Zhifeng Zhao
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View article: Plasma Shape Control via Zero-shot Generative Reinforcement Learning
Plasma Shape Control via Zero-shot Generative Reinforcement Learning Open
Traditional PID controllers have limited adaptability for plasma shape control, and task-specific reinforcement learning (RL) methods suffer from limited generalization and the need for repetitive retraining. To overcome these challenges, …
View article: High-fidelity data-driven dynamics model for reinforcement learning-based control in HL-3 tokamak
High-fidelity data-driven dynamics model for reinforcement learning-based control in HL-3 tokamak Open
Reinforcement learning (RL)-based control in tokamaks offers improved flexibility for nuclear fusion, but typically depends on simulators that can accurately evolve the high-dimensional plasma state. First-principle simulators are often to…
View article: Intelligent operation monitoring and finite element coupled identification of hyperstatic structures
Intelligent operation monitoring and finite element coupled identification of hyperstatic structures Open
The safety, longevity, and healthy operation and maintenance of world-class large bridges are a research hotspot that continues to attract attention from academia and industry. In particular, during the sustainable operation period of larg…
View article: AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air
AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air Open
Operating Large Language Models (LLMs) on edge devices is increasingly challenged by limited communication bandwidth and strained computational and memory costs. Thus, cloud-assisted remote fine-tuning becomes indispensable. Nevertheless, …
View article: Topology-Assisted Spatio-Temporal Pattern Disentangling for Scalable MARL in Large-scale Autonomous Traffic Control
Topology-Assisted Spatio-Temporal Pattern Disentangling for Scalable MARL in Large-scale Autonomous Traffic Control Open
Intelligent Transportation Systems (ITSs) have emerged as a promising solution towards ameliorating urban traffic congestion, with Traffic Signal Control (TSC) identified as a critical component. Although Multi-Agent Reinforcement Learning…
View article: Physics-driven self-supervised learning for fast high-resolution robust 3D reconstruction of light-field microscopy
Physics-driven self-supervised learning for fast high-resolution robust 3D reconstruction of light-field microscopy Open
View article: Multi-agent Uncertainty-Aware Pessimistic Model-Based Reinforcement Learning for Connected Autonomous Vehicles
Multi-agent Uncertainty-Aware Pessimistic Model-Based Reinforcement Learning for Connected Autonomous Vehicles Open
Deep Reinforcement Learning (DRL) holds significant promise for achieving human-like Autonomous Vehicle (AV) capabilities, but suffers from low sample efficiency and challenges in reward design. Model-Based Reinforcement Learning (MBRL) of…
View article: Robust Event-Triggered Integrated Communication and Control with Graph Information Bottleneck Optimization
Robust Event-Triggered Integrated Communication and Control with Graph Information Bottleneck Optimization Open
Integrated communication and control serves as a critical ingredient in Multi-Agent Reinforcement Learning. However, partial observability limitations will impair collaboration effectiveness, and a potential solution is to establish consen…
View article: Select2Drive: Pragmatic Communications for Real-Time Collaborative Autonomous Driving
Select2Drive: Pragmatic Communications for Real-Time Collaborative Autonomous Driving Open
Vehicle-to-everything communications-assisted autonomous driving has witnessed remarkable advancements in recent years, with pragmatic communications (PragComm) emerging as a promising paradigm for real-time collaboration among vehicles an…
View article: Separate Source Channel Coding Is Still What You Need: An LLM-based Rethinking
Separate Source Channel Coding Is Still What You Need: An LLM-based Rethinking Open
Along with the proliferating research interest in Semantic Communication (SemCom), Joint Source Channel Coding (JSCC) has dominated the attention due to the widely assumed existence in efficiently delivering information semantics. Neverthe…
View article: Recent Advancement of Nanocrystal Dosage Forms
Recent Advancement of Nanocrystal Dosage Forms Open
Drug nanocrystal (NC) is a formulation approach, which has been extensively exploited to enhance drug delivery for application in both dissolution rate improvement and sustained release of poorly water-soluble drugs by size reduction and s…
View article: RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms
RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms Open
Intelligent control of Uncrewed Aerial Vehicles (UAVs) swarms has emerged as a critical research focus, and it typically requires the swarm to navigate effectively while avoiding obstacles and achieving continuous coverage over multiple mi…
View article: Adapted Swin Transformer-based real-time plasma shape detection and control in HL-3
Adapted Swin Transformer-based real-time plasma shape detection and control in HL-3 Open
In the field of magnetic confinement plasma control, the accurate feedback of plasma position and shape primarily relies on calculations derived from magnetic measurements through equilibrium reconstruction or matrix mapping method. Howeve…
View article: MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification
MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification Open
We present MERLOT, a scalable mixture-of-expert (MoE) based refinement of distilled large language model optimized for encrypted traffic classification. By applying model distillation techniques in a teacher-student paradigm, compact model…
View article: PNR: Physics-informed Neural Representation for high-resolution LFM reconstruction
PNR: Physics-informed Neural Representation for high-resolution LFM reconstruction Open
Light field microscopy (LFM) has been widely utilized in various fields for its capability to efficiently capture high-resolution 3D scenes. Despite the rapid advancements in neural representations, there are few methods specifically tailo…
View article: High-speed in toto 3D imaging with isotropic resolution by scanning light-field tomography
High-speed in toto 3D imaging with isotropic resolution by scanning light-field tomography Open
In toto imaging of large-scale transparent samples or cleared tissue is in high demand in broad biological applications such as oncology, neuroscience, and developmental biology to understand the functions and organizations of large-scale …
View article: High-Fidelity Data-Driven Dynamics Model for Reinforcement Learning-based Control in HL-3 Tokamak
High-Fidelity Data-Driven Dynamics Model for Reinforcement Learning-based Control in HL-3 Tokamak Open
The success of reinforcement learning (RL)-based control in tokamaks, an emerging technique for controlled nuclear fusion with improved flexibility, typically requires substantial interaction with a simulator capable of accurately evolving…
View article: Adapted Swin Transformer-based Real-Time Plasma Shape Detection and Control in HL-3
Adapted Swin Transformer-based Real-Time Plasma Shape Detection and Control in HL-3 Open
In the field of magnetic confinement plasma control, the accurate feedback of plasma position and shape primarily relies on calculations derived from magnetic measurements through equilibrium reconstruction or matrix mapping method. Howeve…
View article: Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach
Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach Open
Optimizing the deployment of large language models (LLMs) in edge computing environments is critical for enhancing privacy and computational efficiency. Toward efficient wireless LLM inference in edge computing, this study comprehensively …
View article: Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G
Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G Open
In the evolution towards 6G, integrating Artificial Intelligence (AI) with advanced network infrastructure emerges as a pivotal strategy for enhancing network intelligence and resource utilization. Existing distributed learning frameworks …
View article: Prominent involvement of acetylcholine in shaping stable olfactory representation across the Drosophila brain
Prominent involvement of acetylcholine in shaping stable olfactory representation across the Drosophila brain Open
Despite the vital role of neuromodulation in the neural system, the specific spatiotemporal dynamics of neuromodulators and their interactions with neuronal activities in vivo are still unclear, hampering our understanding of their informa…
View article: Topology Data Analysis-based Error Detection for Semantic Image Transmission with Incremental Knowledge-based HARQ
Topology Data Analysis-based Error Detection for Semantic Image Transmission with Incremental Knowledge-based HARQ Open
Semantic communication (SemCom) aims to achieve high fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy. Nevertheless, semantic communication still suffers from unexpected channel volat…
View article: Interplay of Semantic Communication and Knowledge Learning
Interplay of Semantic Communication and Knowledge Learning Open
In the swiftly advancing realm of communication technologies, Semantic Communication (SemCom), which emphasizes knowledge understanding and processing, has emerged as a hot topic. By integrating artificial intelligence technologies, SemCom…
View article: Reinforcement Learning-powered Semantic Communication via Semantic Similarity
Reinforcement Learning-powered Semantic Communication via Semantic Similarity Open
We introduce a new semantic communication mechanism, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. Starting by analyzing the defects of existing joint source channel coding (JS…
View article: Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles
Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles Open
Autonomous Vehicles (AVs) have attracted significant attention in recent years and Reinforcement Learning (RL) has shown remarkable performance in improving the autonomy of vehicles. In that regard, the widely adopted Model-Free RL (MFRL) …
View article: Communication-Efficient Soft Actor-Critic Policy Collaboration via Regulated Segment Mixture
Communication-Efficient Soft Actor-Critic Policy Collaboration via Regulated Segment Mixture Open
Multi-Agent Reinforcement Learning (MARL) has emerged as a foundational approach for addressing diverse, intelligent control tasks in various scenarios like the Internet of Vehicles, Internet of Things, and Unmanned Aerial Vehicles. Howeve…
View article: Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning Open
Generally, Reinforcement Learning (RL) agent updates its policy by repetitively interacting with the environment, contingent on the received rewards to observed states and undertaken actions. However, the environmental disturbance, commonl…
View article: Spatial redundancy transformer for self-supervised fluorescence image denoising
Spatial redundancy transformer for self-supervised fluorescence image denoising Open
Fluorescence imaging with high signal-to-noise ratios has become the foundation of accurate visualization and analysis of biological phenomena. However, the inevitable noise poses a formidable challenge to imaging sensitivity. Here we prov…
View article: Self-Critical Alternate Learning based Semantic Broadcast Communication
Self-Critical Alternate Learning based Semantic Broadcast Communication Open
Semantic communication (SemCom) has been deemed as a promising communication paradigm to break through the bottleneck of traditional communications. Nonetheless, most of the existing works focus more on point-to-point communication scenari…
View article: Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance
Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance Open
Multi-Robot System (MRS) has garnered widespread research interest and fostered tremendous interesting applications, especially in cooperative control fields. Yet little light has been shed on the compound ability of formation, monitoring …