Temporal and spatial aspects of an environmental stimulus influence the dynamics of behavioral regulation of the Aplysia siphon-withdrawal response.

Exposure to turbulence, an environmental stimulus, produces behavioral adaptation in the Aplysia siphon-withdrawal response (SWR). The authors show that the duration and spatial extent of turbulence influence adaptation recovery. In terms of duration, recovery in whole animals and reduced preparations (tail, siphon, and CNS) was more rapid after longer exposures to turbulence (10 min) than after briefer exposures (10 s-5 min). In terms of spatial extent, recovery in reduced preparations was more rapid after diffuse turbulence (tail and siphon together) compared with focal turbulence (siphon alone). Furthermore, spatial extent and duration interact: Duration regulates recovery only when turbulence is diffuse. Results suggest that SWR adaptation reflects a composite of cellular processes, including short-term synaptic enhancement in L30 inhibitory interneurons. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

初轧机主传动系统扭转自激振动的稳定性

用变量摄动法对初轧机主传动系统扭转自激振动的稳定性进行了初步研究，讨论了稳定性条件，为初轧机打滑状态的诊断、监测提供了参考依据。     

Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition

We propose subspace distance measures to analyze the similarity between intrapersonal face subspaces, which characterize the variations between face images of the same individual. We call the conventional intrapersonal subspace average intrapersonal subspace (AIS) because the image differences often come from a large number of persons. An intrapersonal subspace is referred to as specific intrapersonal subspace (SIS) if the image differences are from just one person. We demonstrate that SIS varies significantly from person to person, and most SISs are not similar to AIS. Based on these observations, we introduce the maximum a posteriori (MAP) adaptation to the problem of SIS estimation, and apply it to the Bayesian face recognition algorithm. Experimental results show that the adaptive Bayesian algorithm outperforms the non-adaptive Bayesian algorithm as well as Eigenface and Fisherface methods if a small number of adaptation images are available.     

HIST: A hierarchical self test methodology for chips,boards, and systems

This article presents the HIST approach, which allows the automated insertion of self test hardware into hierarchically designed circuits and systems to implement the RUNBIST instruction of the IEEE 1149.1 standard. To achieve an optimal and throughout self testable system, the inherent design hierarchy is fully exploited. All chips and boards are provided with appropriate test controllers at each hierarchy level. The approach is able to detect all those faults, which are in the scope of the underlying self test algorithms. In this paper the hierarchical test architecture, the test controllers as well as all necessary synthesis procedures are presented. Finally a successful application of the HIST approach to a cryptography processor is described.     

水工自密实混凝土的设计及应用

自密实混凝土具有高流态、免振等特性,适用于体形复杂、钢筋密集等难以浇筑的特殊部位。介绍了水工自密实混凝土的设计要求、思路及配合比参数选定方法。以三峡二期工程左岸厂房坝段压力钢管外包混凝土为例,给出了水工自密实混凝土的具体设计及应用。该项技术具有较好的推广应用前景。     

铁铝酸盐复合膨胀水泥化学预应力机理及其应用研究

本文阐述铁铝酸盐自应力混凝土的主要物理力学特征.研究了限制膨胀应变与配筋率之间的定量函数关系.通过正交试验和方差分析,确定了复合外加剂对其性能的影响.针对其在自应力管中的应用,提出多层中空圆柱理论计算模型,按弹性理论计算出自应力管初始应力分布规律.对比以前的线性理论计算误差,给出相应的修正系数.本文对类似的自应力混凝土,具有普遍的指导意义.     

矿区复杂地质条件下隧洞围岩塌方处理

主要介绍金堆城东川河改道工程隧洞施工过程中出现的一次较大塌方治理，塌方体松散并含孤石，传统方法无法处理，根据塌方体的特点，有针对性地采取自钻式锚杆、固结灌浆等施工工艺，并获得成功；对隧洞衬砌、地表陷坑进行了处理，确保了工程安全、质量。同时根据矿区隧洞围岩复杂多变的特点，对后期隧洞施工提出了建议。     

认知定位系统的自适应精度研究

为了能够实现定位精度的自适应,本文分析了认知无线电定位系统(cognitive positioning systems,CPS)的原理。该系统利用频谱及环境感知等模块中的传感器单元来获取所需的定位参数,并将其与到达时间(time of arrival,TOA)定位估计算法相结合,以克拉美罗下界(Cramer-Rao lower bound,CRLB)为准则分析最大似然(maximum likelihood,ML)定位精度自适应算法。本文还对单径和多径环境条件下定位精度自适应性能进行了比较。仿真结果表明,该算法能够实现定位精度的自适应,并能通过改善系统可用的频带带宽、分散频谱的数目、信号的调制方式等因素来提高定位精度。     

Interpreting the negative mood-helping literature via "mega"-analysis: A contrary view.

"Mega"-analysis was developed by M. Carlson and N. Miller (see record 1987-31249-001) as an extension of traditional meta-analytic procedures for conducting integrative reviews of existing research literatures. One such mega-analysis was conducted by Carlson and Miller to synthesize the literature on the relation between negative mood states and helping. That analysis found no support for a theoretical account (negative state relief) that had been confirmed previously by using various experimental approaches. In an attempt to reconcile the discrepancy, the logic and methods used in Carlson and Miller's mega-analysis of the negative mood-helping literature were examined, and several serious problems were found. These problems are discussed, and data are presented to show that the results of that mega-analysis, and perhaps all mega-analyses, should not be viewed with confidence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Cross lingual speech emotion recognition via triple attentive asymmetric convolutional neural network

The application of cross‐corpus for speech emotion recognition (SER) via domain adaptation methods have gain high acknowledgment for developing good robust emotion recognition systems using different corpora or datasets. However, the issue of cross‐lingual still remains a challenge in SER and needs more attention to resolve the scenario of applying different language types in both training and testing. In this paper, we propose a triple attentive asymmetric convolutional neural network to address the recognition of emotions for cross‐lingual and cross‐corpus speech in an unsupervised approach. The proposed method adopts the joint supervision of softmax loss and center loss to learn high power discriminative feature representations for target domain via the use of high quality pseudo‐labels. The proposed model uses three attentive convolutional neural networks asymmetrically, where two of the networks are used to artificially label unlabeled target samples as a result of their predictions from training on source labeled samples and the other network is used to obtain salient target discriminative features from the pseudo‐labeled target samples. We evaluate our proposed method on three different language types (i.e., English, German, and Italian) data sets. The experimental results indicate that, our proposed method achieves higher prediction accuracy over other state‐of‐the‐art methods.