Sequential recommendation aims to model users' evolving preferences based on their historical interactions. Recent advances leverage Transformer-based architectures to capture global dependencies, but existing methods often suffer from high computational overhead, primarily due to discontinuous memory access in temporal encoding and dense attention over long sequences. To address these limitations, we propose FuXi-γ, a novel sequential recommendation framework that improves both effectiveness and efficiency through principled architectural design. FuXi-γ adopts a decoder-only Transformer structure and introduces two key innovations: (1) An exponential-power temporal encoder that encodes relative temporal intervals using a tunable exponential decay function inspired by the Ebbinghaus forgetting curve. This encoder enables flexible modeling of both short-term and long-term preferences while maintaining high efficiency through continuous memory access and pure matrix operations. (2) A diagonal-sparse positional mechanism that prunes low-contribution attention blocks using a diagonal-sliding strategy guided by the persymmetry of Toeplitz matrix. Extensive experiments on four real-world datasets demonstrate that FuXi-γ achieves state-of-the-art performance in recommendation quality, while accelerating training by up to 4.74× and inference by up to 6.18×, making it a practical and scalable solution for long-sequence recommendation. Code: https://github.com/Yeedzhi/FuXi-gamma.

State access is a critical part of smart contract execution which seriously affects the efficiency of smart contract execution in the mainstream Ethereum blockchain. To reduce state access latency, existing studies typically require manual source code modifications, which in practice may deliver limited performance gains and shift the burden to developers. In this paper, we propose CROSC to reduce state access latency and improve smart contract execution efficiency by a compilation-runtime joint optimization approach. CROSC consists of three key parts: 1) a runtime memory management mechanism named Fast State Memory (FastSM) to fully utilize the working memory and provide the context for the contract compiler; 2) a State Variable Address Relocation (SVAR) strategy to minimize costly persistent storage operations by precisely redirecting state variable access targets during compilation; 3) a one-shot unpacking design that eliminates frequent decoding overhead for low-bitwidth state variables. Preliminary experimental results highlight that, compared with the baseline compilation and runtime system of Ethereum, CROSC can achieve 2.5× and 7.5× speedups for single state load and store operations, respectively. CROSC reduces state access latency by up to 81.3%, and overall contract execution latency by 32.9% on average across 14 typical types of smart contracts. Extended evaluations on ERC20 and ERC721 token standard contracts show that CROSC delivers significant benefits in critical areas while remaining unobtrusive for less intensive state operations.

Previous studies have shown that opportunistic routing (OR) can significantly improve the performance of wireless multi-hop networks, such as wireless adhoc networks and wireless sensor networks. However, most existing opportunistic routing schemes only consider node location as the priority metric in relay selection, where packets may accumulate in relays' packet buffer, especially in data intensive applications. This can increase end-to-end packet delay and inevitably introduce negative effects on network throughput. In this paper, Buffer-Aware Opportunistic Routing scheme (BAOR) is proposed. The proposed scheme combines the location and buffer length of relay candidates to prioritize the relay selection, so as to prevent the situation where a larger number of packets queue in the buffer of relays on the shortest path. Network simulation results demonstrate that the proposed BAOR outperforms traditional OR schemes in terms of both network throughput and end-to-end packet latency.