Accurate channel prediction is crucial for high-speed railway communications, especially in the 5G/6G era. Existing single-modality methods struggle to capture the intricate temporal and environmental dynamics, leading to suboptimal performance. To address this, we propose ChannelMTS, a novel multi-modal time-series framework that integrates both channel and environmental information to enhance prediction accuracy. First, ChannelMTS represents environmental conditions as snapshots, which are subsequently enhanced by a retrieval-augmented statistical channel module and embedded into an environmental time-series space using a transformer. Then, it aligns the channel and environmental time-series distributions to reduce the modality disparity. Finally, it adaptively fuses both modalities to achieve accurate channel prediction. This design can effectively leverage the complementary strengths of both modalities to enrich single-modality channel time series. Extensive experiments on real-world channel datasets show that ChannelMTS consistently outperforms state-of-the-art baselines. Moreover, online A/B testing reveals a significant 70%-90% performance improvement, and real-world deployment confirms its practical value.

Large Language Models (LLMs) have revolutionized the fields of computer vision (CV) and natural language processing (NLP). One of the most notable advancements of LLMs is that a single model is trained on vast and diverse datasets spanning multiple domains -- a paradigm we term 'All in One'. This methodology empowers LLMs with super generalization capabilities, facilitating an encompassing comprehension of varied data distributions. Leveraging these capabilities, a single LLM demonstrates remarkable versatility across a variety of domains -- a paradigm we term 'One for All'. However, applying this idea to the graph field remains a formidable challenge, with cross-domain pretraining often resulting in negative transfer. This issue is particularly important in few-shot learning scenarios, where the paucity of training data necessitates the incorporation of external knowledge sources. In response to this challenge, we propose a novel approach called Graph COordinators for PrEtraining (GCOPE), that harnesses the underlying commonalities across diverse graph datasets to enhance few-shot learning. Our novel methodology involves a unification framework that amalgamates disparate graph datasets during the pretraining phase to distill and transfer meaningful knowledge to target tasks. Extensive experiments across multiple graph datasets demonstrate the superior efficacy of our approach. By successfully leveraging the synergistic potential of multiple graph datasets for pretraining, our work stands as a pioneering contribution to the realm of graph foundational model. Code available at https://github.com/cshhzhao/GCOPE.

Anomaly detection (AD) plays a pivotal role in numerous web-based applications, including malware detection, anti-money laundering, device failure detection, and network fault analysis. Most methods, which rely on unsupervised learning, are hard to reach satisfactory detection accuracy due to the lack of labels. Weakly Supervised Anomaly Detection (WSAD) has been introduced with a limited number of labeled anomaly samples to enhance model performance. Nevertheless, it is still challenging for models, trained on an inadequate amount of labeled data, to generalize to unseen anomalies. In this paper, we introduce a novel framework, Knowledge-Data Alignment (KDAlign), to integrate rule knowledge, typically summarized by human experts, to supplement the limited labeled data. Specifically, we transpose these rules into the knowledge space and subsequently recast the incorporation of knowledge as the alignment of knowledge and data. To facilitate this alignment, we employ the Optimal Transport (OT) technique. We then incorporate the OT distance as an additional loss term to the original objective function of WSAD methodologies. Comprehensive experimental results on five real-world datasets demonstrate that our proposed KDAlign framework markedly surpasses its state-of-the-art counterparts, achieving superior performance across various anomaly types. Our codes are released at https://github.com/cshhzhao/KDAlign.