The management of large-scale events poses significant resilience challenges to urban traffic systems, where classical computational methods often prove inadequate for the resulting complex, dynamic optimization problems. This paper introduces a quantum-enhanced hybrid high-performance computing framework designed to improve traffic resilience during such events. The framework leverages a Quantum Annealing (QA) algorithm to solve a Quadratic Unconstrained Binary Optimization (QUBO) model of key traffic optimization tasks, integrated with classical computing for data processing and workflow control. Using the traffic management for a major concert at Hefei Luogang Park as a case study, we focus on modeling and applying this framework to two core resilience problems: Dynamic Evacuation Route Optimization (DERO) and Emergency Resource Allocation (ERA). Our simulation results provide empirical validation of the framework's effectiveness, demonstrating that the proposed quantumenhanced approach reduces total evacuation time by 20.0 % and average emergency response time by 32.2 % compared to welldefined greedy heuristics. These findings highlight the potential of quantum computing as an emerging high-performance computing paradigm for addressing the intricate challenges of traffic resilience.

The intensified concerns arising from the widespread adoption of deep learning have led to increased scrutiny of intellectual property protection in DNN models. Existing audio watermarking techniques, predominantly based on traditional signal processing methods, struggle to balance robustness, imperceptibility, and defense resistance in the face of evolving adversarial attacks. These limitations underscore the urgent need for more effective watermarking solutions in the audio domain. In this paper, we propose a dynamic audio watermarking framework that introduces an optimization-based approach to attach robust and adaptable triggers at arbitrary positions within audio signals, and innovatively integrates boundary sample selection driven by forgetting events and an adaptive watermark trigger embedding technique based on the SNR. Comprehensive experimental results reveal that our scheme preserves high model performance while maintaining remarkable stealthiness and robustness, offering a secure and reliable solution for safeguarding intellectual property in the audio domain and advancing the field of DNN watermarking.

Federated learning (FL) is a promising approach for privacy-preserving machine learning, enabling collaborative model training across distributed devices without sharing raw data. However, FL faces significant challenges due to the nonindependent and identically distributed (non-IID) nature of data across devices, leading to difficulties in model convergence and generalization. In this paper, we propose FedDiT, a novel federated learning framework that combines knowledge distillation with vision transformers. FedDiT introduces the Distilled Vision Transformer (DTViT) model on the client side, incorporating a distillation token to enhance local learning and knowledge transfer. This approach significantly improves the robustness and performance of FL in non-IID environments. We validated FedDiT through extensive experiments on public datasets, and the results show that it outperforms existing FL methods in both accuracy and smoother convergence. Additionally, FedDiT achieves higher throughput compared to standard transformers and knowledge distillation methods, making it more efficient for practical deployment in federated learning scenarios.

Low-rank approximation (LRA) is a powerful technique for seismic diffraction separation and imaging, providing higher-resolution images of subsurface discontinuities compared to traditional reflection imaging. However, in complex wavefields where reflections lack distinct low-rank characteristics, diffractions and reflections can overlap within the same eigenimages, making traditional LRA less effective for separation. To address this limitation, we propose a diffraction separation and imaging method based on multi-directional wavefield low-rank approximation (MDWLRA). The MDWLRA method employs multi-directional wavefield decomposition (MDWD) to divide complex wavefields into angular slices with similar dip angles. These slices are classified as either diffraction slices or reflection slices, with the latter containing mostly reflections and some residual diffractions. LRA is then applied to the reflection slices to separate the remaining diffractions from reflections. By reducing wavefield complexity using MDWD, reflections in the reflection slices exhibit clearer low-rank characteristics than those in the full wavefields, allowing for more effective separation using LRA. Numerical tests on synthetic data from the modified Sigsbee2A model and field data demonstrate that the MDWLRA method outperforms traditional methods, achieving more accurate separation with fewer leakages than traditional LRA, while also improving diffraction fidelity compared to the Curvelet-transform-based method.

Software defect prediction has always been a hot research topic in the field of software engineering owing to its capability of allocating limited resources reasonably. Compared with cross-project defect prediction (CPDP), heterogeneous defect prediction (HDP) further relaxes the limitation of defect data used for prediction, permitting different metric sets to be contained in the source and target projects. However, there is still a lack of a holistic understanding of existing HDP studies due to different evaluation strategies and experimental settings. In this paper, we provide an empirical study on HDP approaches. We review the research status systematically and compare the HDP approaches proposed from 2014 to June 2018. Furthermore, we also investigate the feasibility of HDP approaches in CPDP. Through extensive experiments on 30 projects from five datasets, we have the following findings: (1) metric transformation-based HDP approaches usually result in better prediction effects, while metric selection-based approaches have better interpretability. Overall, the HDP approach proposed by Liet al.(CTKCCA) currently has the best performance. (2) Handling class imbalance problems can boost the prediction effects, but the improvements are usually limited. In addition, utilizing mixed project data cannot improve the performance of HDP approaches consistently since the label information in the target project is not used effectively. (3) HDP approaches are feasible for cross-project defect prediction in which the source and target projects have the same metric set.