Institute of Software

In this paper, we addressed the limitation of relying solely on distribution alignment and source-domain empirical risk minimization in Unsupervised Domain Adaptation (UDA). Our information-theoretic analysis showed that this standard adversarial-based framework neglects the discriminability of target-domain features, leading to suboptimal performance. To bridge this theoretical-practical gap, we defined "good representation learning" as guaranteeing both transferability and discriminability, and proved that an additional loss term targeting target-domain discriminability is necessary. Building on these insights, we proposed a novel adversarial-based UDA framework that explicitly integrates a domain alignment objective with a discriminability-enhancing constraint. Instantiated as Domain-Invariant Representation Learning with Global and Local Consistency (RLGLC), our method leverages Asymmetrically-Relaxed Wasserstein of Wasserstein Distance (AR-WWD) to address class imbalance and semantic dimension weighting, and employs a local consistency mechanism to preserve fine-grained target-domain discriminative information. Extensive experiments across multiple benchmark datasets demonstrate that RLGLC consistently surpasses state-of-the-art methods, confirming the value of our theoretical perspective and underscoring the necessity of enforcing both transferability and discriminability in adversarial-based UDA.

RGB-D salient object detection (RGB-D SOD) aims to accurately localize and segment visually salient objects by jointly leveraging RGB images and depth maps. Some existing methods rely on static fusion strategies with fixed paths and weights, which treat all regions equally and fail to capture the varying importance of different regions and modalities. Although some attention-based methods alleviate the limitations of static fusion by assigning adaptive weights to different regions and modalities, the quality of RGB and depth data may degrade in real-world scenarios due to sensor noise, illumination changes, or environmental interference. These attention-based methods often overlook inter-modality quality differences and complementarity, making them prone to over-relying on a certain modality, which can lead to noise introduction, feature conflicts, and performance degradation. To address these limitations, this paper proposes a novel dynamic feature routing and fusion framework for RGB-D SOD, which adaptively adjusts the fusion strategy according to the quality of input modalities. To enable modality quality awareness, the proposed method characterizes the modality complementarity between RGB and depth features in a task-driven manner inspired by information-theoretic principles. We introduce a task-relevance scoring function which is integrated with a mutual information estimator to quantify such complementarity, and emphasizes task-relevant features while suppressing redundancy. A dynamic routing module is then designed to perform feature selection guided by the captured complementarity. In addition, we propose a novel cross-modal fusion module to adaptively fuse the features selected by the dynamic routing module, which effectively enhances complementary representations while suppressing redundant features and noise interference. Extensive experiments conducted on seven public RGB-D SOD benchmark datasets demonstrate that the proposed method consistently achieves competitive performance, outperforming existing methods by an average of approximately 1% across multiple evaluation metrics. Notably, in challenging scenarios with severe modality quality degradation, the proposed method outperforms existing best-performing methods by up to 1.8%, demonstrating strong robustness against cluttered backgrounds, complex object structures, and diverse object scales. Overall, the proposed dynamic fusion framework provides a novel solution to modality quality imbalance in RGB-D salient object detection.

Salient object ranking aims to assign a relative importance order to multiple objects in an image, aligning with human visual attention. However, existing methods struggle with ranking ambiguity in complex scenes, particularly when objects are numerous, occluded, or semantically similar, leading to decreased accuracy for low-saliency objects. To address this, we propose PairwiseSOR-MLMs, a novel framework leveraging multimodal large models and pairwise comparison to achieve salient object ranking. The approach decomposes global ranking into a series of pairwise comparison tasks. It first employs object detection and instance segmentation to identify objects, uses image inpainting to reconstruct scenes by removing occlusions, and then prompts MLMs to perform pairwise comparisons based on visual saliency cues. Finally, another MLM inference aggregates these comparisons into a consistent global ranking. Experiments on ASSR and IRSR benchmarks show our method achieves state-of-the-art or competitive performance across metrics, demonstrating robustness in handling occlusion and semantic similarity. Its pairwise comparison paradigm can extend to other relative assessment tasks.

Barrier certificates play an important role in verifying the safety of continuous-time systems, including autonomous driving, robotic manipulators and other critical applications. Recently, ReLU neural barrier certificates---barrier certificates represented by the ReLU neural networks---have attracted significant attention in the safe control community due to their promising performance. However, because of the approximate nature of neural networks, rigorous verification methods are required to ensure the correctness of these certificates. This paper presents a necessary and sufficient condition for verifying the correctness of ReLU neural barrier certificates. The proposed condition can be encoded as either an Satisfiability Modulo Theories (SMT) or optimization problem, enabling both verification and falsification. To the best of our knowledge, this is the first approach capable of falsifying ReLU neural barrier certificates. Numerical experiments demonstrate the validity and effectiveness of the proposed method in both verifying and falsifying such certificates.

Vulnerability-Fixing Commit Identification(VFCI) is a critical task in software security maintenance that aims to automatically identify code commits that patch security vulnerabilities. However, existing approaches face challenges in handling low-quality commit messages and entangled commits, which limit their identification performance. To address these issues, we propose VFCionX, a novel VFCI framework that integrates large and small language models in a collaborative architecture. VFCionX consists of three core modules: Message Classifier, Patch Classifier, and Ensemble Classifier. The Message Classifier employs a multi-source contextual augmentation strategy to enhance the quality of commit messages and fine-tunes the Qwen2.5-1.5B model, significantly improving classification performance in the textual modality. The Patch Classifier combines heuristic rules with a Qwen2.5-Coder-7B-driven file selector to filter noise from entangled commits, and incorporates a line-level feature extractor based on CodeBERT and CNN to capture local pattern differences between added and deleted code lines. The Ensemble Classifier integrates predictions from both channels using the AdaBoost algorithm, enhancing model robustness and generalization. Experimental results on five popular C/C++ repositories comprising 24,630 commits show that VFCionX achieves an F1-score of 81.47%, outperforming the best baseline by 9.42%. Ablation studies validate the effectiveness of each component, while sensitivity analysis reveals optimal parameter settings for balancing performance and noise resilience. This work provides a new and effective solution for robust vulnerability patch identification.

Abstract Accurately simulating orography‐induced mountain waves over steep terrain, such as the Tibetan Plateau (TP), remains a major challenge for numerical weather prediction (NWP) models due to grid distortions inherent in traditional terrain‐following coordinates. To address this issue, we developed an AI‐driven adaptive mesh refinement (AMR) framework within the Fluidity‐Atmosphere model, which employs a 3D unstructured mesh to mitigate geometric distortions. A Long Short‐Term Memory (LSTM) neural network is integrated to enhance the AMR process, replacing traditional adaptation criteria with data‐driven predictions. A series of idealized 2D and 3D experiments demonstrate that both the traditional AMR and LSTM‐driven approaches reproduce mountain wave dynamics with higher efficiency than fixed mesh. Furthermore, the LSTM model suppresses numerical noise near terrain, preventing spurious over‐refinement. In realistic simulations over the TP, the LSTM‐enhanced model successfully captured the life cycle of mountain waves, reproducing key physical features such as vertical velocity structures, wave amplitude decay, and upstream phase tilt. Comparative tests further revealed efficiency gains of up to 71.4% over fixed meshes and 23.8% over traditional AMR at high resolution, alongside accuracy improvements in vertical velocity, potential temperature, and wave propagation. These findings validate the LSTM‐based AMR framework as a robust and efficient approach for atmospheric simulations over complex terrain. By intelligently allocating computational resources while preserving physical accuracy, this method offers a scalable pathway toward next‐generation atmospheric modeling, with future applications targeted at realistic meteorological conditions over the TP.