In mobile edge computing (MEC), edge caching (EC), i.e., deploying data closer to end users at the network edge layer, has become one of the most active research areas for mitigating the high latency caused by massive data in centralized storage systems. However, when caching data that are sensitive to freshness, distributed EC systems often face severe strong content consistency challenges, as such data undergo frequent updates and modifications over long usage cycles. To address the strong consistency issue faced by freshness-sensitive data in resource-constrained MEC scenarios, we innovatively reformulate this challenge as a content replacement problem. The objective is to minimize long-term content transmission costs while maintaining strong content consistency. More specifically, we propose the Content Strong Consistency Decision Transformer (CSC-DT) algorithm to maintain strong consistency. The cache replacement problem is modeled as a Markov Decision Process (MDP), with decisions constructed as trajectories. These trajectories are processed using the Transformer architecture to capture the long-term dependencies between the temporal dynamics of content requests and updates, optimizing cache replacement decisions. Validated with railway engineering data, which undergo frequent updates during the design phase, our method effectively maintains strong content consistency and reduces content transmission costs.

Recommender systems are widely used to help users discover content of interest. However, due to their reliance on observational user–item interaction data, they often suffer from data bias. Such biases primarily stem from non-random exposure and users’ self-selection behavior, which distort the data distribution and lead to suboptimal performance of recommendation models. Existing debiasing methods, especially those based on loss reweighting strategies, have shown promising empirical results but still lack solid theoretical guarantees. In particular, they struggle to handle the complex, diverse, and often unidentifiable types of bias encountered in real-world scenarios. In this article, we revisit the problem of unbiased recommendation from the perspective of data bias and propose a unified debiasing framework that mitigates the effect of bias by aligning the distribution of training data with that of unbiased data collected under randomized exposure. We provide a thorough analysis of the theoretical limitations of existing reweighting methods, and we further propose a principled method, mEta Representation Alignment (ERA) , aiming to alleviate the inconsistency between user and item features under different distributions. Extensive experiments on real-world and semi-synthetic datasets demonstrate the effectiveness of ERA.

The rapid expansion and intelligent development of railway infrastructure are driving significant growth in railway engineering data. For the dispersed users across railway networks’ complex topology, traditional centralized storage systems are insufficient for their low-latency, cost-efficient data retrieval. Existing edge caching solutions based on multi-agent reinforcement learning fail to address the asymmetric relationships among railway nodes, such as data centers, stations, and sections, etc. Besides, the complexity of computing Nash equilibrium points also gets higher as the number of agents (edge caching servers) increases. This study introduces a Hierarchical Game model-based MADRL-driven Collaborative Edge Caching method(HG-MCEC) tailored for railway engineering data. By considering the distribution characteristics and caching strategy games among railway nodes, a hierarchical game model for collaborative edge caching is constructed. This model treats the railway edge caching as a multi-agent system, in which each railway node server is regarded as an agent. HG-MCEC utilizes deep learning to mitigate computational complexity and recognize agents’ asymmetry. Upper-level agents adjust cache replacement strategies according to environmental changes and decisionmaking experience. Lower-level agents, under the guidance of upper-level decisions, optimize collaborative caching strategies toward achieving hierarchical game equilibrium. Using a highspeed railway building information modeling data for validation, the method significantly outperforms existing approaches by enhancing content hit rates and reducing latency at edge caching servers while decreasing system content transmission costs.

Data sparsity poses a significant challenge for recommendation systems, prompting the research of Cross-Domain Recommendation ( CDR ). CDR aims to leverage more user-item interaction information from source domains to improve the recommendation performance in the target domain. However, a major challenge in CDR is the identification of transferable features. Traditional CDR methods struggle to distinguish between the various features of users, including domain-invariant features that are effective for feature transfer and domain-specific features that are detrimental to cross-domain information transfer. In this article, we aim to disentangle domain-invariant features and domain-specific features and effectively utilize these different features. This enables effective domain-to-domain information transfer by only transferring domain-invariant features while still considering the role of domain-specific features within their respective domains. Based on the superiority of graph structural feature learning and disentangled represent learning, we propose \(\mathbf{DMGCDR}\) —a model that learns D isentangled user feature representations and constructs a M ulti- G raph network for bidirectional knowledge transfer of shared features for CDR . Specifically, we designed two regularization terms to disentangle domain-invariant features and domain-specific features. Subsequently, we established a multi-graph convolutional network to enhance domain-specific features within single-domain graphs and transfer domain-invariant features across cross-domain graphs. Our approach also includes designing feature constraints to enhance the combination of features derived from different graphs and to uncover potential correlations among them. Extensive experiments on real-world datasets have demonstrated that our model significantly outperforms state-of-the-art CDR approaches.

In recommender systems, learning high-quality user and item representations is crucial for predicting user preferences. However, there are various confounding factors in observational data, resulting in data bias, which hinders the learning of user and item representations. Recent work proposed to use uniform data to alleviate bias problem. However, these methods fail to learn pure representations for unbiased prediction, which are not affected by confounding factors. This paper introduces a novel disentangled framework, named CDLRec, for learning unbiased representations, leveraging uniform data as supervisory signal for disentangling. Furthermore, to address the scarcity problem of uniform data, the contrastive learning is utilized to implement disentanglement by providing augmented samples. Specifically, two contrastive strategies are designed based on different sampling ways for positives and negatives. Extensive experiments are conducted over two real-world datasets and the results demonstrate the superior performance of our proposed method.

The deployment of Deep Neural Networks (DNNs) requires significant computational and storage resources, which is challenging for resource-constrained end devices. To this end, collaborative deep inference is proposed, in which the DNN is divided into two parts and executed on the end device and cloud respectively. The selection of DNN partition point is the key challenge to realize end-cloud collaborative deep inference, especially in mobile environments with unstable networks. In this paper, we propose a Real-time Adaptive Partition (RAP) framework, in which a fast split point decision algorithm is proposed to realize real-time adaptive DNN model partition in the mobile network. A weighted joint optimization of DNN quantization loss, inference and transmission latency is performed. We further propose a Joint Multi-user Model Partition and Resource Allocation (JM-MPRA) algorithm under RAP framework. JM-MPRA aims to guarantee the optimized latency, accuracy and resource utilization in the multi-user scene. Experimental evaluations have demonstrated the effectiveness of RAP with JM-MPRA in improving the performance of real-time end-cloud collaborative inference in both stable and unstable mobile networks. Compared with the state-of-the-art methods, the proposed approaches can achieve up to 5.06x decrease in inference latency and bring performance improvement of 1.52% in inference accuracy.

Most existing pre-training strategies usually choose the popular Graph Neural Networks (GNNs), which can be seen as a special form of low-pass filter, but fail to effectively capture heterophily. In this paper, we first present an experimental investigation exploring the performance of low-pass and high-pass filters in heterophily graph classification, where the results clearly show that high-frequency signal is important for learning heterophily graph representation. In addition, it is still unclear how to effectively capture the structural pattern of graphs and how to measure the capability of the self-supervised pre-training strategy in capturing graph structure. To address the problem, we first design a quantitative Metric for Graph Structure (MGS), which analyzes the correlation between structural similarity and embedding similarity of graph pairs. Then, to enhance the graph structural information captured by self-supervised learning, we propose a novel self-supervised strategy for Pre-training GNNs based on the Metric (PGM). Extensive experiments validate our pre-training strategy achieves state-of-the-art performance for molecular property prediction and protein function prediction. In addition, we find choosing a suitable filter sometimes may be better than designing good pre-training strategies for heterophily graph classification.

In rail trains, the prognostics and health management system will make use of data from sensors deployed in key components. These data are typically non-stationary multi-variable time series with abrupt variations. The challenges of multivariate time series forecasting include how to identify the interactions between various variables, and how to assess the influence of historical data on present data. This paper presents the nature of the air brake system and its physical models. By analyzing the observed data obtained from the freight train air brake system, a Memory network-based Time series Multi-step fault Forecasting model (MTMF) is proposed. MTMF consists of a dual-encoder and a memory network module for feature extraction. The two encoders are used to extract features of short-term and long-term historical data respectively. The memory network is used to further mine the different weights of the long-term historical data. MTMF also designs a joint multi-step forecasting loss function, which is composed of shape, time and mean square error losses between the forecasting series and the real series. The improved loss function is no longer restricted to the differences in evaluation points. MTMF is evaluated on the real freight train braking system time series dataset. The results show that MTMF outperforms other forecasting methods on multivariate time series. The forecasting accuracy is increased by 5%-45%, demonstrating that the model is effective for non-stationary series and can better mine the dependence patterns for the train braking system.

Electronically controlled pneumatic (ECP) brake is widely used in heavy-haul train. Although the latest data-driven fault diagnosis can exploit the collection data from the braking system, it still has challenges for effective fault diagnosis model because of industrial data noise and insufficient fault samples. This article proposes a fault diagnosis model based on multidimensional feature fusion and ensemble learning for braking system of heavy-haul train (MFF-GBFD). First, the multidimensional features are extracted. By principal component analysis and feature fusion, the redundant features are eliminated. Then, the model is trained under ensemble learning framework with boosting strategy. Experiments are carried out on the data from the ECP braking system of DK-2 locomotive. The efforts show that the proposed MFF-GBFD model presents better performances as a result from the early-stage feature extraction, feature selection, and feature fusion. It also has higher accuracy and $F_1$ values compared with the traditional classification algorithms.