultimately, through this semantic-guided mutual enhancement process, SMR-Net excels in both producing high-quality fused images and achieving effective salient object detection. Our extensive experiments on image fusion and SOD tasks convincingly demonstrate the superiority of our network over existing state-of-the-art alternatives without introducing noticeable computational costs. Compared to nearest competitors, our method demonstrates a stronger generalization ability with 26% fewer parameters.

Large language models (LLMs) have shown significant promise in question-answering (QA) tasks, particularly in retrieval-augmented generation (RAG) scenarios and long-context applications. However, their performance is hindered by noisy reference documents, which often distract from essential information. Despite fine-tuning efforts, Transformer-based architectures struggle to prioritize relevant content. This is evidenced by their tendency to allocate disproportionate attention to irrelevant or later-positioned documents. Recent work proposes the differential attention mechanism to address this issue, but this mechanism is limited by an unsuitable common-mode rejection ratio (CMRR) and high computational costs. Inspired by the operational amplifier (OpAmp), we propose the OpAmp adaptation to address these challenges, which is implemented with adapters efficiently. By integrating the adapter into pre-trained Transformer blocks, our approach enhances focus on the golden context without costly training from scratch. Empirical evaluations on noisy-context benchmarks reveal that our Qwen2.5-OpAmp-72B model, trained with our OpAmp adaptation, surpasses the performance of state-of-the-art LLMs, including DeepSeek-V3 and GPT-4o.Our code is available at https://github.com/wuhy68/OpampAdapter.

Large language models (LLMs) achieve remarkable performance in code generation tasks. However, a significant performance disparity persists between popular programming languages (e.g., Python, C++) and others. To address this capability gap, we leverage the code translation task to train LLMs, thereby facilitating the transfer of coding proficiency across diverse programming languages. Moreover, we introduce OORL for training, a novel reinforcement learning (RL) framework that integrates on-policy and off-policy strategies. Within OORL, on-policy RL is applied during code translation, guided by a rule-based reward signal derived from unit tests. Complementing this coarse-grained rule-based reward, we propose Group Equivalent Preference Optimization (GEPO), a novel preference optimization method. Specifically, GEPO trains the LLM using intermediate representations (IRs) groups. LLMs can be guided to discern IRs equivalent to the source code from inequivalent ones, while also utilizing signals about the mutual equivalence between IRs within the group. This process allows LLMs to capture nuanced aspects of code functionality. By employing OORL for training with code translation tasks, LLMs improve their recognition of code functionality and their understanding of the relationships between code implemented in different languages. Extensive experiments demonstrate that our OORL for LLMs training with code translation tasks achieves significant performance improvements on code benchmarks across multiple programming languages.

The success of most robust model estimation methods heavily relies on their used data sampling algorithms. This paper proposes a novel sampling algorithm, called Guided Sampling by Neighborhood Information and Matching Scores (NIMS), to efficiently sample promising hypotheses for fitting multi-structure data. Specifically, NIMS follows a specific sampling process. First, the proposed NIMS randomly selects a data point. Then, NIMS selects the neighbors of the selected data by using the neighborhood information to remove most of the outlier neighbors. Finally, NIMS samples a data subset using matching scores from the selected neighbors, which encourages NIMS to sample inliers from the selected neighbors. Experimental results on the publicly availableAdelaideRMFdataset demonstrate that the proposed NIMS outperforms several state-of-the-artsampling algorithms.