The rapid evolution of deepfake techniques presents dual challenges for detection models: adapting to continuously shifting attack distributions while retaining previously learned knowledge. Although recent continual deepfake detection methods have made progress, they often rely on replay-based training, which limits scalability and deployment. Meanwhile, the task structure of deepfake detection offers a unique opportunity that remains under-explored: it is inherently a binary classification problem with a fixed label space, where the main difficulty lies in distributional drift rather than class expansion. This insight enables the modeling of each incremental distribution shift as a dedicated expert, focusing on specific forgery patterns. To this end, we propose a novel analytically driven, replay-free continual detection framework that eliminates the need for iterative gradient updates. In this framework, task-specific experts are constructed via closed-form ridge regression, requiring only a single forward pass and ensuring non-interference with previous tasks. To enhance the model's capacity for fine-grained forgery recognition, we introduce a lightweight Forgery-Aware Residual Enhancer (FARE). At inference, an Uncertainty-Guided Expert Selection module (UGES) dynamically routes each sample to the most confident expert, which does not require prior knowledge of the attack type. The proposed framework achieves a favorable trade-off between efficiency, privacy, and generalization. It achieves state-of-the-art performance across four benchmark datasets, with an average accuracy of 91.82% and only 1.78% forgetting. Notably, it improves cross-forgery generalization by 9.28% on unseen forgery types, demonstrating strong generalization.

Identifying influential spreaders is crucial for understanding the dynamics of information diffusion within complex networks. Several centrality methods have been proposed to address this, but these studies often concentrate on only one aspect. To solve this problem, we introduce a dual-perspective approach which considers both global and local perspectives for identifying influential nodes in complex networks. From a global perspective, if a node has the capability to efficiently transmit information to various clusters within a network, then the information originating from that node will quickly spread across a large area. From a local perspective, when a node has a greater number of neighbors—especially those that are significant within the network—the information emanating from that node is less likely to be confined to a localized region. Based on this understanding, we first design a novel clustering method to detect groups in which the connections among nodes are denser than those with the rest of the network. The most influential nodes in each group are identified as global critical nodes. Subsequently, the local influence of a node is defined by the number and significance of its neighboring nodes. Ultimately, nodes are ranked according to their local influence, their proximity to the global critical nodes using the shortest paths, and the importance of these global critical nodes. To evaluate the performance of the proposed method, the susceptible-infected-removed (SIR) diffusion model is used. Results of the investigation on real networks and realistic synthetic benchmarks show that the proposed method can identify nodes with high influence better than other centrality methods.

Solving the multi-UAV task allocation problem with complex constraints (MTAPCc) by means of the constrained multi-objective evolutionary algorithms (cMOEAs) is novel research in the field of Operation Research. Its advantages mainly consist of two aspects. One is that it can find feasible solutions that satisfy the constraints within an acceptable time. The other is that the obtained Pareto solution set can offer more options for decision-makers. This paper presents a dualneighborhood search based dual-population coevolutionary algorithm (ccDNCA), which can specifically solve the constrained multi-objective combinatorial optimization problems (cMCOPs) based on permutation encoding, including the MTAPCc. The dual-population coevolutionary framework and the multistrategy collaborative constraint handling method of ccDNCA can effectively improve the efficiency of constraint handling and the ability of finding better solutions. The dual-neighborhood alternating local search (DN-ALS) framework can effectively increase the proportion of feasible solutions during the evolution and enhance the quality of the final solution set. The strategy pool integrated with multiple local search strategies can push the search towards regions with better objective values and constraint values, while enhancing the generalization ability of ccDNCA. In the experimental part, by comprehensively comparing the solution results of ccDNCA with those of other advanced algorithms, it is demonstrated that ccDNCA has significant superiority when dealing with cMCOPs based on permutation encoding, such as the MTAPCc and the Vehicle Routing Problem with Time Window constraints (VRPTW).

Test-time adaptation (TTA) has gained increasing popularity due to its efficacy in addressing "distribution shift" issue while simultaneously protecting data privacy. However, most prior methods assume that a paired source domain model and target domain sharing the same label space coexist, heavily limiting their applicability. In this paper, we investigate a more general source model capable of adaptation to multiple target domains without needing shared labels. This is achieved by using a pre-trained vision-language model (VLM), e.g., CLIP, that can recognize images through matching with class descriptions. While the zero-shot performance of VLMs is impressive, they struggle to effectively capture the distinctive attributes of a target domain. To that end, we propose a novel method - Context-aware Language-driven TTA (COLA). The proposed method incorporates a lightweight context-aware module that consists of three key components: a task-aware adapter, a context-aware unit, and a residual connection unit for exploring task-specific knowledge, domain-specific knowledge from the VLM and prior knowledge of the VLM, respectively. It is worth noting that the context-aware module can be seamlessly integrated into a frozen VLM, ensuring both minimal effort and parameter efficiency. Additionally, we introduce a Class-Balanced Pseudo-labeling (CBPL) strategy to mitigate the adverse effects caused by class imbalance. We demonstrate the effectiveness of our method not only in TTA scenarios but also in class generalisation tasks. The source code is available at https://github.com/NUDT-Bai-Group/COLA-TTA.

Lip reading aims at transforming the videos of continuous lip movement into textual contents, and has achieved significant progress over the past decade. It serves as a critical yet practical assistance for speech-impaired individuals, with more practicability than speech recognition in noisy environments. With the increasing interpersonal communications in social media owing to globalization, the existing monolingual datasets for lip reading may not be sufficient to meet the exponential proliferation of bilingual and even multilingual users. However, to our best knowledge, research on code-switching is only explored in speech recognition, while the attempts in lip reading are seriously neglected. To bridge this gap, we have collected a bilingual code-switching lip reading benchmark composed of Chinese and English, dubbed CSLR. As the pioneering work, we recruited 62 speakers with proficient foundations in bothspoken Chinese and English to express sentences containing both involved languages. Through rigorous criteria in data selection, CSLR benchmark has accumulated 85,560 video samples with a resolution of 1080x1920, totaling over 71.3 hours of high-quality code-switching lip movement data. To systematically evaluate the technical challenges in CSLR, we implement commonly-used lip reading backbones, as well as competitive solutions in code-switching speech for benchmark testing. Experiments show CSLR to be a challenging and under-explored lip reading task. We hope our proposed benchmark will extend the applicability of code-switching lip reading, and further contribute to the communities of cross-lingual communication and collaboration. Our dataset and benchmark are accessible at https://github.com/cslr-lipreading/CSLR.

During the COVID-19 pandemic, numerous people experiencing illness or senescence choose to receive home health care (HHC) services. However, a rapid increase in patients makes it a challenge to reasonably allocate nurses to provide HHC services under the condition of a paucity of nurse resources and patient time window constraints. To solve the large-scale HHC problem, a hybrid heuristic-exact optimization algorithm is proposed with three novel contributions. First, a framework of hybrid heuristic-exact optimization is designed to solve the large-scale problem where a reasonable solution is difficult to obtain under constraints. Second, a multi-objective mixed-integer linear programming modelization is formulated to get a more diverse nurse assignment. Finally, an improved branch and bound algorithm is proposed to speed up computation for the large-scale problem. Computational results on different HHC instances from 25 to 1000 patients demonstrate that the proposed algorithm can optimize the HHC problem with more than 100 patients and can provide various assignments for different numbers of nurses, which the common algorithm cannot optimize.

Class incremental learning has yet to be explored in the field of lip-reading, which can circumvent data privacy issues and avoid the high training costs associated with joint training. In this paper, we introduce a benchmark for Class-Incremental Lip-Reading (CILR). To simultaneously improve the plasticity for new classes and stability for old classes in incremental learning, we propose a Slow-Fast Time Parameter Aggregation Network (TPAN) that decouples representation learning of new and old knowledge, taking into account the task characteristics of lip-reading. The TPAN comprises two dynamically evolving branches: one that uses fast gradient descent and the other employs slow momentum updates to retain old knowledge while adapting to new knowledge. Additionally, to achieve efficient knowledge transfer of the incremental model, we design a Hybrid Sequence-Distribution Distillation (HSDD) strategy to transfer knowledge in temporal feature view and classification probability view. We present a comprehensive comparison of the proposed method and previous state-of-the-art class incremental learning methods on the most commonly used lip-reading datasets LRW and LRW1000. The experimental result show that the proposed method can reduce the effect of catastrophic forgetting and improve the incremental accuracy.