Fractional repetition codes (FRCs)/Generalized FRCs (GFRCs) are classes of minimum bandwidth regenerating codes that play a key role in distributed storage systems. In this paper, first, we explore the inherent connection between biregular bipartite graphs and FRCs/GFRCs. Using the structural characteristics of these graphs, we determine the lower bound of the file size of the FRCs/GFRCs related to these graphs. Specifically, we leverage the eigenvalues of biregular bipartite graphs and derive several lower bounds on the file size of FRCs/GFRCs with flexible parameters and reconstruction degrees.

Target-oriented multimodal sentiment classification seeks to predict sentiment polarity for specific targets from image-text pairs. While existing works achieve competitive performance, they often over-rely on textual content and fail to consider dataset biases, in particular word-level contextual biases. This leads to spurious correlations between text features and output labels, impairing classification accuracy. In this paper, we introduce a novel counterfactual-enhanced debiasing framework to reduce such spurious correlations. Our framework incorporates a counterfactual data augmentation strategy that minimally alters sentiment-related causal features, generating detail-matched image-text samples to guide the model’s attention toward content tied to sentiment. Furthermore, for learning robust features from counterfactual data and prompting model decisions, we introduce an adaptive debiasing contrastive learning mechanism, which effectively mitigates the influence of biased words. Experimental results on several benchmark datasets show that our proposed method outperforms state-of-the-art baselines.

Vehicular edge computing (VEC) is an emerging computing paradigm that is rapidly advancing the development of the Internet of Vehicles (IoV). However, edge server has limited data storage capacity and computing resource, making it difficult to handle the massive offloading requests from IoV applications. Moreover, the mobility of vehicles and dynamic data traffic make it highly challenging to design optimal offloading and resource allocation strategies. To address the challenges mentioned above, we design a cloud-edge–vehicle hierarchical architecture for IoV task offloading, introducing a cloud server to assist in computation and alleviate the overload pressure on edge server. Considering the impact of vehicle mobility on task offloading, we propose a mobility detection method to predict which vehicles might leave the communication range of the base station, thereby preventing task offloading failures. Additionally, to achieve efficient task offloading and resource allocation in this complex IoV system, we propose a multiagent-reinforcement-learning-based vehicle proposal offloading algorithm (MVPOA). This algorithm enables vehicles to autonomously decide whether to process tasks locally or propose offloading to edge server. The edge server then decides whether to accept offloading requests based on task priority and sends rejected tasks to cloud server for processing, thereby maximizing the utilization of resources at each layer of the system. Simulation results demonstrate that MVPOA outperforms other baseline approaches in optimizing system delay and energy consumption.

Heterogeneous graph neural networks (HGNNs) have recently demonstrated significant advantages of capturing powerful structural and semantic information in heterogeneous graphs. Different from homogeneous graph neural networks directly aggregating information based on neighbors, HGNNs aggregate information based on complex metapaths. ReRAM-based processing-in-memory (PIM) architecture can reduce data movement and compute matrix-vector multiplication (MVM) in analog. It can be well used to accelerate HGNNs. However, the complex metapath-based aggregation of HGNNs makes it challenging to efficiently utilize the parallelism of ReRAM and vertices data reuse. To this end, we propose ChainPIM, the first ReRAM-based processing-in-memory accelerator for HGNNs featuring high-computing parallelism and vertices data reuse. Specifically, we introduce R-chain, which is based on a chain structure to build related metapath instances together. We can efficiently reuse vertices through R-chain and process different R-chains in parallel. Then, we further design an efficient storage format for storing R-chains, which reduces a lot of repeated vertices storage. Finally, a specialized ReRAM-based architecture is developed to pipeline different types of aggregations in HGNNs, fully exploiting the huge potential of multilevel parallelism in HGNNs. Our experiments show that ChainPIM achieves an average memory space reduction of 47.86% and performance improvement by$128.29\times $compared to NVIDIA Tesla V100 GPU.