Conversion objectives in large-scale recommender systems are sparse, making them difficult to optimize. Generative recommendation (GR) partially alleviates data sparsity by organizing multi-type behaviors into a unified token sequence with shared representations, but conversion signals remain insufficiently modeled. While recent behavior-aware GR models encode behavior types and employ behavior-aware attention to highlight decision-related intermediate behaviors, they still rely on standard attention over the full history and provide no additional supervision for conversions, leaving conversion sparsity largely unresolved. To address these challenges, we propose RCLRec, a reverse curriculum learning–based GR framework for sparse conversion supervision. For each conversion target, RCLRec constructs a short curriculum by selecting a subsequence of conversion-related items from the history in reverse. Their semantic tokens are fed to the decoder as a prefix, together with the target conversion tokens, under a joint generation objective. This design provides additional instance-specific intermediate supervision, alleviating conversion sparsity and focusing the model on the user's critical decision process. We further introduce a curriculum quality-aware loss to ensure that the selected curricula are informative for conversion prediction. Experiments on offline datasets and an online A/B test show that RCLRec achieves superior performance, with +2.09% advertising revenue and +1.86% orders in online deployment.

The modern smart grid, a mission-critical Cyber-Physical Power System (CPPS), relies on a vast Internet of Things (IoT) comprised of interconnected sensor terminals, such as smart meters and Phasor Measurement Units (PMUs). Ensuring the resilience of this infrastructure by identifying its most critical nodes is of utmost importance to prevent cascading failures. To address this challenge, this paper proposes the Graph Convolutional Network with Integrated Deformable Depthwise Attention (GCN-IDDA), a novel framework based on graph convolutional networks. The model is specifically designed to fuse heterogeneous, multimodal data streams that span physical-layer readings collected from these sensor terminals, network-layer communication metrics, and the overall IoT network topology. To strictly evaluate performance in realistic scenarios, we employ the SimBench dataset to construct grid topologies and the Susceptible-Infected-Recovered (SIR) model to simulate dynamic threat propagation. Extensive experiments on four distinct smart grid IoT network testbeds show that GCN-IDDA significantly outperforms traditional centrality measures and baseline machine learning methods in identification accuracy. This research not only delivers an effective tool for smart grid vulnerability analysis but also provides a scalable, data-driven paradigm for enhancing the security of other large-scale, mission-critical sensor networks.

Transformer-based sequential recommendation (TSR) models have shown superior performance in recommendation systems, where the quality of item representations plays a crucial role. Classical representation methods integrate item features using concatenation or neural networks to generate homogeneous representation sequences. While straightforward, these methods overlook the heterogeneity of item features, limiting the transformer's ability to capture fine-grained patterns and restricting scalability. Recent studies have attempted to integrate user-side heterogeneous features into item representation sequences, but item-side heterogeneous features, which are vital for performance, remain excluded. To address these challenges, we propose a Heterogeneous Information Transformer model for Sequential Recommendation (HeterRec), which incorporates Heterogeneous Token Flatten Layer (HTFL) and Hierarchical Causal Transformer Layer (HCT). Our HTFL is a novel item tokenization method that converts items into a heterogeneous token set and organizes these tokens into heterogeneous sequences, effectively enhancing performance gains when scaling up the model. Moreover, HCT introduces token-level and item-level causal transformers to extract fine-grained patterns from the heterogeneous sequences. Experiments on offline and online datasets show that the HeterRec model achieves superior performance.

Protein-protein interactions (PPIs) play an indispensable role in understanding disease-causing mechanisms, and the basic laws of food and drugs on life. Contemporary research on this issue, however, is incapable of guaranteeing structure consistency between extracted features and raw data, and fails to fully investigate the interconnection information of features. Thus, this paper proposes a subspace structure consistency-based method for protein-protein interactions prediction. SSC-PPI is not only capable of investigating the coherent relations between the encoded features generated from amino acid composition and conjoint triad numeric composition of F-vector, composition and transition descriptors, but also fully maintains the latent geometrical structure consistency between feature subspace and data space. Numerous comparative experiments demonstrate its excellent predictable performance with significant accuracies of 100$\%$, 99.95$\%$, 99.98$\%$, 100$\%$ and 100$\%$ respectively on Helicobacter pylori, Human, Saccharomyces cerevisiae (core subset), Human-Bacillus Anthracis and Human-Yersinia pestis datasets, significantly outperforming the comparative models by average increases of 14.39$\%$, 5.45$\%$, 8.10$\%$, 6.05$\%$ and 8.79$\%$ respectively. Additionally, SSC-PPI offers an efficient and reliable framework for large-scale prediction tasks such as drug-drug and drug-food interactions.