Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence

Knowledge graphs (KGs), which could provide essential relational information between entities, have been widely utilized in various knowledge-driven applications. Since the overall human knowledge is innumerable that still grows explosively and changes frequently, knowledge construction and update inevitably involve automatic mechanisms with less human supervision, which usually bring in plenty of noises and conflicts to KGs. However, most conventional knowledge representation learning methods assume that all triple facts in existing KGs share the same significance without any noises. To address this problem, we propose a novel confidence-aware knowledge representation learning framework (CKRL), which detects possible noises in KGs while learning knowledge representations with confidence simultaneously. Specifically, we introduce the triple confidence to conventional translation-based methods for knowledge representation learning. To make triple confidence more flexible and universal, we only utilize the internal structural information in KGs, and propose three kinds of triple confidences considering both local and global structural information. In experiments, We evaluate our models on knowledge graph noise detection, knowledge graph completion and triple classification. Experimental results demonstrate that our confidence-aware models achieve significant and consistent improvements on all tasks, which confirms the capability of CKRL modeling confidence with structural information in both KG noise detection and knowledge representation learning.Comment: 8 page

Symbol-Based Successive Cancellation List Decoder for Polar Codes

Polar codes is promising because they can provably achieve the channel capacity while having an explicit construction method. Lots of work have been done for the bit-based decoding algorithm for polar codes. In this paper, generalized symbol-based successive cancellation (SC) and SC list decoding algorithms are discussed. A symbol-based recursive channel combination relationship is proposed to calculate the symbol-based channel transition probability. This proposed method needs less additions than the maximum-likelihood decoder used by the existing symbol-based polar decoding algorithm. In addition, a two-stage list pruning network is proposed to simplify the list pruning network for the symbol-based SC list decoding algorithm.Comment: Accepted by 2014 IEEE Workshop on Signal Processing Systems (SiPS

A Reduced Latency List Decoding Algorithm for Polar Codes

Long polar codes can achieve the capacity of arbitrary binary-input discrete memoryless channels under a low complexity successive cancelation (SC) decoding algorithm. But for polar codes with short and moderate code length, the decoding performance of the SC decoding algorithm is inferior. The cyclic redundancy check (CRC) aided successive cancelation list (SCL) decoding algorithm has better error performance than the SC decoding algorithm for short or moderate polar codes. However, the CRC aided SCL (CA-SCL) decoding algorithm still suffer from long decoding latency. In this paper, a reduced latency list decoding (RLLD) algorithm for polar codes is proposed. For the proposed RLLD algorithm, all rate-0 nodes and part of rate-1 nodes are decoded instantly without traversing the corresponding subtree. A list maximum-likelihood decoding (LMLD) algorithm is proposed to decode the maximum likelihood (ML) nodes and the remaining rate-1 nodes. Moreover, a simplified LMLD (SLMLD) algorithm is also proposed to reduce the computational complexity of the LMLD algorithm. Suppose a partial parallel list decoder architecture with list size $L=4$ is used, for an (8192, 4096) polar code, the proposed RLLD algorithm can reduce the number of decoding clock cycles and decoding latency by 6.97 and 6.77 times, respectively.Comment: 7 pages, accepted by 2014 IEEE International Workshop on Signal Processing Systems (SiPS

International Communication of Tai Chi Culture: Challenges and Opportunities

As China’s economic strength and political influence have been greatly increased during the past decades, Tai Chi culture, which has been long viewed as one of the very essential parts of traditional Chinese culture, is being given a golden opportunity to step into the world. Tai Chi contains a variety of values that are quite beneficial for people all over the world to live a peaceful and healthy life. Actually it represents a life attitude that stresses the harmony between man and nature, which is obviously significant in the contemporary society. Today with the intensification of globalization, Tai Chi culture cannot and also should not be restricted only to Chinese people. Instead, it should belong to the whole world. However, the international communication of Tai Chi culture meets both challenges and opportunities. Some myriad stereotypes, misperceptions, and distortions about China may hinder the cultural spreading. The conflict and competition between Tai Chi and other cultures are another problem. In addition, lack of theoretical study of international communication systems should be taken into serious consideration. On the other hand, the culture communication is faced with a favorable internal and external political and economic environment. Besides, practical demands in strained life of contemporary society also call for the wide spreading of Tai Chi culture. In short, joint efforts should be made to overcome the challenges and make full use of the opportunities so as to promote the worldwide spreading of Tai Chi culture