To reduce traffic accidents caused by drowsy driving, computer vision techniques have been widely adopted to analyze facial cues and assess driver fatigue levels. However, existing methods suffer from several limitations. Most approaches extract features from only one or two facial regions (e.g., the eyes and mouth), which restricts their ability to capture variations in drowsiness expression across different fatigue levels within the same driver, as well as individual differences among drivers. Moreover, although facial regions are inherently structured, current models typically lack structural awareness, resulting in weakly structured feature representations that may lose subtle drowsiness-related details. To address these issues, we propose a Feature-Enhanced Channel Topology Graph Convolutional Network (FE-CTGCN). The proposed framework consists of three key modules: the Feature-Enhanced Global–Local Information Module (FEGL), the Multi-source Information Representation Module (MSIR), and the Channel-wise Topology Graph Convolution Module (CTGCN). The FEGL module extracts visual features from five local facial regions and the entire face, with an emphasis on enhancing discriminative patterns across different fatigue states. The MSIR module introduces an attention-based feature fusion mechanism that not only integrates multi-source features via attention but also captures temporal dynamics, enabling effective modeling of correlations between global and local facial cues. Together, FEGL and MSIR address intra-driver variations across drowsiness levels and inter-driver differences. In addition, the CTGCN module constructs a topology-aware graph where nodes correspond to the five local facial features and the global facial representation. By modeling spatial relationships among these nodes, it facilitates structured information exchange and builds a strongly structured facial feature space that enhances internal feature integration. Experimental results demonstrate that FE-CTGCN achieves superior detection performance compared to existing methods, validating its effectiveness for driver drowsiness detection. To facilitate reproducibility and further research, the source code is available athttps://github.com/zzs-code/FECTGCN.git

This study aims to explore how to improve the reliability of the next-generation mobility model—Mobility as a Service (MaaS) based on autonomous vehicles, with a particular focus on the system’s resilience to uncertainty. Currently, the application of reliability engineering in the field of smart mobility services is primarily concentrated on technical details, lacking unified standards and methods to enhance the reliability of service levels. This paper attempts to fill this research gap. In this study, we adopt a combined approach of system analysis and optimization algorithms. First, we design a system reliability analysis method by examining the potential discrepancies between the system’s capability to provide mobility services and stakeholder demands. Subsequently, we propose a reinforcement learning-based system service capacity optimization algorithm aimed at enhancing the system’s resilience at the service level to tackle challenges posed by uncertainty. To validate the effectiveness of the proposed method, we conduct a case study on a practical intelligent mobility service framework. Through system simulation, we generate and collect data on system service capacity, demand discrepancies, and uncertainty, as well as stakeholders’ expectations for the MaaS framework evaluation. Case studies and experimental data analysis confirm that the proposed resilience engineering approach effectively identifies potential risks in system service capacity and provides a compromise system resilience engineering solution in the context of conflicting stakeholder demands. To facilitate reproducibility and further research, the core code is available at https://github.com/zzs-code/MaaS-RE-MARL.git.

In this paper, we focus on enhancing a diffusion-based text-to-video (T2V) model during the post-training phase by distilling a highly capable consistency model from a pretrained T2V model. Our proposed method, T2V-Turbo-v2, introduces a significant advancement by integrating various supervision signals, including high-quality training data, reward model feedback, and conditional guidance, into the consistency distillation process. Through comprehensive ablation studies, we highlight the crucial importance of tailoring datasets to specific learning objectives and the effectiveness of learning from diverse reward models for enhancing both the visual quality and text-video alignment. Additionally, we highlight the vast design space of conditional guidance strategies, which centers on designing an effective energy function to augment the teacher ODE solver. We demonstrate the potential of this approach by extracting motion guidance from the training datasets and incorporating it into the ODE solver, showcasing its effectiveness in improving the motion quality of the generated videos with the improved motion-related metrics from VBench and T2V-CompBench. Empirically, our T2V-Turbo-v2 establishes a new state-of-the-art result on VBench, **with a Total score of 85.13**, surpassing proprietary systems such as Gen-3 and Kling.

A major challenge for Multi-Agent Systems (MAS) is enabling agents to adapt dynamically to diverse environments in which opponents and teammates may continually change. Agents trained using conventional methods tend to excel only within the confines of their training cohorts; their performance drops significantly when confronting unfamiliar agents. To address this shortcoming, we introduce Inverse Attention Agents that adopt concepts from the Theory of Mind (ToM) implemented algorithmically using an attention mechanism trained in an end-to-end manner. Crucial to determining the final actions of these agents, the weights in their attention model explicitly represent attention to different goals. We furthermore propose an inverse attention network that deduces the ToM of agents based on observations and prior actions. The network infers the attentional states of other agents, thereby refining the attention weights to adjust the agent's final action. We conduct experiments in a continuous environment, tackling demanding tasks encompassing cooperation, competition, and a blend of both. They demonstrate that the inverse attention network successfully infers the attention of other agents, and that this information improves agent performance. Additional human experiments show that, compared to baseline agent models, our inverse attention agents exhibit superior cooperation with humans and better emulate human behaviors.

In-memory databases (IMDBs) have been the backbone of modern systems that demand high throughput and low latency. Because of the cost and volatility of DRAM, IMDBs become incompetent when dealing with workloads that require large data volume and strict durability. The emergence of non-volatile memory (NVM) brings new opportunities for IMDBs to tackle this situation. However, it is non-trivial to build an NVM-based IMDB, due to performance degradation, NVM programming complexity, and other challenges. In this paper, we present Tair-PMem , an NVM-based enterprise-strength database atop Redis, the most popular IMDB. Tair-PMem adopts a well-controlled data layout and a log-as-user-data design to mitigate NVM overheads. It eases the NVM programming complexity by providing a hybrid memory programming toolkit. To better leverage the enterprise-strength features and implementations from Redis, Tair-PMem retrofits it in a less intrusive way to achieve full compatibility and stability, while retaining its advanced features. With all of the above techniques elaborately implemented, Tair-PMem satisfies full durability, high throughput, and low latency at the same time. Tair-PMem has now been publicly available as a cloud service on Alibaba Cloud. To the best of our knowledge, Tair-PMem is the first cloud service that makes good use of the persistence capability of NVM.

Reconstructing the depth information from the 3D scene using stereo vision is a key element in the development of advanced driver assistance systems. We previously proposed a novel real-time stereo matching method based on the Multi-paths Viterbi that outperforms the well-known SGBM (Semi-Global Block-Matching Algorithm) algorithm in both disparity accuracy and density. In this paper, we extend the previous framework to estimate the depth information for challenging environments such as nighttime. Estimating the depth at nighttime is generally challenging as the night images are dark and noisy and the estimated depth information is not accurate. In our proposed work, we modify the non-local means filter and propose a new non-local cost function to combine the noise reduction and stereo vision within a single framework. We evaluate our proposed algorithm on both natural and synthetic datasets and show that the proposed algorithm can significantly improve the quality of the stereo results in the low light condition. Moreover, our proposed method can be implemented in real-time for autonomous driver applications.

This paper proposes an image fusion algorithm based on a sparse linear equation system, which uses local extreme of high resolution image and intensity of multi-spectral image to construct the system. Based on this sparse system, the multi-scale image decomposition algorithm can be implemented. This algorithm extracts the details of the high-resolution image and integrates with the multi-spectral information to derive the fused image.