Ship detection in synthetic aperture radar (SAR) images is crucial for maritime monitoring. Anchor-based deep learning methods are prone to performance degradation due to redundant anchors. In contrast, keypoint-based anchor-free methods typically rely on Gaussian kernels with fixed standard deviations to generate heatmaps for detecting keypoints, reducing accuracy in multi-scale ship detection. To this end, we propose the scale-adaptive keypoint Gaussian heatmap regression network (SAG-KeyNet), an anchor-free framework for oriented SAR ship detection. The proposed scale-adaptive Gaussian heatmap regression (SAGHR) dynamically adjusts the Gaussian kernel’s standard deviation, ensuring precise keypoint localization across varying ship scales. Additionally, the ViTAE with enhanced deformation (ViTAED) backbone is utilized to extract ship features, enhancing adaptability to variations in ship posture and shape. Finally, the large kernel attention-enhanced PAFPN (LKA-PAFPN) is designed to improve multi-scale feature fusion across spatial and channel dimensions. Extensive experimental results on three datasets demonstrate the effectiveness and generalization of SAG-KeyNet. Notably, an average precision (AP50) of 73.8% is achieved on the RSDD-SAR dataset for inshore ship detection.

Build failures are a major obstacle in RISC-V software migration, often involving complex interactions across logs, configurations, and environments. Traditional diagnostic tools struggle with the unstructured, multi-phase nature of build logs and lack semantic reasoning.We propose a two-stage framework for automated root cause analysis. RV-LAD compresses logs using template-based filtering and applies phase-aware anomaly detection via few-shot LLM prompting. MCTS-RCA integrates a domain-specific knowledge base with Monte Carlo Tree Search to perform LLM-guided multi-source reasoning under classification constraints.To support evaluation, we construct a curated dataset of 117 real-world RISC-V build failures, each annotated with logs, spec files, and repair records. Experiments show our approach achieves 75.2% diagnosis accuracy, surpassing previous LLM-based and rule-based methods. It also offers interpretable reasoning traces, enabling practical and transparent diagnosis. This work provides an effective and extensible solution for RCA in emerging software ecosystems like RISC-V, bridging large language models with domain-aware inference.

Modeling spatial and temporal dependencies is essential for achieving accurate air quality prediction. Current air quality prediction models often overlook the underlying causal relationships in the data and primarily focus on statistical correlations. As a result, these models lack sufficient predictive power for medium- and long-term forecasts. This paper introduces causality into air quality prediction and proposes a Causal Spatio-Temporal Convolutional Network (CSTCN). We utilize an attention mechanism to automatically assign attention weights to each air quality monitoring site, enabling the capture of causal relationships between sites in the spatial dimension. Conducting tests on the identified relationships ensures the causality of the data in space. Furthermore, convolution operations are applied to extract the spatio-temporal features of the monitoring stations, while also utilizing causal convolution to ensure the causality of the data over time. The experiments conducted on the Beijing air quality dataset demonstrate that CSTCN exhibits outstanding performance in medium- and long-term predictions.

Patient similarity measuring plays a significant role in many healthcare applications, such as cohort study and treatment comparative effectiveness research. Existing methods mainly rely on supervised metric learning method to study patient similarity from Electronic Health Records (EHRs), facing the challenge of differentiating patients with a large number of fine-grained disease categories. Deep metric learning has gained noticeable success in fine-grained image categorization problem, however, it cannot be directly applied to classification of patients with hierarchical disease labels. In this paper, we present a novel three layer patient similarity deep metric learning framework (PSDML) by optimizing quadruple loss improved from triplet loss, to learn an embedding distance for disease classification among the patients. The context semantic relation of multi diagnosis labels encoding by ICD-10 is taken into account to compute the supervised distance of patients. To solve the diagnosis class imbalance, patient tuples that violate deep metric learning framework loss constraints are chosen prior as samples to accelerate the convergence of the neural network. We conducted KNN multi label classification experiment using the learned similarity metric on the real EHRs about stroke disease collected by Chinese Stroke Data Center. The results demonstrate substantial improvement over the baselines.