Image retouching aims to enhance visual quality while aligning with users' personalized aesthetic preferences. To address the challenge of balancing controllability and subjectivity, we propose a unified diffusion-based image retouching framework called PerTouch. Our method supports semantic-level image retouching while maintaining global aesthetics. Using parameter maps containing attribute values in specific semantic regions as input, PerTouch constructs an explicit parameter-to-image mapping for fine-grained image retouching. To improve semantic boundary perception, we introduce semantic replacement and parameter perturbation mechanisms during training. To connect natural language instructions with visual control, we develop a VLM-driven agent to handle both strong and weak user instructions. Equipped with mechanisms of feedback-driven rethinking and scene-aware memory, PerTouch better aligns with user intent and captures long-term preferences. Extensive experiments demonstrate each component’s effectiveness and the superior performance of PerTouch in personalized image retouching.

Marine intelligent transportation systems (M-ITS) face challenges in achieving adaptability and efficiency under dynamic spatiotemporal (ST) environments. This paper proposes a novel multimodal underwater transformable agent (MUTA) designed as an adaptive and efficient information node for M-ITS. The MUTA integrates a biomimetic morphing wing, multi-drive propulsion system, incremental environmental perception, and an uncertainty-aware dynamic modeling framework to switch smoothly among long-range (LR) cruising, high-maneuverability (HM) operation, and synergy modes. Comprehensive lake and sea trials demonstrate that the MUTA maintains high motion precision and achieves significant energy efficiency improvements, reaching depths up to 1200 m and covering a range of over 3000km. The MUTA has achieved over 17% efficiency gain in lake and marine environments. This work provides an adaptive and efficient solution for M-ITS under uncertain marine conditions.

Underwater robots are critical observation platforms for diverse ocean environments. However, existing robotic designs often lack long-range and deep-sea observation capabilities and overlook the effects of environmental uncertainties on robotic operations. This paper presents a novel long-range underwater robot for extreme ocean environments, featuring a low-power dual-circuit buoyancy adjustment system, an efficient mass-based attitude adjustment system, flying wings, and an open sensor cabin. After that, an extended environment perception strategy with incremental updating is proposed to understand and predict full hydrological dynamics based on sparse observations. On this basis, a real-time dynamic modeling approach integrates multibody dynamics, perceived hydrological dynamics, and environment-robot interactions to provide accurate dynamics predictions and enhance motion efficiency. Extensive simulations and field experiments covering 600 km validated the reliability and autonomy of the robot in long-range ocean observations, highlighting the accuracy of the extended perception and real-time dynamics modeling methods.

The research on continuous control in reinforcement learning has been a hot topic in recent years. The Deep Deterministic Policy Gradient (DDPG) algorithm performs well in continuous control tasks. DDPG algorithm uses experience replay mechanism to train the network model, in order to further improve the efficiency of experience replay mechanism in the DDPG algorithm, the cumulative reward is used as the transiton classification basis, a Deep Deterministic Policy Gradient with Episodic Experience Replay (EER-DDPG) algorithm is proposed. First of all, the transitions are stored in the unit of episode, and two replay buffers are introduced respectively to classify the transitions according to the cumulative reward. Then, the quality of policy can be improved in network model training period by random samping of the episodes with large cumulative rewards. In the continuous control tasks, this algorithm is verified by experiments, and compared with DDPG algorithm, Trust Region Policy Optimization (TRPO) algorithm and Proximal Policy Optization (PPO) algorithm. The experimental results show that EER-DDPG algorithm has better performance.

The Vector Quantized Variational AutoEncoder (VQ-VAE) has shown great potential in image generation, especially the methods with hierarchical features. However, the lack of decoupling of structural information between hierarchical features leads to semantic inconsistencies and redundant structural features, resulting in incompatible outputs. In this study, we propose the Adaptively Hierarchical Quantization Variational AutoEncoder (AHQ-VAE) to generate high-fidelity images with a unified structure. To ensure the semantic consistency of continuous space, we employ the Spatially Consistent Semantic Embedding (SCSE) module to align the hierarchical features, while decoupling global structural information and local details. To ensure the consistency of discrete space, we introduce the Adaptive Bottom Quantizer (ABQ) to generate the quantized bottom codes consistent with quantized top codes, so that the local details can adapt to the global semantics. Extensive experiments demonstrate our approach can generate high-quality images with a unified structure.

Two-dimensional layered semiconductors have attracted a great deal of attention recently from many fields of science and technology. This article overviews the major progress from the perspective of light-emission properties and related device applications of such 2-D materials. We begin with the overview of basic optical properties, including emission features of various excitonic complexes, many-body effects, light-emission enhancement due to plasmonic coupling, and mechanisms of optical gain. This is followed by discussions of coupling of 2-D materials with an optical cavity, including cavity-enhanced emission due to the Purcell effect, strong and weak coupling, as well as lasing behavior. We then discuss the design and the fabrication of various heterostructures by stacking layers of different 2-D materials for the purpose of confining and injecting charge carriers. Such structures are indispensable for light-emitting devices under electrical injection, the ultimate goal of any semiconductor-based light-emitting diode (LED) or lasers. The progress in electrical injection devices is reviewed next, where LEDs under lateral and vertical electrical injection schemes are discussed. The review is concluded with an outlook and future perspectives.