Accurate ensemble pruning with PL-bagging

Ensemble pruning deals with the selection of base learners prior to combination in order to improve prediction accuracy and efficiency. In the ensemble literature, it has been pointed out that in order for an ensemble classifier to achieve higher prediction accuracy, it is critical for the ensemble classifier to consist of accurate classifiers which at the same time diverse as much as possible. In this paper, a novel ensemble pruning method, called PL-bagging, is proposed. In order to attain the balance between diversity and accuracy of base learners, PL-bagging employs positive Lasso to assign weights to base learners in the combination step. Simulation studies and theoretical investigation showed that PL-bagging filters out redundant base learners while it assigns higher weights to more accurate base learners. Such improved weighting scheme of PL-bagging further results in higher classification accuracy and the improvement becomes even more significant as the ensemble size increases. The performance of PL-bagging was compared with state-of-the-art ensemble pruning methods for aggregation of bootstrapped base learners using 22 real and 4 synthetic datasets. The results indicate that PL-bagging significantly outperforms state-of-the-art ensemble pruning methods such as Boosting-based pruning and Trimmed bagging.     

Yield improvement and yield variability reduction in an EOQ model with planned shortages and random yield

This paper reports the results of an investigation of a set of continuous time, constant demand inventory models under the condition of yield uncertainty. Specifically, the impact of yield improvement programs on lot size, backorder level, and the resulting costs are examined. Models for improving yield rate and reducing yield variability are developed and examined through a series of numerical exercises. In addition, a model for the simultaneous improvement of yield rate and yield variability is presented for the case where there is a relationship between the mean and variance of the yield distribution. In all cases, investment programs improve the picture with respect to manufacturing yield for processes which are not necessarily under statistical control.     

OCT图像散斑的形成机理和清除方法

根据光相干层析成像（OCT）的光学和光电检测系统特点，结合现有的理论模型，分析了OCT成像和散斑形成机理。指出失去相干性的多次散射光部分会被滤波电路排除，散斑是由高散射组织相干长度之内的不同散射截面上的若干具有nπ光程差的相干散射光彼此叠加形成的。最后讨论了消除散斑的方法。     

A stochastic model for localized disturbances and its applications

A stochastic model for local disturbances, particularly for a temporal harmonic with random modulations in amplitude and/or phase, is proposed in this paper. Results for the second moment responses of a linear single-degree-of-freedom system to this type of stochastic loading demonstrate a significant change in response characteristics due to a small uncertainty. A local phenomenon may last much longer and resonance may be smeared to a broad range. Integrated with wavelet transform, the proposed approach may be used to model a random process with non-stationary frequency content. Especially, it can be effectively used for Monte Carlo simulation to generate large size of samples that have similar characteristics in time and frequency domains as a pre-selected mother sample has. The technique has a great potential for the case where uncertainty study is warranted but the available samples are limited.     

合成孔径雷达（SAR）的最新发展

全文分若干单元分别论述了机载2D-SAR自聚焦技术，降低斑点技术，MTI-SAR和3D-SAR（含机载和空载），对上述各种SAR分别讨论了工作原理，列出了多个实例以及应用情况。     

数字散斑干涉技术及应用

介绍了数字散斑干涉技术在位移、应变、振动和医学诊断等领域中的应用和国内外发展状况。提出了一种新的应用领域—数字散斑干涉技术在语音识别中的应用,并对该方法作了介绍,讨论了它的特点。     

Probabilistic analysis of a static frame model

This paper describes our efforts during our participation in the Sandia Validation Workshop. The focus of the paper is the calibration of material models and simulation of random fields to characterize the variations of material properties across spatial field. Both parametric and non-parametric methods were used to represent uncertainty. Part of the challenge of this problem is the small amount of data that is available for the necessary probabilistic analyses in support of calibration, validation, accreditation and prediction activities. The analysis methods and corresponding results are described.     

Signal analysis of total internal reflection fluorescent speckle microscopy (TIR-FSM) and wide-field epi-fluorescence FSM of the actin cytoskeleton and focal adhesions in living cells

Fluorescent speckle microscopy (FSM) uses low levels of fluorescent proteins to create fluorescent speckles on cytoskeletal polymers in high‐resolution fluorescence images of living cells. The dynamics of speckles over time encode subunit turnover and motion of the cytoskeletal polymers. We sought to improve on current FSM technology by first expanding it to study the dynamics of a non‐polymeric macromolecular assembly, using focal adhesions as a test case, and second, to exploit for FSM the high contrast afforded by total internal reflection fluorescence microscopy (TIR‐FM). Here, we first demonstrate that low levels of expression of a green fluorescent protein (GFP) conjugate of the focal adhesion protein, vinculin, results in clusters of fluorescent vinculin speckles on the ventral cell surface, which by immunofluorescence labelling of total vinculin correspond to sparse labelling of dense focal adhesion structures. This demonstrates that the FSM principle can be applied to study focal adhesions. We then use both GFP‐vinculin expression and microinjected fluorescently labelled purified actin to compare quantitatively the speckle signal in FSM images of focal adhesions and the actin cytoskeleton in living cells by TIR‐FM and wide‐field epifluorescence microscopy. We use quantitative FSM image analysis software to define two new parameters for analysing FSM signal features that we can extract automatically: speckle modulation and speckle detectability. Our analysis shows that TIR‐FSM affords major improvements in these parameters compared with wide‐field epifluorescence FSM. Finally, we find that use of a crippled eukaryotic expression promoter for driving low‐level GFP‐fusion protein expression is a useful tool for FSM imaging. When used in time‐lapse mode, TIR‐FSM of actin and GFP‐conjugated focal adhesion proteins will allow quantification of molecular dynamics within interesting macromolecular assemblies at the ventral surface of living cells.     

Cycle detection using a stack

We present an algorithm for detecting periodicity in sequences produced by repeated application of a given function. Our algorithm uses logarithmic memory with high probability, runs in linear time, and is guaranteed to stop within the second loop through the cycle. We also present a partitioning technique that offers a time/memory tradeoff. Our algorithm is especially well suited for sequences where the cycle length is typically small compared to the length of the acyclic prefix.