High efficiency liquid crystal lasers giving "white light" emission from arrays and flexible substrates

The self-organization of the helical structure of chiral nematic liquid crystals combined with their sensitivity to electric fields makes them particularly interesting for low-threshold, wavelength tunable laser devices. We have studied these organic lasers in detail, ranging from the influence specific macroscopic properties, such as birefringence and order parameter, have on the output characteristics, to practical systems in the form of two-dimensional arrays, double-pass geometries and paintable lasers. Furthermore, even though chiral nematics are responsive to electric fields there is no facile means by which the helix periodicity can be adjusted, thereby allowing laser wavelength tuning, without adversely affecting the optical quality of the resonator. Therefore, in addition to studying the liquid crystal lasers, we have focused on finding a novel method with which to alter the periodicity of a chiral nematic using electric fields without inducing defects and degrading the optical quality factor of the resonator. This paper presents an overview of our research, describing (i) the correlation between laser output and material properties,(ii) the importance of the gain medium,(iii) multicolor laser arrays, and (iv) high slope efficiency (>60%) silicon back-plane devices. Overall we conclude that these materials have great potential for use in versatile organic laser systems.

Group Size and Composition of Black．and．White Snub．nosedMonkey(rhinopithecus)Estimated by Faecesof Sleeping Sites at Baima Snow Mountain

介绍了应用过夜地粪便来估计白马雪山黑白仰鼻猴群大小和组成的一种方法。该物种以单雄多雌单 元和全雄组的形式在树上过夜。粪粒根据其大小可分为3种类型：成年雄性的(最大)、成年雌性的(中等大小)和 未成年个体的(最小)。2000一2001年，搜集了滇西北白马雪山国家级自然保护区北部南任村(99。04 7E，28。34 7N) 附近黑白仰鼻猴群每个季节2个过夜地的粪粒。根据2001年11月猴群通过开阔地的数据来确定猴群组成。每个 季节，由于单雄多雌单元的成年个体数与其粪粒数正相关，所以二者回归直线的斜率可以看作是每个个体每晚 的平均排便量。由于该物种的栖息地主要为高山峡谷，而且能见度较低，因此，利用过夜地粪便比以前通过猴群 活动痕迹来估计猴群大小和组成相对准确、可靠。从估计成年雌性个体数的角度看，利用粪粒来估计种群大约有 9．4％的偏差。导致偏差的可能原因有杂草和灌丛对粪粒准确计数的影响、个体排粪率的差异以及成年雄性最小 粪粒与成年雌性最大粪粒的混淆等。该方法适应于栖息地和主要食物与本文研究种群相似的其他种群。

应用Wyaff-White方法计算寄生物种群γm的研究

寄生物种群γm的精确值与ln(Md)/d或ln(Md/2)/d之间存在着线性关系,这种关系可用两个公式表达: (1) γ_(m)=0.845ln(Md)/d; (2)γ_(m)=0.880ln(Md/2)/d。公式可以给出 γ_(m)的精确估计值, 公式2的估计效果更好。这种方法不要求组建生殖力表。图3表1参14

Derivative Kernels for Noise Robust ASR

Recently there has been interest in combined gen- erative/discriminative classifiers. In these classifiers features for the discriminative models are derived from generative kernels. One advantage of using generative kernels is that systematic approaches exist how to introduce complex dependencies beyond conditional independence assumptions. Furthermore, by using generative kernels model-based compensation/adaptation tech- niques can be applied to make discriminative models robust to noise/speaker conditions. This paper extends previous work with combined generative/discriminative classifiers in several directions. First, it introduces derivative kernels based on context- dependent generative models. Second, it describes how derivative kernels can be incorporated in continuous discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high- dimensional features of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task.

Sleeping sites of black-and-white snub-nosed monkeys (Rhinopithecus bieti) at Baima Snow Mountain, China

Data on sleeping sites of a group of black-and-white snub-nosed monkeys Rhinopithecus bieti (Colobinae, Primates) were collected between April-July and September-December 2001 to try to determine the factors affecting site selection at Nanren (99 degrees

Social organization of black-and-white snub-nosed monkeys (Rhinopithecus bieti) at Deqin, China

Data on social organization of two bands of black-and-white snub-nosed monkeys (Rhinopithecus bieti) 14 were collected when the monkeys were crossing an open spot at Nanren and Bamei (northwest of Yunnan, China) using a sampling rule where individuals wit

Seasonality of mating and birth in wild black-and-white snub-nosed monkeys (Rhinopithecus bieti) at Xiaochangdu, Tibet

Data on mating and birth seasonality were recorded in wild black-and-white snub-nosed monkeys (Rhinopithecus bieti) at Xiaochangdu in the Honglaxueshan National Nature Reserve, Tibet. This represents one of the harshest habitats utilized by any nonhuman p

A variational perspective on noise-robust speech recognition

Model compensation methods for noise-robust speech recognition have shown good performance. Predictive linear transformations can approximate these methods to balance computational complexity and compensation accuracy. This paper examines both of these approaches from a variational perspective. Using a matched-pair approximation at the component level yields a number of standard forms of model compensation and predictive linear transformations. However, a tighter bound can be obtained by using variational approximations at the state level. Both model-based and predictive linear transform schemes can be implemented in this framework. Preliminary results show that the tighter bound obtained from the state-level variational approach can yield improved performance over standard schemes. © 2011 IEEE.

Derivative kernels for noise robust ASR

Recently there has been interest in combining generative and discriminative classifiers. In these classifiers features for the discriminative models are derived from the generative kernels. One advantage of using generative kernels is that systematic approaches exist to introduce complex dependencies into the feature-space. Furthermore, as the features are based on generative models standard model-based compensation and adaptation techniques can be applied to make discriminative models robust to noise and speaker conditions. This paper extends previous work in this framework in several directions. First, it introduces derivative kernels based on context-dependent generative models. Second, it describes how derivative kernels can be incorporated in structured discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high-dimensional feature-spaces of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task. © 2011 IEEE.

Extending noise robust structured support vector machines to larger vocabulary tasks

This paper describes a structured SVM framework suitable for noise-robust medium/large vocabulary speech recognition. Several theoretical and practical extensions to previous work on small vocabulary tasks are detailed. The joint feature space based on word models is extended to allow context-dependent triphone models to be used. By interpreting the structured SVM as a large margin log-linear model, illustrates that there is an implicit assumption that the prior of the discriminative parameter is a zero mean Gaussian. However, depending on the definition of likelihood feature space, a non-zero prior may be more appropriate. A general Gaussian prior is incorporated into the large margin training criterion in a form that allows the cutting plan algorithm to be directly applied. To further speed up the training process, 1-slack algorithm, caching competing hypothesis and parallelization strategies are also proposed. The performance of structured SVMs is evaluated on noise corrupted medium vocabulary speech recognition task: AURORA 4. © 2011 IEEE.