Perceived stress management skill mediates the relationship between optimism and positive mood following radical prostatectomy.

This study evaluated relations among optimism, perceived stress management skills (PSMS), and positive mood in 46 men who had surgical treatment for localized prostate cancer. The authors found that optimism, PSMS, and positive mood scores were positively correlated. Positive mood was unrelated to demographic and disease-related control variables. In a hierarchical regression model controlling for PSMS, the relationship between optimism and positive mood became nonsignificant, whereas PSMS remained a correlate of positive mood. Results suggest that the relationship between optimism and positive mood may be mediated by belief in being able to use stress management techniques effectively. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Extending boosting for large scale spoken language understanding

We propose three methods for extending the Boosting family of classifiers motivated by the real-life problems we have encountered. First, we propose a semisupervised learning method for exploiting the unlabeled data in Boosting. We then present a novel classification model adaptation method. The goal of adaptation is optimizing an existing model for a new target application, which is similar to the previous one but may have different classes or class distributions. Finally, we present an efficient and effective cost-sensitive classification method that extends Boosting to allow for weighted classes. We evaluated these methods for call classification in the AT&;T VoiceTone^® spoken language understanding system. Our results indicate that it is possible to obtain the same classification performance by using 30% less labeled data when the unlabeled data is utilized through semisupervised learning. Using model adaptation we can achieve the same classification accuracy using less than half of the labeled data from the new application. Finally, we present significant improvements in the “important” (i.e., higher weighted) classes without a significant loss in overall performance using the proposed cost-sensitive classification method.     

Finite sample properties of estimators of spatial autoregressive models with autoregressive disturbances

The article investigates the finite sample properties of estimators for spatial autoregressive models where the disturbance terms may follow a spatial autoregressive process. In particular we investigate the finite sample behavior of the feasible generalized spatial two-stage least squares (FGS2SLS) estimator introduced by Kelejian and Prucha (1998), the maximum likelihood (ML) estimator, as well as that of several other estimators. We find that the FGS2SLS estimator is virtually as efficient as the ML estimator. This is important because the ML estimator is computationally burdensome, and may even be forbidding in large samples, while the FGS2SLS estimator remains computationally feasible in large samples. Received: 20 January 2001 / Accepted: 31 August 2001     

Characteristics of origin-destination pair traffic in Funet

In this paper we analyze measurements gathered from a 2.5~Gbps link in the Finnish university network (Funet) in 2004. The traffic is broken down into origin-destination (OD) pair components based on source and destination IP address. We study the traffic characteristics of these components, and identify four typical representative OD pairs. For these pairs we investigate the validity of a moving IID Gaussian model. We find that the statistical properties of these OD pairs differ significantly from each other, with only some of them close to Gaussian. The OD pairs are also found to have some cross-correlation between each other, contradicting an often made assumption about OD pair independence. Furthermore, the existence of a mean-variance relation between the OD pairs is studied. We find that there is a relation between mean and variance, but for some periods of time it is rather weak.     

基于聚类算法实现信号盲分类

本文解决了信号处理、工业控制等领域存在的非平稳信号盲分类问题。在聚类中广泛应用的K-Means算法及其它基于中心的聚类算法有两个共同的缺陷-需要预先确定类数目且随机初始化中心引起性能不稳定。本文提出的算法较好地解决了这两个问题，提高了算法稳定性，实现了非平稳信号盲分类。提取非平稳信号的小波系数作为聚类的样本空间，分析聚类结果的统计偏差以估计类的数目，采用调和均值准则进行分类。最后给出的仿真结果表明本文提出的方法较传统的K-Means算法明显降低分类错误率。     

裂缝性油气藏采收率：100个裂缝性油气田实例的经验总结

通过对世界上100个裂缝性油气藏的综合评价，研究储集层及流体本身的性质(包括孔隙度、渗透率、黏度、可动油比例、含水饱和度、润湿性及裂缝分布特征等)和驱动机制及油藏管理战略(优化日产量和采用不同类型的提高采收率技术)对其最终采收率的影响。将裂缝性油气藏分为4类：I类的基质几乎没有孔隙度和渗透率，裂缝是储存空间和流体流动的通道；Ⅱ类的基质有较低的孔隙度和渗透率，基质提供储存空间，裂缝提供流动通道；Ⅲ类(微孔隙)的基质具有高孔隙度和低渗透率，基质提供储存空间，裂缝提供流动通道；Ⅳ类(大孔隙)的基质具有高的孔隙度和渗透率，基质提供储存空间和流动通道，裂缝仅增加渗透率。对26个Ⅱ类油气藏和20个Ⅲ类油气藏的开采历史的研究表明：Ⅱ类油气藏的采收率受水驱强度和最优日产量控制，日产量过高会很容易破坏Ⅱ类油气藏，一些Ⅱ类油气藏如果管理得当，采收率可以很高，不需要二次或三次采油；Ⅲ类油气藏的采收率主要受岩石和流体本身性质的影响，特别是基质渗透率、流体重度、润湿性以及裂缝强度等，不进行二次或三次采油不可能完全开采，往往需要采用一些提高采收率的专门技术。以往将Ⅱ类和Ⅲ类裂缝性油气藏归为一类，认识它们的区别将有助于选择更好的开发策略。     

Using classification techniques for informal requirements in the requirements analysis-supporting system

In order to efficiently develop large-scale and complicated software, it is important for system engineers to correctly understand users’ requirements. Most requirements in large-scale projects are collected from various stakeholders located in various regions, and they are generally written in natural language. Therefore, the initial collected requirements must be classified into various topics prior to analysis phases in order to be usable as input in several requirements analysis methods. If this classification process is manually done by analysts, it becomes a time-consuming task. To solve this problem, we propose a new bootstrapping method which can automatically classify requirements sentences into each topic category using only topic words as the representative of the analysts’ views. The proposed method is verified through experiments using two requirements data sets: one written in English and the other in Korean. The significant performances were achieved in the experiments: the 84.28 and 87.91 F1 scores for the English and Korean data sets, respectively. As a result, the proposed method can provide an effective function for an Internet-based requirements analysis-supporting system so as to efficiently gather and analyze requirements from various and distributed stakeholders by using the Internet.     

玉米须单体成分纯化及类别鉴定

以玉米须浸膏为原料,采用萃取和液相制备色谱相结合的办法分离得到高纯度玉米须黄酮单体,并优化出纯化工艺。     

Healing the psyche through music, myth, and ritual.

According to Carl Jung, a person's symptoms occur as a result of the psyche's creative attempt to self-regulate. These symptoms are viewed as the symbol-making function of the psyche. Music therapy as a therapeutic tool is also viewed as having a symbol-making function in that the physical act of making music involves conceptualizing one's symptoms into sounds. The sound serves as a musical symbol of the symptom. The myth of Orpheus is offered as an example of how early man amplified the creative potential contained in music to heal from life's woes. The Orphic archetype illustrates that music serves as a medium for healing and as an expression of Self. That psyche can be healed by music is a reminder of the connection between music therapy and the myth of Orpheus. In using the therapeutic tool of music one can transcend, through the symbol making process inherent in music therapy, the tension of opposites that are created at the crossroads of disease and wellness. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

CPM自滤波非稳腔YAG激光器研究

把由非谐振环和凸面全反射镜构成的自滤波非稳腔应用于撞脉冲锁模Ｎｄ：ＹＡＧ激光器，改善了光束的空间质量和输出稳定性，获得发散角为２倍衍射极限，能量５０ｍＪ，脉宽１０ｐｓ的锁模脉冲，并与未加滤波小孔的ＣＰＭ非稳腔的输出性能进行实验比较。，