The integration of reconfigurable intelligent surfaces (RIS) with relays can enhance the quality and coverage of wireless communication. However, relays introduce extra energy consumption, and the transmission quality of RIS-aided relay systems can be further improved. Polarization-adjusted convolutional (PAC) codes exhibit superior error-correction performance at short code lengths, demonstrating potential to enhance the performance of RIS-aided relay systems. In this paper, we consider an energy-efficient RIS-aided coded cooperation (RIS-CC) system based on PAC codes to achieve wide coverage transmission with high reliability and low latency. First, a parity-check (PC) aided PAC-CC (PC-PAC-CC) scheme is designed to build the considered RIS-CC system for efficient relay forwarding. Using PC, the proposed PC-PAC-CC scheme avoids the unnecessary energy consumption on relays at high signal-to-noise ratio (SNR). Second, we propose a PC-aided low-complexity list (PC-LCL) decoding algorithm to reduce decoding complexity at receivers, thus decreasing transmission latency. Moreover, the proposed PC-LCL decoding algorithm is implemented by software to demonstrate its practicability. Third, we derive closed-form expressions for the tight upper bound of ergodic channel capacity (ECC) in the considered RIS-CC system under Nakagami-mfading, and conduct its asymptotic analysis at high SNR. Test results show that 1) the proposed PC-PAC-CC scheme has better error-correction performance than state-of-the-art (SOTA) work, and reduces energy consumption on relays by up to 81.02%; 2) the proposed PC-LCL software decoder has a 35.29% reduction on latency compared with the SOTA PAC software decoder. Besides, the derived closed-form expressions are verified by simulations.

Hypergraph Pattern Mining (HPM) aims to identify all the instances of user-interested subhypergraphs (patterns) in hypergraphs, which has been widely used in various applications. However, existing solutions either need significant enumeration overhead because they extend subhypergraphs at the granularity of vertices, or suffer from massive redundant computations because they often need to repeatedly fetch and process the same incident hyperedges for different vertices. This paper presents an overlap-centric system named OHMiner to efficiently support HPM. OHMiner proposes an overlap-centric execution model to determine the subhypergraphs isomorphism through computing and comparing overlaps among hyperedges using set operations. This model aims to efficiently handle the vertices that collectively share the same incident hyperedges. To automatically and precisely retrieve an arbitrary pattern's overlapping semantics without performing redundant set computations, OHMiner further proposes a redundancy-free compiler, which constructs an Overlap Intersection Graph (OIG) for the pattern, optimizes the OIG, and generates an overlap-centric execution plan to guide the procedure of HPM. Moreover, OHMiner designs an overlap-centric parallel execution engine, which adopts an incremental overlap-pruned approach to fast validate candidates for HPM. Additionally, it proposes a degree-aware data store to support efficient generation of candidates. Through evaluating OHMiner on a broad range of real-world hypergraphs with various patterns, our experimental results show that OHMiner outperforms the state-of-the-art HPM system by 5.4×-22.2×.