Fangying Chen | Searcharxiv

arXiv2025

MEC Task Offloading in AIoT: A User-Centric DRL Model Splitting Inference Scheme

With the rapid development of the Artificial Intelligence of Things (AIoT), mobile edge computing (MEC) becomes an essential technology underpinning AIoT applications. However, multi-angle resource constraints, multi-user task competition, and the complexity of task offloading decisions in dynamic MEC environments present new technical challenges. Therefore, a user-centric deep reinforcement learning (DRL) model splitting inference scheme is proposed to address the problem. This scheme combines model splitting inference technology and designs a UCMS_MADDPG-based offloading algorithm to realize efficient model splitting inference responses in the dynamic MEC environment with multi-angle resource constraints. Specifically, we formulate a joint optimization problem that integrates resource allocation, server selection, and task offloading, aiming to minimize the weighted sum of task execution delay and energy consumption. We also introduce a user-server co-selection algorithm to address the selection issue between users and servers. Furthermore, we design an algorithm centered on user pre-decision to coordinate the outputs of continuous and discrete hybrid decisions, and introduce a priority sampling mechanism based on reward-error trade-off to optimize the experience replay mechanism of the network. Simulation results show that the proposed UCMS_MADDPG-based offloading algorithm demonstrates superior overall performance compared with other benchmark algorithms in dynamic environments.

cs.NI↗

arXiv2022

A Molecular Hyper-message Passing Network with Functional Group Information

We proposed the molecular hyper-message passing network (MolHMPN) that predicts the properties of a molecule with prior knowledge-guided subgraph. Modeling higher-order connectivities in molecules is necessary as changes in both the pair-wise and higher-order interactions among atoms results in the change of molecular properties. Many approaches have attempted to model the higher-order connectivities. However, those methods relied heavily on data-driven approaches, and it is difficult to determine if the utilized subgraphs contain any properties of interest or have any significance on the molecular properties. Hence, we propose MolHMPN to utilize the functional group prior knowledge and model the pair-wise and higher-order connectivities among the atoms in a molecule. Molecules can contain many types of functional groups, which affect the properties the molecules. For example, the toxicity of a molecule is associated with toxicophores, such as nitroaromatic groups and thiourea. MolHMPN uses functional groups to construct hypergraphs, modifies the hypergraph using domain knowledge-guided modification scheme, embeds the graph and hypergraph inputs using a hypergraph message passing (HyperMP) layer, and uses the updated graph and hypergraph embeddings to predict the properties of the molecules. Our model provides a way to utilize prior knowledge in chemistry for molecular properties prediction tasks, and balance between the usage of prior knowledge and data-driven modification adaptively. We show that our model is able to outperform the other baseline methods for most of the dataset, and show that using domain knowledge-guided data-learning iseffective.

cs.CE↗