A graph associated with a linear code, which originates from a δ-decodable code pair for the two-user binary adder channel, is investigated based on the structure of the linear code. Subgraphs of the graph that are induced by cosets of the linear code are introduced. It is found that these are vertextransitive and are disconnected for uniquely decodable (1-decodable) code pair. Moreover, a class of graphs associated with linear codes is proved to consist of clique components and their independence numbers are successfully formulated. Applications to channel coding for the two-user binary adder channel are also discussed.

In the context of Web 2.0, the interaction between users and resources is more and more frequent in the process of resource sharing and consumption. However, the current research on resource pricing mainly focuses on the attributes of the resource itself, and does not weigh the interests of the resource sharing participants. In order to deal with these problems, the pricing mechanism of resource-user interaction evaluation based on multi-agent game theory is established in this paper. Moreover, the user similarity, the evaluation bias based on link analysis and punishment of academic group cheating are also included in the model. Based on the data of 181 scholars and 509 articles from the Wanfang database, this paper conducts 5483 pricing experiments for 13 months, and the results show that this model is more effective than other pricing models - the pricing accuracy of resource resources is 94.2%, and the accuracy of user value evaluation is 96.4%. Besides, this model can intuitively show the relationship within users and within resources. The case study also exhibits that the user's knowledge level is not positively correlated with his or her authority. Discovering and punishing academic group cheating is conducive to objectively evaluating researchers and resources. The pricing mechanism of scientific and technological resources and the users proposed in this paper is the premise of fair trade of scientific and technological resources.

It is known that the uniquely decodable code pairs (C1, C2) for the two-user binary adder channel relates to the maximum independent set of a graph associated with a binary code. This paper formulates the independence number of a class of graphs associated with binary linear codes, and presents an algorithm of the maximum independent set for those graphs. Uniquely decodable code pairs (C1, C2)'s are produced, where C1 is a linear code and C2 is a maximum independent set of the graph associated with C1. For the given C1, the transmission rate of C2 is higher than that by Khachatrian, which is known as the best result as so far. This is not rather surprising because the code C2 is a maximum independent set in this paper but not be Khachatrian's.

A fully-integrated double-channel 5-Gb/s/ch 2:1 serializer array is designed and fabricated in a standard 0.18-$mu $m CMOS technology, which can be easily expanded to any even-number-channel array, e.g. 12 channels, by means of arrangement in a parallel manner. Besides two conventional half-rate 2:1 serializers, both phase-locked loop and delay-locked loop techniques are employed locally to deal with the involved clocking-related issues, which make the serializer array self-contained, compact and automatic. The system architecture, circuit and layout designs are discussed and analyzed in detail. The chip occupies a die area of 673,$mu $m$, imes ,$667,$mu $m with a core width of only 450,$mu $m. Measurement results show that it works properly without a need for additional clock channels, reference clocks, off-chip tuning, external components, and so on. From a single supply of 1.8,V, a power of 200,mW is consumed and a single-ended swing of above 300,mV for each channel is achieved.

Resource sharing is to ensure required resources available for their demanders. However, due to the lack of proper sharing model, the current sharing rate of the scientific and technological resources is low, impeding technological innovation and value chain development. Here we propose a novel method to share scientific and technological resources by storing resources as nodes and correlations as links to form a complex network. We present a few-shot relational learning model to solve the cold-start and long-tail problems that are induced by newly added resources. Experimentally, using NELL-One and Wiki-One datasets, our one-shot results outperform the baseline framework - metaR by 40.2% and 4.1% on MRR in Pre-Train setting. We also show two practical applications, a resource graph and a resource map, to demonstrate how the complex network helps resource sharing.

Ranking as an important task in information systems has many applications, such as document/webpage retrieval, collaborative filtering and advertising. The last decade has witnessed a growing interest in the study of learning to rank as a means to leverage training information in a system. In this paper, we propose a new learning to rank method, i.e. BLM-Rank, which uses a linear function to score samples and models the pairwise preference of samples relying on their scores under a Bayesian framework. A stochastic gradient approach is adopted to maximize the posterior probability in BLM-Rank. For industrial practice, we have also implemented the proposed algorithm on Graphic Processing Unit (GPU). Experimental results on LETOR have demonstrated that the proposed BLM-Rank method outperforms the state-of-the-art methods, including RankSVM-Struct, RankBoost, AdaRank-NDCG, AdaRank-MAP and ListNet. Moreover, the results have shown that the GPU implementation of the BLM-Rank method is ten-to-eleven times faster than its CPU counterpart in the training phase, and one-to-four times faster in the testing phase.