Chinese spelling correction (CSC) models detect and correct a text typo based on the misspelled character and its context. Recently, Bert-based models have dominated the research of Chinese spelling correction. However, these methods only focus on the semantic information of the text during the pretraining stage, neglecting the learning of correcting spelling errors. Moreover, when multiple incorrect characters are in the text, the context introduces noisy information, making it difficult for the model to accurately detect the positions of the incorrect characters, leading to false corrections. To address these limitations, we apply the multimodal pre-trained language model ChineseBert to the task of spelling correction. We propose a self-distillation learning-based pretraining strategy, where a confusion set is used to construct text containing erroneous characters, allowing the model to jointly learns how to understand language and correct spelling errors. Additionally, we introduce a single-channel masking mechanism to mitigate the noise caused by the incorrect characters. This mechanism masks the semantic encoding channel while preserving the phonetic and glyph encoding channels, reducing the noise introduced by incorrect characters during the prediction process. Finally, experiments are conducted on widely used benchmarks. Our model achieves superior performance against state-of-the-art methods by a remarkable gain.

The important issue of an adaptive scheduling scheme is to maximize throughput while providing fair services to all users, especially under strict quality of service requirements. To achieve this goal, we consider the problem of multiuser scheduling under a given fairness constraint. A novel Adaptive Fairness and Throughput Control (AFTC) approach is proposed to maximize the network throughput while attaining a given min-max fairness index. Simulation results reveal that comparing to straightforward methods, the proposed AFTC approach can achieve the desired fairness while maximizing the throughput with short convergence time, and is stable in dynamic scenarios. The trade-off between fairness and throughput can be accurately controlled by adjusting the scheduler's parameters.

The timing synchronization scheme for massive MIMO systems is proposed with CCK (Complementary Code Keying) codes in this letter. Our proposed scheme is specifically designed to generate synchronization sequence with large family size. The optimal sequences can be constructed with ZCZ or LCZ based on the number of antennas. Furthermore Monte Carlo simulation results confirmed the expected detection probability for massive MIMO systems.

We consider to design approximation algorithms for the survivable network design problem in hypergraphs (SNDPHG) based on algorithms developed for the survivable network design problem in graphs (SNDP) or the element connectivity problem in graphs (ECP). Given an instance of the SNDPHG, by replacing each hyperedge e={v1,,vk} with a new vertex we and k edges {we, v1},, {we, vk}, we define an SNDP or ECP in the resulting graph. We show that by approximately solving the SNDP or ECP defined in this way, several approximation algorithms for the SNDPHG can be obtained. One of our results is a dmax+-approximation algorithm for the SNDPHG with dmax 3, where dmax (resp. dmax+) is the maximum degree of hyperedges (resp. hyperedges with positive cost). Another is a dmax+(rmax)-approximation algorithm for the SNDPHG, where (i)=j=1i(1/j) is the harmonic function and rmax is the maximum connectivity requirement.