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