Image-based virtual try-on (VTON) aims to synthesize photorealistic images of a person wearing specified garments. Despite significant progress, building a universal VTON framework that can flexibly handle diverse and complex tasks remains a major challenge. Recent methods explore multi-task VTON frameworks guided by textual instructions, yet they still face two key limitations: (1) semantic gap between text instructions and reference images, and (2) data scarcity in complex scenarios. To address these challenges, we propose UniFit, a universal VTON framework driven by a Multimodal Large Language Model (MLLM). Specifically, we introduce an MLLM-Guided Semantic Alignment Module (MGSA), which integrates multimodal inputs using an MLLM and a set of learnable queries. By imposing a semantic alignment loss, MGSA captures cross-modal semantic relationships and provides coherent and explicit semantic guidance for the generative process, thereby reducing the semantic gap. Moreover, by devising a two-stage progressive training strategy with a self-synthesis pipeline, UniFit is able to learn complex tasks from limited data. Extensive experiments show that UniFit not only supports a wide range of VTON tasks, including multi-garment and model-to-model try-on, but also achieves state-of-the-art performance.

Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought have demonstrated remarkable capabilities in code generation and mathematical reasoning.However, their potential in multi-turn Textto-SQL tasks remains largely underexplored.Existing approaches typically rely on unstable API-based inference or require expensive fine-tuning on small-scale models.In this work, we present Rose-SQL, a training-free framework that leverages small-scale LRMs through in-context learning to enable accurate context-dependent parsing.We introduce the Role-State, a fine-grained representation that bridges the structural gap between schema linking and SQL generation by serving as a structural blueprint.To handle conversational dependencies, Rose-SQL traces the evolution of Role-State through historical context via structural isomorphism checks, guiding the model to infer the possible SQL composition for the current question through verified interaction trajectories.Experiments on the SParC and CoSQL benchmarks show that, within the Qwen3 series, Rose-SQL outperforms in-context learning baselines at the 4B scale and substantially surpasses state-of-the-art fine-tuned models at the 8B and 14B scales, while showing consistent gains on additional reasoning backbones.

Biselection (feature and sample selection) enhances the efficiency and accuracy of machine learning models when handling large-scale data. Fuzzy rough sets, an uncertainty mathematical model known for its excellent interpretability, are widely used in machine learning, particularly for feature selection. While the consistent matrix has significantly improved the computational efficiency and scalability of feature selection, like most fuzzy rough set-based methods, it focuses only on feature selection and seldom incorporates sample selection. This feature-centric approach can limit classification performance, particularly in noisy and large-scale datasets where both features and samples require judicious selection. To overcome these limitations, this article explores the integration of sample selection with feature selection. First, we introduce a$\beta$-consistent granulation method to generate more accurate and concise fuzzy information granules. In addition, a novel membership function is employed to distinguish noise samples and irrelevant features simultaneously. As a result, a biselection algorithm with lower computational complexity is proposed to select high-quality features and samples. Numerical experiments demonstrate that, compared to eleven representative algorithms, our proposed method achieves an average accuracy improvement of 9.66% and a 933-fold increase in efficiency.

Monotonic classification is a special ordinal classification task that involves monotonicity constraints between features and the decision. Monotonic feature selection can reduce dimensionality while preserving the monotonicity constraints, ultimately improving the efficiency and performance of monotonic classifiers. However, existing feature selection algorithms cannot handle large-scale monotonic data sets due to their lack of consideration for monotonic constraints or their high computational complexities. To address these issues, building on our team's previous research, we define the monotonic related family method with lower time complexity to select informative features and obtain multi-reducts carrying complementary information from multi-view for raw feature space. Using bi-directional rank mutual information, we build two trees for each feature subset and fuse all trees using the corresponding decision support level (BFMDT). Compared with six representative algorithms for monotonic feature selection, BFMDT's average classification accuracy increased by 4.06% (FFREMT), 6.77% (FCMT), 5.61% (FPRS_up), 6.05% (FPRS_down), 5.86%(FPRS_global), 4.41% (Bagging), 7.65% (REMT) and 21.89% (FMKNN), the average execution time compared to tree-based algorithms decreased by 83.41% (FFREMT), 96.96% (FCMT), 75.64% (FPRS_up), 59.43% (FPRS_down), 84.65%(FPRS_global), 81.50% (Bagging) and 63.41% (REMT), while most of comparing algorithms were unable to complete computation on six high-dimensional datasets.

With the booming of social media, sentiment analysis has developed rapidly in recent years. However, only a few studies focused on the field of transportation, which failed to meet the stringent requirements of safety, efficiency, and information exchange of intelligent transportation systems (ITSs). We propose the traffic sentiment analysis (TSA) as a new tool to tackle this problem, which provides a new prospective for modern ITSs. Methods and models in TSA are proposed in this paper, and the advantages and disadvantages of rule- and learning-based approaches are analyzed based on web data. Practically, we applied the rule-based approach to deal with real problems, presented an architectural design, constructed related bases, demonstrated the process, and discussed the online data collection. Two cases were studied to demonstrate the efficiency of our method: the “yellow light rule” and “fuel price” in China. Our work will help the development of TSA and its applications.