다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Social Network Analysis

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances In this article, we review recent developments in recommender systems and discuss the major challenges We compare and evaluate available algorithms and examine their roles in the future developments In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems Potential impacts and future directions are discussed We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers

Recommender Systems

Dynamics of information diffusion and its applications on complex networks

Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this article, we propose a recommendation algorithm based on an integrated diffusion on user–item–tag tripartite graphs. We use three benchmark data sets, Del.icio.us , MovieLens and BibSonomy , to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.

/pdf/personalized-recommendation-via-integrated-diffusion-on-user-2so6m5n40k.pdf

Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs

Based on the user-tag-object tripartite graphs, we propose a recommendation algorithm that makes use of social tags. Besides its low cost of computational time, the experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can enhance the algorithmic accuracy and diversity. Especially, it provides more personalized recommendation when the assigned tags belong to more diverse topics. The proposed algorithm is particularly effective for small-degree objects, which reminds us of the well-known cold-start problem in recommender systems. Further empirical study shows that the proposed algorithm can significantly solve this problem in social tagging systems with heterogeneous object degree.

/pdf/solving-the-cold-start-problem-in-recommender-systems-with-40tdryh5d9.pdf

Solving the cold-start problem in recommender systems with social tags

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.

/pdf/tag-aware-recommender-systems-a-state-of-the-art-survey-3nbued852d.pdf

Zi-Ke Zhang

Papers

Recommender Systems

Dynamics of information diffusion and its applications on complex networks

Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs

Solving the cold-start problem in recommender systems with social tags

Tag-aware recommender systems: a state-of-the-art survey