Search Results (933)

Recent advances in large language models (LLMs) have raised questions about their potential role in portfolio allocation beyond traditional sentiment analyses. This study investigated whether LLMs, when prompted directly, can autonomously generate portfolio weights that compete with classical optimization and AI-enhanced strategies. We evaluated seven medium-sized open-source LLMs—Gemma-7B, Mistral-7B, Jansen Adapt-Finance-Llama2-7B, DeepSeek-R1-8B, QuantFactory Llama-3-8B-Instruct-Finance, Qwen-7B, and Llama2-7B—using systematic prompt engineering and temperature tuning. Portfolios were constructed from financial news headlines for S&P 500 equities and benchmarked against mean–variance optimization (MVO), the Black–Litterman model, AI-driven optimizers, and naive diversification strategies. The results show that, while LLM-generated portfolios outperformed naive diversification (Sharpe ratio up to 0.741), they lagged behind AI-optimized benchmarks (Sharpe ratio up to 1.361). A transaction cost analysis revealed that low-turnover LLM strategies retain their competitiveness post-costs, surpassing cap-weighted benchmarks. Statistical tests confirmed significant performance differences ($p\le 0.01$). These findings highlight the ability of LLMs to extract signals from unstructured text, but also their limitations without explicit optimization. Future research should explore hybrid frameworks that combine LLM reasoning with quantitative optimization for cost-sensitive environments. Full article

Few-shot object detection aims to accurately identify and localize novel categories using only a small number of labeled samples. In remote sensing images, however, this task faces significant challenges due to substantial variations in target scale and complex backgrounds. To address these issues, this paper proposes a dual-attention guided few-shot object detection framework, DAFSDet. Specifically, a dual-attention strategy is implemented across the feature modeling and proposal generation stages. For feature fusion, the Content-Aware Strip Pyramid (CASP) is designed to enhance multi-scale feature representation by modeling spatial and contextual information. In the detection stage, a Deformable Attention RPN (DA-RPN) is proposed to improve the localization quality of candidate regions. With these designs, the proposed method effectively mitigates the challenges posed by multi-scale variations and complex backgrounds. Experimental results on the DIOR and NWPU VHR-10 datasets demonstrate consistent improvements over baseline methods, with notable gains of 7.54 mAP on DIOR Split 2 under the 10-shot setting and 2.09 mAP on NWPU VHR-10 under the 3-shot setting. These results indicate that the proposed method offers an effective solution for few-shot object detection in complex remote sensing scenarios. Full article

This paper provides a systematic review of recent applications of NLP methods for analyzing traffic crash reports, with a focus on estimating crash severity, crash duration, and crash causation. The review covers prior research using probabilistic topic modeling methods such as LDA, STM, and hierarchical Dirichlet processes in addition to research using transformer-based language models, which include encoder-based models like BERT and PubMedBERT as well as decoder-based models like GPT, GPT2, ChatGPT, GPT-3, and LLaMA. The review starts with a systematic literature selection process with predefined inclusion criteria. We categorize the reviewed studies into the following application areas: crash severity prediction, risk factor identification in crashes, and road safety analysis. The results show several complementary advantages of using different NLP techniques to achieve different analytical goals. Topic models allow for interpretable and exploratory pattern discovery, while encoder models are well-suited for structured prediction problems. Decoder models have the additional flexibility to perform zero-shot and few-shot reasoning, which makes them useful for reasoning about under-sampled or under-reported data. Across the literature, hybrid methods that combine text and structured data outperform individual methods in terms of prediction accuracy and broad applicability. Challenges across the literature include class imbalance, lack of standardization in preprocessing and evaluation methods, and the tradeoff between prediction accuracy and interpretability of prediction models. These findings highlight the importance of aligning model selection with data availability and operational constraints, pointing toward future research directions in hybrid modeling frameworks, standardized evaluation protocols, and real-world deployment of NLP-driven traffic safety systems. Full article

Camera rotation estimation is a key component in video stabilization and motion analysis. In many practical scenarios, inertial measurements are unavailable or temporally unreliable, while classical geometric pipelines degrade under blur, low texture, and low illumination. This paper investigates whether substantially downsampled optical flow can retain sufficient structure for accurate frame-to-frame rotation regression. We present VisualRNet, a lightweight rotation-specific visual regression framework trained with cross-modal IMU supervision. Our design uses coordinate-aware feature encoding, depthwise separable convolutions, lightweight attention, and a compact 6D rotation head to model the spatial structure of rotational flow fields. On Deep-FVS, VisualRNet achieves a mean rotation error of 0.3151${}^{\circ }$ on the test set. The VisualRNet regression head contains 7.7 K parameters, 0.002 GFLOPs, and runs at 729 FPS, while the full pipeline with the FastFlowNetv2 frontend contains 1.374 M parameters, 7.194 GFLOPs, and runs at approximately 113 FPS. A cross-camera adaptation experiment on TUM VI further indicates that the learned motion representation can be aligned to a new camera system with limited calibration data. These results support low-resolution optical flow as a practical input for visual rotation estimation and suggest particular value in stabilization-oriented and cost-sensitive applications where approximate rotational trend matters more than full scene geometry. Full article

System logs have been critical for analyzing the operational status and abnormal behavior of highly distributed and heterogeneous edge computing nodes. In edge environments, logs exhibit cross-event and cross-field structural interactions, making it difficult to uncover potential anomaly patterns from isolated events. Moreover, sparse annotations and varying log formats limit the effectiveness of existing methods. To address these challenges, we propose a graph neural network (GNN) anomaly detection framework with prompt learning. It leverages few-shot prompt learning to automatically extract key fields and constructs a weighted directed graph that jointly models semantic embeddings and temporal dependencies, fully representing the structural interactions and semantic associations across events and fields. Furthermore, the framework performs graph-level anomaly detection by jointly optimizing graph representation learning and classification objective within an enhanced one-class directed graph convolutional network, enabling effective identification of global structural anomaly patterns in log graphs. Experimental results demonstrate that the proposed method achieves an average F1-score of 93.3%, surpassing the current state-of-the-art (SOTA) methods by 6.93%. Full article

In high-value precision machining, existing tool wear monitoring models often suffer from two major limitations: poor generalization under varying cutting conditions and heavy reliance on large amounts of labeled data for new operating scenarios. These limitations hinder the practical deployment of intelligent monitoring systems. To address these challenges, this paper proposes a few-shot warm-start framework based on model-agnostic meta-learning. The method consists of two stages. First, meta-training is performed on historical machining data to learn a task-sensitive parameter initialization that enables rapid adaptation. Second, under a new operating condition, the few-shot warm-start mechanism collects a minimal number (1 to 5) of samples through a targeted physical trial-cutting process for online fine-tuning, aligning the model with the current physical environment. Experiments on the PHM2010 dataset fully simulate varying cutting scenarios. The experimental results demonstrate that the proposed framework consistently outperforms traditional transfer learning, deep learning models, and existing meta-learning approaches, offering an effective solution for fast and accurate tool wear prediction under few-shot and varying cutting conditions. Full article

Motor fault diagnosis under small-sample conditions remains challenging because limited labeled data often cause deep models to overfit and generalize poorly. To address this problem, we propose STR-DDPM, a fault data augmentation framework that combines moving-average-based seasonal–trend–residual decomposition with a denoising diffusion probabilistic model. Specifically, multichannel signals are decomposed into trend, seasonal, and residual components, and class-conditional diffusion modeling is performed only in the residual domain. This design emphasizes fault-related stochastic variations while reducing interference from deterministic structures. To improve generation stability, we adopt velocity prediction and develop an enhanced one-dimensional U-Net with multi-scale convolutions, channel attention, self-attention, and feature-wise linear modulation for controllable conditional generation. Experiments on the University of Ottawa and Paderborn motor fault datasets demonstrate that the proposed method generates samples that are highly consistent with real data and improves diagnostic performance under multiple synthetic-data-assisted settings. These results indicate that STR-DDPM provides an effective and practical solution for data augmentation in data-limited motor fault diagnosis. Full article

Model-Agnostic Meta-Learning (MAML) and Prototypical Networks (ProtoNet) establish the foundational paradigms for few-shot classification. However, MAML suffers from optimization instability caused by reconstructing classification boundaries for every new task. Conversely, ProtoNet lacks the internal mathematical capacity necessary for task-specific parameter adaptation under domain shifts. To reconcile these structural limitations, we introduce Metric-based Model-Agnostic Meta-Learning (M²AML). By completely excising the parameterized classification layer from the episodic adaptation sequence, our framework replaces traditional inner-loop classification with a dynamic self-exclusive geometric similarity metric. Substituting functional mappings with spatial distance optimizations efficiently resolves evaluation conflicts, thereby establishing perfectly synchronized inner and outer learning rates alongside substantially accelerated adaptation steps. Extensive experiments across mini-ImageNet, tiered-ImageNet, and CIFAR-FS validate our approach against a comprehensive array of established algorithms. To ensure strictly fair comparative evaluations, we meticulously reproduce the MAML, ProtoNet, and Proto-MAML baselines. Empirical results demonstrate that M²AML achieves state-of-the-art performance across most evaluation settings, delivering absolute accuracy improvements ranging from 0.1% to 2.1% over existing leading models. Full article

The rapid evolution of malware families poses significant challenges for cybersecurity systems, particularly when newly emerging threats lack sufficient labeled data. Although image-based deep learning approaches have achieved strong performance under fully supervised conditions, their dependence on retraining limits adaptability in dynamic environments. To address this issue, we propose a Retrieval-Augmented Few-Shot Malware Detection Framework that integrates binary-to-image visualization, multimodal embedding using a frozen Vision–Language Model (Qwen2.5-VL), and similarity-based external memory retrieval. Malware binaries are converted into grayscale images and embedded into a semantic vector space without task-specific fine-tuning. During inference, query samples retrieve similar support embeddings from a vector database, and predictions are generated through similarity-weighted aggregation, enabling adaptation without parameter updates. Evaluated on the MalImg dataset with 25 malware families under 1-shot to 10-shot settings, the framework achieves 0.886 accuracy in the 10-shot configuration. Ablation results demonstrate that combining VLM embeddings with retrieval mechanisms provides consistent improvements over individual components. These findings highlight the effectiveness of decoupling representation learning from adaptation for scalable few-shot malware detection. Full article

Plant disease diagnosis in real-world agricultural environments is challenged by data scarcity, domain shift, privacy constraints, and limited edge-device resources. This paper proposes FMEL-FSDA, a Federated Multimodal Edge Learning framework with Few-Shot Domain Adaptation for robust field-based plant disease recognition. The framework integrates attention-based RGB–text feature fusion, privacy-preserving federated learning, rapid few-shot personalization, and uncertainty-aware inference within an edge-efficient architecture. Federated training enables collaborative learning across distributed farms without sharing raw data, while few-shot adaptation allows fast deployment to new regions using only 1–10 labeled samples per class. Experiments on the PlantWild in-the-wild dataset show that FMEL-FSDA outperforms centralized, federated, and few-shot baselines, achieving 93.78% accuracy, 93.33% F1-score, and 0.97 AUC. The model maintains strong performance under privacy mechanisms such as gradient perturbation and secure aggregation, reduces communication overhead by up to 4×, and supports low-latency edge inference. Uncertainty estimation and Grad-CAM-based explainability further enhance reliability by identifying low-confidence cases and highlighting disease-relevant regions. Overall, FMEL-FSDA offers a scalable, privacy-aware, and field-ready solution for intelligent plant disease diagnosis in precision agriculture. Full article

Hyperspectral image (HSI) classification in few-shot scenarios faces two core challenges. Limited samples and high spectral similarity lead to insufficient inter-class feature discriminability, and commonly used deep models suffer from the risk of overfitting. To address these problems, this paper proposes a Class-Adaptive Dictionary Reconstruction with Broad Learning (CADR-BL) method. Specifically, the method constructs an exclusive adaptive dictionary for each category and adopts an alternating minimization strategy to achieve sparse reconstruction of intra-class pixels, thereby enhancing intra-class spectral consistency and suppressing inter-class interference. On this basis, an improved Hyperspectral Broad Learning (HS-BL) model is introduced to efficiently classify the reconstructed features. Random feature mapping and closed-form solutions of output weights are utilized to alleviate overfitting in few-shot learning. Experiments conducted on three benchmark datasets, namely Indian Pines, Salinas, and WHU-Hi-HanChuan, show that CADR-BL outperforms several mainstream few-shot classification methods in terms of overall accuracy, average accuracy, and Kappa coefficient. Notably, CADR-BL maintains robust performance even with extremely limited training samples, and is less sensitive to variations in sample size than other comparative methods, demonstrating strong generalization ability. The proposed method provides a reliable technical reference for few-shot HSI classification in applications such as precision agriculture, environmental monitoring, and resource exploration. Full article

In remote sensing image oriented object detection tasks, weakly supervised learning methods based on horizontal bounding boxes have attracted much attention due to their lower annotation costs compared to fully supervised methods. However, remote sensing images, characterized by complex backgrounds, exhibit a wide range of target scales and diverse geometric characteristics across target categories. Existing methods exhibit inadequate exploitation of background and angular information under weak supervision, resulting in compromised perception of dense and high-aspect-ratio targets. Neglecting the imbalance in angle estimation samples further leads to excessively low detection accuracy for few-shot categories. To address the aforementioned issues, this paper proposes a Geometry-Aware Enhancement Network (WSOOD-GAEN) for weakly supervised oriented object detection tasks. First, in the backbone network stage, a channel-space deformable attention module (DAE-ResNet) was constructed. Through deformable sampling and screening of key regions, feature extraction has both morphological adaptability to complex shapes and semantic discriminability of key features in complex backgrounds. Secondly, in the feature pyramid stage, an Angle-Guided Feature Pyramid Network (AG-FPN) is proposed. This module dynamically applies rotation transformation to the sampling offsets of deformable convolutions, thereby enhancing the feature representation of objects with different orientations and scales. Furthermore, an adaptive geometric perception loss (AGL) was designed. Based on the geometric characteristics of different categories, it automatically learns differentiated rotation and flip consistency weights, thereby improving the prediction accuracy of small sample categories. Experiments on the DOTA-v1.0, HRSC, and RSAR datasets validate our approach. Specifically, under the AP75 evaluation metric, the proposed method outperforms existing weakly supervised methods by 1.51%, 9.86%, and 3.28%, respectively. Full article

Cross-domain few-shot learning (CD-FSL) remains challenging in medical imaging, where labeled data are scarce and source–target domain gaps are often large due to modality differences. In particular, existing few-shot learning methods rely on source–target domain similarity, which limits their effectiveness in cross-modality settings such as MRI-to-CT transfer. To address this problem, this paper proposes an adapter-based Vision Transformer framework for cross-domain few-shot brain tumor classification. Lightweight adapter modules are inserted into a pretrained Vision Transformer to enable parameter-efficient domain adaptation without fine-tuning the entire backbone. In addition, a Prototypical Network is employed to construct class prototypes from limited labeled samples, while a prototype-level Maximum Mean Discrepancy (MMD) loss is introduced to align feature distributions across domains. Unlike prior approaches, the proposed framework introduces a unified prototype-level alignment strategy within an episodic learning paradigm, enabling direct class-wise cross-modal alignment. This design improves generalization under large modality gaps and limited labeled data by jointly optimizing representation learning and domain adaptation. The proposed framework is evaluated on MRI-to-CT brain tumor classification as well as several heterogeneous cross-domain benchmarks, including Chest X-ray, ISIC, CropDisease, and EuroSAT. Experimental results demonstrate that the proposed method achieves competitive performance compared to existing few-shot learning baselines, showing strong robustness under significant domain shifts. Full article

To address the challenges of scarce unmanned aerial vehicle (UAV) track samples, severe class imbalance, and high motion similarity between UAVs and birds in low-altitude radar recognition, this paper proposes a trajectory classification method integrating a TCN-Transformer model with a physics-constrained TimeGAN (PC-TimeGAN) data augmentation framework. Specifically, the PC-TimeGAN generates high-quality, kinematically compliant UAV trajectories to alleviate data scarcity and class imbalance. A multi-scale TCN-Transformer is then constructed to comprehensively extract features, utilizing multi-kernel dilated convolutions for local temporal correlations and self-attention mechanisms for global temporal dependencies, thereby improving the discrimination between UAV and bird trajectories with similar motion patterns. Furthermore, a joint loss function combining Focal Loss and Triplet Loss is employed to optimize the decision boundaries and feature space, enhancing model robustness and generalization. Experiments on a measured dataset demonstrate that, under the 15-dimensional input setting, the proposed method achieves a UAV recall of 80.00%, an FAR of 3.15%, a precision of 64.00%, and an F1-score of 0.7111. Compared to baseline methods (e.g., SVM, LSTM, GRU, Transformer, and 1D-CNN), the proposed approach significantly improves UAV recall under limited trajectory information while keeping the false-alarm rate of misclassifying birds as UAVs low. Ultimately, this method markedly enhances the comprehensive performance of rapid track-level target classification for low-altitude surveillance radars. Full article

Cross-Domain Few-Shot Scene Classification (CDFSSC) aims to transfer knowledge from a source domain to a target domain for few-shot classification tasks, and is essential for remote sensing applications involving diverse platforms and dynamic environments. However, distribution discrepancies and category misalignment across domains often introduce high predictive uncertainty, significantly degrading model performance. To address these challenges, an uncertainty-aware cross-domain (UACD) framework is proposed to enhance model reliability by systematically mining uncertainty-related information. Specifically, in the cross-domain training process, a feature-decision consistency regularization (FDCR) structure is designed to stabilize cross-domain training by enforcing consistency at both feature and decision levels. Furthermore, an uncertainty-aware knowledge mining (UKM) policy is introduced to effectively exploit high-uncertainty target samples, mitigating the negative impact of unreliable pseudo-labels and improving representation learning. In the few-shot adaptation stage, an uncertainty-aware predictor is developed to enhance adaptability and decision-making in target tasks. Extensive experiments on 12 cross-domain scenarios demonstrate that the proposed UACD framework consistently achieves superior or competitive performance, with strong robustness and generalization capability across diverse CDFSSC tasks. Full article