ID 原文 译文
45626 社区结构的发现已成为其他社交网络分析任务的基础和关键。 The discovery of community structure has be-come the basis and key point of other social network analysis tasks.
45627 随着在线社交网络用户数量的急剧增长，传统的社区发现手段已经难以适应，从而催生了并行社区发现技术的发展。 With the rapid growth of the number of online social network users, the traditional community detection methods have been difficult to be used, which contributes to the development of parallel community detection technology.
45628 对当前主流并行社区发现方法 Louvain 算法和标签传播算法在超大规模数据集上的可扩展性进行了研究，指出了各自的优缺点，为后续应用提供参考。 The current mainstream parallel community detection methods,including Louvain algorithm and label propagation algorithm, were tested in the large-scale data sets, and corresponding advantages and disadvantages were pointed out so as to provide useful information for later applications.
45629 针对 OFDM 系统中的脉冲噪声问题，提出一种基于压缩感知技术的脉冲噪声抑制方法。 Aiming at the impulsive noise occurring in OFDM systems, an impulsive noise mitigation algorithm based on compressed sensing theory was proposed.
45630 该方法将信道脉冲响应和脉冲噪声联合视作一个稀疏向量， The proposed algorithm firstly treated the channel impulse response and the impulsive noise as a joint sparse vector by exploiting the sparsity of both them.
45631 将发射数据符号视作未知参数，利用稀疏贝叶斯学习理论联合估计信道、脉冲噪声和数据符号。 Then the sparse Bayesian learning framework was adopted to jointly estimate the channel impulse response, the impulsive noise and the data symbols, in which the data symbols were regarded as unknown parameters.
45632 与现有脉冲噪声抑制方法相比，该方法不仅能够充分利用全部子载波信息，而且不需要信道和脉冲噪声的先验统计信息。 Compared with the existing impulsive noise mitigation methods, the proposed algorithm not only utilized all sub-carriers but also did not use any a priori information of the channel and impulsive noise.
45633 仿真结果表明，所提方法在信道估计及误比特率性能上有明显改善。 The simulation results show that the proposed algorithm achieves significant improvement on the channel estimation and bit error rate performance.
45634 基于多种模型检测方法组合的复合检测方式是当前软件模型检测领域开展研究的热点之一。 For software model checking, approaches that combine with different kind of verification methods are now under research.
45635 在当前的研究中，提高检测的规模和检测的对象复杂程度的关键在于如何有效处理抽象的扩张和收缩。 The key to improve scale and complexity of verifiable software is handling the method for abstraction widening and strengthening wisely and precisely.