On-line policy optimisation of spoken dialogue systems via live interaction with human subjects

Statistical dialogue models have required a large number of dialogues to optimise the dialogue policy, relying on the use of a simulated user. This results in a mismatch between training and live conditions, and significant development costs for the simulator thereby mitigating many of the claimed benefits of such models. Recent work on Gaussian process reinforcement learning, has shown that learning can be substantially accelerated. This paper reports on an experiment to learn a policy for a real-world task directly from human interaction using rewards provided by users. It shows that a usable policy can be learnt in just a few hundred dialogues without needing a user simulator and, using a learning strategy that reduces the risk of taking bad actions. The paper also investigates adaptation behaviour when the system continues learning for several thousand dialogues and highlights the need for robustness to noisy rewards. © 2011 IEEE.

Sparsity-Inducing Optimization Algorithm for the Extraction of Planar Structures in Noisy Point-Cloud Data

Most of the manual labor needed to create the geometric building information model (BIM) of an existing facility is spent converting raw point cloud data (PCD) to a BIM description. Automating this process would drastically reduce the modeling cost. Surface extraction from PCD is a fundamental step in this process. Compact modeling of redundant points in PCD as a set of planes leads to smaller file size and fast interactive visualization on cheap hardware. Traditional approaches for smooth surface reconstruction do not explicitly model the sparse scene structure or significantly exploit the redundancy. This paper proposes a method based on sparsity-inducing optimization to address the planar surface extraction problem. Through sparse optimization, points in PCD are segmented according to their embedded linear subspaces. Within each segmented part, plane models can be estimated. Experimental results on a typical noisy PCD demonstrate the effectiveness of the algorithm.

复合垂直流人工湿地处理养殖废水的TSS动态研究

采用复合垂直流人工湿地系统,研究了人工湿地处理养殖废水过程中,湿地基质空隙中TSS(Total Suspended Solids)的动态变化及去除规律。中试实验结果表明,系统对TSS有良好的净化效果,平均去除率达到70%。循环养殖废水在复合垂直流人工湿地流动时,TSS的去除主要发生在下行池单元上层区域,沿水流方向,随着距离的延长,TSS的降解速率呈现先快后慢的趋势,同时根据动态变化曲线形式,建立了该实验条件下TSS在湿地床内沿程动态变化模型:CL=C0exp(2.967×10-4L2-0.04316L),

鄱阳湖南矶山自然保护区鲤、鲫的随机扩增多态DNA分析

利用RAPD(随机扩增多态)标记分析湖内鲤、鲫群体遗传多样性,从40个随机引物中各筛选出8个引物适合鲤、鲫群体RAPD扩增。在鲤群体中,共检测出60条带,其中多态性带42条,多态位点比率为70.00%;而在鲫群体中,共检测出61条带,其中多态性带40条,多态位点比率为65.57%。用POPGENE软件分析实验数据,结果显示:湖内鲤群体的遗传多样性水平(He=0.230 1,H0=0.391 0)和鲫群体的遗传多样性水平(He=0.218 6,H0=0.375 8)都较高,都有较大的遗传变异。而在鲤、鲫群体

中山市淡水浮游动物区系调查

2005年6～7月和12月对中山市主要淡水水体的浮游动物区系进行了调查。结果表明,中山市主要淡水水体浮游动物由38目92科129属317种组成。区系特点以原生动物最丰富,有30目82科102属263种,占浮游动物物种总数的83%;其次为轮虫,有4目4科19属37种组成,占12%;枝角类和桡足类物种数最少。各样点浮游动物种类在11～168种之间,种类最少的出现在洪奇沥,最多的出现在逍遥谷。研究结果表明,中山市淡水浮游动物资源比较丰富。各区系浮游动物的分布与水体的营养状态密切相关,污染严重的水体,种类数较少,

人工湿地植物根区微生物与净化效果的季节变化

人工湿地污水处理系统具有净化效果显著、建设和运行费用低廉、管理简便等优点.近年来越来越受到人们的重视.人工湿地生态系统受环境影响较大,研究人工湿地在不同季节的净化功能对于充分发挥其作用具有重要意义.对不同季节的芦苇(Phragmite saus-tralis)、茭白(Zizania caduciflora)和香蒲(Typha latifolia)等湿地系统植物根区、无植物对照基质微生物分布状况以及净化效果进行了研究,结果显示:(1)种植不同植物的湿地系统根区微生物数量不同,其湿地净化效果也不同;(2)季节

鱼类的干扰素系统和干扰素系统基因的鉴定

8 63计划项目(2 0 0 2AA62 60 10 ); 国家自然科学基金资助项目 (3 0 13 0 2 40 ;3 0 2 0 0 2 0 7); 中国科学院知识创新方向项目(KSCX2 SW 3 0 3); 武汉青年科技晨光计划 (2 0 0 15 0 0 5 0 5 5 )资助