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| 1 | Towards Fast and Efficient Algorithm for Learning Bayesian Network显示文摘Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph(DAG) is the foundation for dependency analysis algorithms for this problem. Considering the unreliability of high order condition independence(CI) tests, and to improve the efficiency of a dependency analysis algorithm, the key steps are to use few numbers of CI tests and reduce the sizes of conditioning sets as much as possible. Based on these reasons and inspired by the algorithm PC, we present an algorithm, named fast and efficient PC(FEPC), for learning the adjacent neighbourhood of every variable. FEPC implements the CI tests by three kinds of orders, which reduces the high order CI tests significantly. Compared with current algorithm proposals, the experiment results show that FEPC has better accuracy with fewer numbers of condition independence tests and smaller size of conditioning sets. The highest reduction percentage of CI test is 83.3% by EFPC compared with PC algorithm. | LI Yanying YANG Youlong ZHU Xiaofeng YANG Wenming | 2015 | Wuhan University Journal of Natural Sciences2015，20，3： | 2 |
| 2 | Surface-roughness-adjustable Au nanorods with strong plasmon absorption and abundant hotspots for improved SERS and photothermal performances显示文摘The rational optimization of plasmonic property of metal nanocrystals by manipulating the structure and morphology is crucial for the plasmon-enhanced application and has always been an urgent issue.Herein,Au nanorods with tunable surface roughness are prepared by growing PbS,overgrowing Au,and dissolving PbS nanoparticles on the basis of smooth Au nanorods.The transverse plasmon resonance of Au nanorods is notably improved due to plasmon coupling between Au nanorods and the surface-modified Au nanoparticles,resulting in the strong and full-spectrum light absorption.Numerical simulations demonstrate that the surface-rough Au nanorods have abundant and full-surround hotspots coming from surface particle–particle plasmon coupling between ultrasmall nanogaps,sharp tips,and uneven areas on Au nanorods.With these characters,the surface-roughness-adjustable Au nanorods possess high tunability and enhancement of surface-enhanced Raman scattering(SERS)detection of Rhodamine B and significantly improved photothermal conversion efficiency.Au nanorods with the largest surface roughness have the highest Raman enhancement factor both at 532 and 785 nm laser excitation.Meanwhile,photothermal conversion experiments under near-infrared(808 nm)and simulated sunlight irradiation confirm that the Au nanorods with rough surface have prominent photothermal conversion efficiency and can be regarded as promising candidates for photothermal therapy and solar-driven water evaporation. | Sijing Ding Liang Ma Jingru Feng Youlong Chen Dajie Yang Ququan Wang | 2022 | Nano Research2022，15，3： | 2 |
| 3 | A Bayesian Classi- fier Learning Algorithm Based on Optimization Model显示文摘 | Liu Sanyang Zhu Mingmin Yang Youlong | 2013 | Mathemati- cal Problems in Engineering2013，2013，： | 1 |
| 4 | Inner product space and concept classes induced by Bayesian networks 显示文摘 | YANG Youlong WU Yan | 2009 | Acta Application Mathematicae2009，106，3： | 1 |
| 5 | VC dimension and inner product space induced by Bayesian networks显示文摘 | YANG Youlong WU Yan | 2009 | International Journal of Approximate Reasoning2009，50，7： | 1 |
| 6 | Using junction trees for structural learning of Bayesian networks显示文摘The learning Bayesian network (BN) structure from data is an NP-hard problem and still one of the most exciting challenges in the machine learning.In this work,a novel algorithm is presented which combines ideas from local learning,constraintbased,and search-and-score techniques in a principled and effective way.It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree.Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables.Its comparison with the well-known K2 algorithm is also presented. | Mingmin Zhu Sanyang Liu Youlong Yang Kui Liu | 2012 | Journal of Systems Engineering and Electronics2012，23，2： | 1 |
| 7 | Flexural behavior ofwood beams strengthened with HFRP 显示文摘 | Yang Youlong Liu Jinwei Xiong Guangjing | 2013 | Construction and BuildingMaterials2013，43，： | 1 |
| 8 | Age-related changes in cerebral angiogenesis and fetal liver kinase-1 expression after cerebral ischemia/reperfusion显示文摘Cerebral angiogenesis in the early stages after cerebral ischemia injury is essential for the recovery of nerve function, in which fetal liver kinase-1 (Flk-1), as a major regulator of vasculogenesis and angiogenesis, plays a very important role. Microvessel density (MVD) was greater in an aged model group compared with the young sham operated group (P < 0.01). MVD and the sum of the lumen area were decreased in the aged group at 1, 3, 6 and 12 days following ischemia/reperfusion (I/R) injury compared with the young model group (P < 0.05 and P < 0.01, respectively). Flk-1 protein and mRNA expression was greater in the aged model group when compared with the young sham operated group (P < 0.01). Flk-1 protein and mRNA expression was lower in the aged group at 1, 3, 6 and 12 days after I/R compared with the young model group (P < 0.01). Flk-1 expression in aged rats attenuated rapidly, but was still maintained at relatively higher levels at 12 days following I/R in younger rats. The results suggest that angiogenesis was weakened after cerebral I/R in aged rats, and the mechanism of which might be correlated with attenuated expression of Flk-1 protein and mRNA. | Jiansheng Li Ke Liu Xinke Yang Jianfeng Gao Youlong Zhou Yuewu Zhao Zhengguo Liu Jingxia Liu | 2010 | Neural Regeneration Research2010，5，17： | 0 |
| 9 | Graphical model construction based on evolutionary algorithms显示文摘Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining. The experimental results show that the exact theoretical results and the approximations match very well. | Youlong YANG Yan WU Sanyang LIU | 2006 | 控制理论与应用(英文版)2006，4，4： | 0 |
| 10 | An Integrated Causal Path Identification Method显示文摘Finding causality merely from observed data is a fundamental problem in science. The most basic form of this causal problem is to determine whether X leads to Y or Y leads to X in the case of joint observation of two variables X, Y. In statistics, path analysis is used to describe the direct dependence between a set of variables. But in fact, we usually do not know the causal order between variables. However, ignoring the direction of the causal path will prevent researchers from analyzing or using causal models. In this study, we propose a method for estimating causality based on observed data. First, observed variables are cleaned and valid variables are retained. Then, a direct linear non-Gaussian acyclic graph models(DirectLiNGAM) estimates the causal order K between variables. The third step is to estimate the adjacency matrix B of the causal relationship based on K. Next, since B is not convenient for model interpretation, we use adaptive lasso to prune the causal path and variables. Further, a causal path graph and a recursive model are established. Finally, we test and debug the recursive model, obtain a causal model with good fit, and estimate the direct, indirect and total effects between causal variables. This paper overcomes the randomness assigning causal order to variables. This study is different from the researcher’s understanding of his own model by generating some form of simulation data. The simplest and relatively unsmooth statistical learning method used in this study has obvious advantages in the field of interpretable machine learning. | FEI Nina YANG Youlong | 2019 | Wuhan University Journal of Natural Sciences2019，24，4： | 0 |
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