Flexible spines are critical to the remarkable agility and speed of animals. Translating this biological advantage to quadruped robots presents a significant control challenge, particularly in coordinating the spine and limbs for maximal velocity. In this work, we utilize reinforcement learning (RL) to develop high-speed locomotion for a bioinspired mouse robot with a lateral flexible spine. The resulting controller achieves motor performance that demonstrably surpasses non-spined and model-based methods. More importantly, our analysis reveals the principles behind this performance: the emergence of two distinct motion templates. For high-speed walking, the robot learns a “whip-like” spinal oscillation to increase leg swing frequency, while for agile turning, it adopts a dynamic “bend-and-straighten” pattern. These findings demonstrate the capability of RL to not only generate high-performance controllers but also to produce emergent strategies that, upon analysis, reveal underlying principles of high-speed, spine-driven locomotion.

The increasing volume of radiology examinations has created an urgent need for automated report generation that is comparable to those written by radiologists. The major challenge is achieving precise semantic alignment between images and text, ensuring that generated reports accurately capture and describe the visual findings in medical images. Current approaches often struggle with this alignment, compromising diagnostic accuracy and clinical utility. To address these challenges, we present Adaptive Semantic Alignment Method (ASAM), a novel framework that enhances cross-modal semantic alignment through two innovations. First, we introduce a gated disease knowledge base by memory matrix that provides structured medical context to guide the mapping between image and report modalities. Second, we develop a cross-modal pre-trained visual encoder that enriches feature representation through improved understanding of medical imaging characteristics. Extensive experiments demonstrate that ASAM achieves state-of-the-art performance on two public chest X-ray datasets in BLEU-n metrics.

Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models, while recent deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Transformers, have improved performance by effectively analyzing image features. However, CNNs struggle with long-range dependencies, and Transformers demand significant computational resources. To address these limitations, we propose DehazeSNN, an innovative architecture that integrates a U-Net-like design with Spiking Neural Networks (SNNs). DehazeSNN captures multi-scale image features while efficiently managing local and long-range dependencies. The introduction of the Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) enhances cross-channel communication, resulting in superior dehazing performance with reduced computational burden. Our extensive experiments show that DehazeSNN is highly competitive to state-of-the-art methods on benchmark datasets, delivering high-quality haze-free images with a smaller model size and less multiply-accumulate operations. The proposed dehazing method is publicly available at https://github.com/HaoranLiu507/DehazeSNN.

With the increasing integration of renewable energy into power grids, integrated energy systems (IES) have gained global attention. However, the stochastic and uncertain characteristics of wind and solar challenge the stable grid operation significantly. To address these issues, this article proposes an optimal scheduling model for wind-solar-hydrogen IES, incorporating prediction uncertainty and demand response (DR). The equilibrium decision theory framework, combined with frequency enhanced decomposed transformer, interval prediction and decision, is proposed to quantify the uncertainties and determine the action plans. Then, an optimal scheduling model is designed to maximize economic revenue and minimize pollution emissions (PE), which is solved using multiobjective elephant clan optimization algorithm. Furthermore, DR strategy is integrated to enhance system flexibility and stability. Empirical results demonstrate that increased uncertainty leads to more conservative scheduling, improving system robustness. Additionally, DR boosts ER by over 3.50% and reduces PE by more than 3.20% across various scenarios.

The integration of Unmanned Aerial Vehicles (UAVs) and Wireless Power Transfer (WPT) is promising to provide charging and communication services to on-ground devices. However, traditional studies of UAVs at a fixed height to provide services to devices, ignoring the flexible characteristic of UAVs. To fully exploit the potential of UAVs in wearable networks, we optimize the height of UAVs to maximize the network throughput and minimize the task completion time. Considering the UAV coverage and energy constraints, we formulate the service time allocation and UAV height control optimization problem as a Constrained Markov Decision Process (CMDP). Then, we propose a Lagrangian-based Proximal Policy Optimization (PPO)-CMDP algorithm to optimize the primal pair-based policy optimization problem. Finally, simulation evaluation shows that our proposed algorithm has significant advantages in terms of network throughput and task completion time.