Hypergraph neural networks (HGNNs) effectively model complex high-order relationships in domains like protein interactions and social networks by connecting multiple vertices through hyperedges, enhancing modeling capabilities, and reducing information loss. Developing foundation models for hypergraphs is challenging due to their distinct data, which includes both vertex features and intricate structural information. We present Hyper-FM, a Hypergraph Foundation Model for multi-domain knowledge extraction, featuring Hierarchical High-Order Neighbor Guided Vertex Knowledge Embedding for vertex feature representation and Hierarchical Multi-Hypergraph Guided Structural Knowledge Extraction for structural information. Additionally, we curate 11 text-attributed hypergraph datasets to advance research between HGNNs and LLMs. Experiments on these datasets show that Hyper-FM outperforms baseline methods by approximately 13.4%, validating our approach. Furthermore, we propose the first scaling law for hypergraph foundation models, demonstrating that increasing domain diversity significantly enhances performance, unlike merely augmenting vertex and hyperedge counts. This underscores the critical role of domain diversity in scaling hypergraph models.

With the growing reliance on third-party intellectual property (3PIP) in integrated circuit design, the threat of functional failures induced by hardware Trojans embedded within these components has become increasingly critical. The high stealthiness and sophistication of hardware Trojans often render existing detection techniques insufficient for comprehensive coverage. As a result, runtime detection and recovery techniques have emerged and been proposed as a crucial last line of defense. However, these solutions typically introduce substantial hardware overhead, limiting their practicality in resource-constrained applications such as edge computing. To address this critical challenge, this work leverages approximate computing to develop low-cost runtime recovery strategies against hardware Trojans. Specifically, it begins by analyzing the challenges and potential opportunities introduced into existing security schemes when approximate computing is applied. Based on this analysis, targeted solutions and optimization techniques are proposed. These are then integrated into a unified framework that explores the trade-off between hardware resource usage and computational accuracy while maintaining circuit-level security. Experimental results across several commonly used applications demonstrate that the proposed framework can achieve over 20% of hardware resource savings with only a 5% reduction in accuracy. To the best of our knowledge, this work is the first to systematically integrate approximate computing with runtime hardware Trojan recovery, providing a new cost-effective direction for circuit-level security in resource-constrained systems.

Hypergraphs, with their ability to model complex, beyond pair-wise correlations, presents a significant advancement over traditional graphs for capturing intricate relational data across diverse domains. However, the integration of hypergraphs into self-supervised learning (SSL) frameworks has been hindered by the intricate nature of high-order structural variations. This paper introduces the Self-Supervised Hypergraph Training Framework via Structure-Aware Learning (SS-HT), designed to enhance the perception and measurement of these variations within hypergraphs. The SS-HT framework employs a "Masking and Re-Masking" strategy to bolster feature reconstruction in Hypergraph Neural Networks (HGNNs), addressing the limitations of traditional SSL methods. It also introduces a metric strategy for local high-order correlation changes, streamlining the computational efficiency of structural distance calculations. Extensive experiments on 11 datasets demonstrate SS-HT's superior performance over existing SSL methods for both low-order and high-order data. Notably, the framework significantly reduces data labeling dependency, achieving a 32% improvement over HGNN in the downstream task fine-tuning phase under the 1% labeled data setting in the Cora-CC dataset. Ablation studies further validate SS-HT's scalability and its capacity to augment the performance of various HGNN methods, underscoring its robustness and applicability in real-world scenarios.