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Languages using Chinese characters are mostly processed at word level. Inspired by recent success of deep learning, we delve deeper to character and radical levels for Chinese language processing. We propose a new deep learning technique, called "radical embedding", with justifications based on Chinese linguistics, and validate its feasibility and utility through a set of three experiments: two in-house standard experiments on short-text categorization (STC) and Chinese word segmentation (CWS), and one in-field experiment on search ranking. We show that radical embedding achieves comparable, and sometimes even better, results than competing methods.

-Based on an effective clustering algorithm -Affinity Propagation (AP) -we present in this paper a novel semi-supervised text-clustering algorithm, called Seeds Affinity Propagation (SAP). There are two main contributions in our approach: (1) a new similarity metric that captures the structural information of texts; (2) a novel seed construction method to improve the semi-supervised clustering process. To study the performance of the new algorithm, we applied it to the benchmark data set Reuters-21578, and compared it to two state-of-the-art clustering algorithms, namely k-means algorithm and the original AP algorithm. Furthermore, we have analyzed the individual impact of the two proposed contributions. Results show that the proposed similarity metric is more effective in text clustering (F-measures ca. 21% higher than in the AP algorithm) and that the proposed semi-supervised strategy achieves both better clustering results and faster convergence (using only 76% iterations of the original AP). The complete SAP algorithm obtains higher F-measure (ca. 40% improvement over k-means and AP) and lower entropy (ca. 28 % decrease over k-means and AP), improves significantly clustering execution time (twenty time faster) in respect than k-means, and provides enhanced robustness compared with all other methods.

In this article, we study the combination of nonorthogonal multiple access (NOMA) and full-duplex operation as a promising solution to improve the capacity of next-generation wireless systems. We study the application of full-duplex NOMA transmission in wireless cellular, relay and cognitive radio networks, and demonstrate achievable performance gains. It is shown that the effects of self-interference and inter-user interference due to full-duplex operation can be effectively mitigated by optimizing/enhancing the beamforming, power control, and link scheduling techniques. We also discuss research challenges and future directions so that full-duplex NOMA can be made practical in the near future.

Supply networks (SN) must maintain operations and connectedness under disruptions to remain competitive; this is referred to as SN resilience. Building a resilient SN is an underlying challenge in supply chain management. In this paper, SN resilience is examined from the complex network topology perspective to understand how supply chain managers construct resilient networks. The proposed growth model considers enterprises leaving the network, which previous studies have ignored. Considering the heterogeneous roles of enterprises, new metrics based on a new proposed sub-network concept are presented to evaluate resilience. Using a computer simulation, the resilience of the SN generated by the model proposed in this paper is compared with that of other models, and the results indicate that (i) the proposed model can be tuned to generate a desired resilient network; (ii) the proposed metrics capture the resilience requirements of the SN very well; (iii) the more uniform the distribution of the enterprises, the more resilient the corresponding SN; and (iv) the higher the values of α and β, the lower the SN resilience, and β affects the resilience more than α does.