Short-term traffic speed prediction under limited historical data is crucial for intelligent transportation systems, enabling real-time traffic management and congestion mitigation. However, existing methods relying on long lookback window to forecast struggle to capture scenarios where the current traffic state depends more on recent patterns than on distant historical data. To address this challenge, we propose the Spatiotemporal Visibility Graph Network (STVGN), a novel framework that combines visibility graph theory with Graph Neural Networks (GNN). The core of STVGN is the STVGN-Embedding layer, which is designed to enhance short-term spatiotemporal feature representation. This layer leverages the visibility graph’s ability to capture transient patterns and integrates GNNs to model intrinsic relationships within the visibility graph derived from time series data. In Combination with a transformer architecture, STVGN-Embedding extracts complex spatiotemporal features, while the transformer uncovers inherent relationships between temporal and spatial dimensions. To the best of our knowledge, this is the first study to introduce visibility graph theory as an embedding layer within a transformer framework. Experiments on three benchmark datasets, covering urban and freeway traffic scenarios, demonstrate STVGN’s effectiveness, achieving state-of-the-art performance with improvements of 4.8%–16.8% in RMSE and 8.4%–13.3% in MAE over existing SOTA methods. These results highlight the potential of visibility graph-based embeddings to address challenges posed by limited historical data and to capture intricate traffic patterns.

Human joints enable precise bending for fine manipulation and complex movements. Similarly, robotic flexibility relies on bending structures, where accurate bending perception is crucial for precise control and enhanced humanrobot interaction. This paper proposes a C-shaped fiber optic array, embedding a fiber Bragg Grating sensor array into a 2 mm thick silicone layer, successfully achieving a highly sensitive (300 pm/N) and electromagnetic interference-resistant bending sensor. The flexible sensor can sensitively detect external stimuli, such as the touch of a 1g weight or a feather, and exhibits a good linear relationship with curvature, facilitating accurate curvature classification. Additionally, leveraging the wearable nature of the sensor, we achieved the detection of finger bending angles. Finally, by attaching the sensor to the wrist and combining it with deep learning algorithms, we achieved 100% gesture recognition accuracy. This sensor holds significant potential for applications in fields such as fruit size classification, rehabilitation healthcare, and human-robot interaction.

As one of the most popular and sought-after generative models in recent years, diffusion models have sparked the interests of many researchers and steadily shown excellent advantage in various generative tasks such as image synthesis, video generation, bioinformatics engineering, 3D scene rendering and multimodal generation, relying on their dense theoretical principles and reliable application practices. The remarkable success of these recent efforts on diffusion models comes largely from progressive design principles and efficient architecture, training, inference, and deployment methodologies. However, there has not been a comprehensive and in-depth review to summarize these principles and practices to help the rapid understanding and application of diffusion models. In this survey, we provide a new efficiency-oriented perspective on these existing efforts, which mainly focuses on the profound principles and efficient practices in architecture designs, model training, fast inference and reliable deployment, to guide further theoretical research, algorithm migration and model application for new scenarios in a reader-friendly way.

Accurate segmentation of water bodies from aerial images is critical for advancing our understanding of climate change, improving flood prevention and mitigation efforts, and supporting ecological monitoring. Recently, deep learning-based methods have made remarkable progress in water body segmentation. However, there still exist a series of challenges in practical applications, including mis-segmentation of low-contrast regions, difficult delineation of complex terrain boundaries, and loss of small water features. While most of existing methods are designed for supervised learning with paired samples, they may also benefit from self-supervised learning techniques such as masked auto-encoders (MAE). In this work, we focus on the water segmentation problem and propose a new water segmentation framework, named MHNet. MHNet integrates a Hybrid Multi-Scale Encoder-Decoder Network, combining convolutional and transformer-based components to effectively capture global context while minimizing computational costs. A key innovation is the adaptation of the MAE mechanism, where masks are applied to multi-scale Restormer block outputs during the training phase, enabling the model to better integrate local and global information, thereby enhancing boundary segmentation accuracy. Additionally, we propose a Multi-Channel Feature Fusion (MCFF) module that synthesizes masked feature maps across scales, reducing redundancy and improving generalization by capturing both fine details and contextual information. Extensive experiments on multiple public datasets demonstrate that MHNet outperforms state-of-the-art methods, highlighting its effectiveness and robustness in water body extraction tasks. MHNet is deployed and performs online predictions via Google’s Vertex AI platform, thereby integrating it into a Geographic Information System (GIS) using Google Earth Engine (GEE) for accurate and efficient extraction of lakes on the Tibetan Plateau.

Conventionally, federated learning aims to optimize a single objective, typically the utility. However, for a federated learning system to be trustworthy, it needs to simultaneously satisfy multiple objectives, such as maximizing model performance, minimizing privacy leakage and training costs, and being robust to malicious attacks. Multi-Objective Optimization (MOO) aiming to optimize multiple conflicting objectives simultaneously is quite suitable for solving the optimization problem of Trustworthy Federated Learning (TFL). In this article, we unify MOO and TFL by formulating the problem of constrained multi-objective federated learning (CMOFL). Under this formulation, existing MOO algorithms can be adapted to TFL straightforwardly. Different from existing CMOFL algorithms focusing on utility, efficiency, fairness, and robustness, we consider optimizing privacy leakage along with utility loss and training cost, the three primary objectives of a TFL system. We develop two improved CMOFL algorithms based on NSGA-II and PSL, respectively, to effectively and efficiently find Pareto optimal solutions and provide theoretical analysis on their convergence. We design quantitative measurements of privacy leakage, utility loss, and training cost for three privacy protection mechanisms: Randomization, BatchCrypt (an efficient homomorphic encryption), and Sparsification. Empirical experiments conducted under the three protection mechanisms demonstrate the effectiveness of our proposed algorithms.

In this paper, the optimal placement and dynamic resource allocation problem has been investigated for multi-UAV enhanced reconfigurable intelligent surface (RIS) assisted wireless network with uncertain time-varying wireless channels. This paper aims to stimulate the potential of RIS by adding mobility to RIS through unmanned aerial vehicles (UAV). A novel UAV optimal placement and dynamic resource allocation technique needs to be developed jointly. A novel online rein-forcement learning based optimal resource allocation algorithm has been designed. Firstly, a deep Q-learning based K-means clustering algorithm is utilized to optimize the deployment of the multi-UAV. Then, an online actor-critic reinforcement learning algorithm is developed to learn the optimal transmit power control as well as mobile RIS phase shift control policy. Compared with conventional learning algorithms, the developed algorithm can learn the optimal resource allocation and multi-UAV placement for mobile RIS-assisted wireless networks in real-time even with uncertain and time-varying wireless channels. Eventually, numerical simulations are provided to demonstrate the effectiveness of developed schemes.

This paper proposed the Actor-critic-mass (ACM) algorithm to solve the discrete-time mean field type tracking control problem without discretization errors. The traditional large-scale multi-agent control problem suffers from computational explosion and communication difficulty due to the team's nearly infinite agent number. To deal with those challenges, the Mean Field Games (MFG) have been introduced in the multi-agent control problem to disconnect the computation and communication complexity with the agent number. However, traditional mean field type control problems mainly studied the continuous- times systems limited by the concerns of losing the solutions' existence or uniqueness caused by discretization. In this paper, the Feynman-Kac formula is utilized to reformulate the continuous MFG equation system into a backward discretized system. Specifically, the Hamiltonian-Jacobi-Bellman (HJB) equation and backward Kolmogorov equation in the form of a backward stochastic differential equation (BSDE) are introduced. Meanwhile, three neural networks (NN), i.e., the actor, critic, and mass NN, are developed to approximate the MFG equation system's discretized solution and the optimal control. Moreover, the stability and convergence of the NNs and the closed-loop system are provided via the Lyapunov stability analysis. Finally, a series of numerical simulations have shown the new discrete algorithm's performance.