Community structure in directed networks

We consider the problem of finding communities or modules in directed networks. In the past, the most common approach to this problem has been to ignore edge direction and apply methods developed for community discovery in undirected networks, but this approach discards potentially useful information contained in the edge directions. Here we show how the widely used community finding technique of modularity maximization can be generalized in a principled fashion to incorporate information contained in edge directions. We describe an explicit algorithm based on spectral optimization of the modularity and show that it gives demonstrably better results than previous methods on a variety of test networks, both real and computer generated.     

Rate-lossless PAPR reduction in per-subcarrier antenna selection OFDM systems

We propose a generalised rate-lossless technique for reducing the peak-to-average power ratio (PAPR) in orthogonal frequency-division multiplexing systems employing antenna selection on a per-subcarrier basis. The proposed technique, which is from the family of the active constellation extension (ACE) approaches to PAPR reduction, exploits the nulled subcarriers (for a given antenna) that arise through the antenna selection process to reduce the PAPR by introducing a controlled level of distortion to these subcarriers. We demonstrate that known PAPR reduction methods are unsuitable for per-subcarrier antenna selection systems, which motivates our modifications. We then show that our proposal achieves a much lower PAPR than traditional ACE methods for the given systems. Moreover, we demonstrate through a detailed theoretical analysis of the bit-error rate and a simulation analysis of the distortion transmitted on the nulled subcarriers, that the interference to the data signal caused by this distortion will not hinder practical system performance. Finally, we show that the efficacy of the proposed technique increases with the number of transmit antennas.     

Global stability analysis of fluid flows using sum-of-squares

This paper introduces a new method for proving global stability of fluid flows through the construction of Lyapunov functionals. For finite dimensional approximations of fluid systems, we show how one can exploit recently developed optimization methods based on sum-of-squares decomposition to construct a polynomial Lyapunov function. We then show how these methods can be extended to infinite dimensional Navier-Stokes systems using robust optimization techniques. Crucially, this extension requires only the solution of infinite-dimensional linear eigenvalue problems and finite-dimensional sum-of-squares optimization problems.We further show that subject to minor technical constraints, a general polynomial Lyapunov function is always guaranteed to provide better results than the classical energy methods in determining a lower-bound on the maximum Reynolds number for which a flow is globally stable, if the flow does remain globally stable for Reynolds numbers at least slightly beyond the energy stability limit. Such polynomial functions can be searched for efficiently using the SOS technique we propose.     

A New Threshold Selection Method for Separation of Objects and Background

A new threshold selection algorithm for objects and background separation using a histogram of the number of foreground regions was proposed. This method used a human separation method for objects and background, examined the changes in the number of foreground regions in the image with varying threshold and determined a proper threshold value. The proposed technique was applied to several images and was compared with other histogram-based segmentation methods. The possibility of extension to multi-level thresholding was also examined.     

随机矩阵理论在肺癌基因网络识别中的应用

利用随机矩阵理论（RMT）方法除去肺癌基因表达数据中的噪声,并将去噪后的数据分别用模块方法和等级聚类方法进行处理.比较两种方法处理后的结果,发现RMT-等级聚类方法不仅能给出模块,还能给出模块间的关联强度.研究表明,RMT-等级聚类方法是一种有效的识别基因网络的新方法. 关键词： 随机矩阵理论 等级聚类 基因网络 肺癌     

Optimum frequency selection in multifrequency interferometry

We describe a novel technique for measurement of absolute order of interference in multifrequency interferometry. An optimization criterion is introduced that leads to frequency selection formulations that are optimized with respect to the minimum number of frequencies required for achieving the maximum target dynamic range. The method is generalized to N frequencies and gives a definition of measurement reliability. We demonstrate the technique by means of coherent fringe projection for nonintrusive, full-field profilometry. Experimental data for three frequencies are presented.     

高能X射线基材料分解法基材料选择方法

选择基材料是高能双能X射线基材料分解法的重要环节。为了解基材料对材料识别精度的影响,确定选择基材料的方法和原则,利用美国国家标准与技术研究院(NIST)给出的材料的线性衰减系数,选择不同基材料,分析计算了各种待检测材料的原子序数和电子密度及相对误差。归纳总结出选取基材料的"临近原则",即基材料与待检测材料的原子序数接近,能提高待检测材料原子序数和电子密度的分解计算精度。在此基础上,提出了四能基材料分解法。理论计算表明四能基材料分解法比双能基材料分解法对多种材料的整体识别精度更高,更适合用于多种材料同时存在情况下的识别与检测。运用蒙特卡罗方法模拟验证了四能基材料分解法用于实际的可行性。     

Generalized frequency selection in multifrequency interferometry

We present a generalized frequency selection method for N-frequency interferometry to form an optimum geometric series at synthetic wavelengths. The absolute range that is measurable is bounded by the number of beat frequency operations, phase noise, and the number of wavelengths used to form the geometric series of synthetic wavelengths. Theoretical predictions are compared with experimental results from a full-field fringe projector. A comparison of this technique with the method of excess fractions shows orders-of-magnitude faster processing with similar measurement reliability.     

Fabrication of Optical Components and Modules Using Photo-fabrication Technique

We describe optical components and optical modules using a photofabrication technique to demonstrate applicability of the technique for optical purposes. A thick plate and a prism were fabricated to study fabric characteristics. Examples of optical modules implemented by the photofabrication technique are a beam expander and an interferometer baseplate.     

A Bootstrap Framework for Aggregating within and between Feature Selection Methods

In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a common concern in feature selection is that different approaches can give very different results when applied to similar datasets. Aggregating the results of different selection methods helps to resolve this concern and control the diversity of selected feature subsets. In this work, we implemented a general framework for the ensemble of multiple feature selection methods. Based on diversified datasets generated from the original set of observations, we aggregated the importance scores generated by multiple feature selection techniques using two methods: the Within Aggregation Method (WAM), which refers to aggregating importance scores within a single feature selection; and the Between Aggregation Method (BAM), which refers to aggregating importance scores between multiple feature selection methods. We applied the proposed framework on 13 real datasets with diverse performances and characteristics. The experimental evaluation showed that WAM provides an effective tool for determining the best feature selection method for a given dataset. WAM has also shown greater stability than BAM in terms of identifying important features. The computational demands of the two methods appeared to be comparable. The results of this work suggest that by applying both WAM and BAM, practitioners can gain a deeper understanding of the feature selection process.     

Estimating the number of components in Gaussian mixture models adaptively for medical image

An important but difficult problem of Gaussian mixture models (GMM) for medical image analysis is estimating and testing the number of components by model selection criterion. There are many available methods to estimate the k based on likelihood function. However, some of them need the maximum number of components is known as priori and data is usually over-fitted by them when log-likelihood function is far larger than penalty function. We investigate the log-characteristic function of the GMM to estimate the number of models adaptively for medical image. Our method defines the sum of weighted real parts of all log-characteristic functions of the GMM as a new convergent function and model selection criterion. Our new model criterion makes use of the stability of the sum of weighted real parts of all log-characteristic functions of the GMM when the number of components is larger than the true number of components. The univariate acidity, simulated 2D datasets and real 2D medical images are used to test and experiment results suggest that our method without any priori is more suited for large sample applications than other typical methods.     

SELECTION OF TRAINING SAMPLES FOR MODEL UPDATING USING NEURAL NETWORKS

One unique feature of neural networks is that they have to be trained to function. In developing an iterative neural network technique for model updating of structures, it has been shown that the number of training samples required increases exponentially as the number of parameters to be updated increases. Training the neural network using these samples becomes a time-consuming task. In this study, we investigate the use of orthogonal arrays for the sample selection. A comparison between this orthogonal arrays method and four other methods is illustrated by two numerical examples. One is the update of the felxural rigidities of a simply supported beam and the other is the update of the material properties and the boundary conditions of a circular plate. The results indicate that the orthogonal arrays method can significantly reduce the number of training samples without affecting too much the accuracy of the neural network prediction.     

Simulated Annealing Using Hybrid Monte Carlo

We propose a variant of the simulated annealing method for optimization in the multivariate analysis of differentiable functions. The method uses global actualizations via the hybrid Monte Carlo algorithm in their generalized version for the proposal of new configurations. We show how this choice can improve upon the performance of simulated annealing methods (mainly when the number of variables is large) by allowing a more effective searching scheme and a faster annealing schedule.     

Engineering quantum states of a nanoresonator via a simple auxiliary system

We show how to engineer an extensive range of nonlinear Hamiltonians for a nanomechanical resonator. The technique requires only a time-dependent drive applied to a Cooper-pair box or second oscillator to which the nanoresonator is coupled. This method allows one to generate a large number of nonclassical states, as well as Hamiltonians whose classical counterparts are chaotic.     

Analysis and interpretation of high transverse entanglement in optical parametric down conversion

Quantum entanglement associated with transverse wave vectors of down conversion photons is investigated based on the Schmidt decomposition method. We show that transverse entanglement involves two variables: orbital angular momentum and transverse frequency. We show that in the monochromatic limit high values of entanglement are closely controlled by a single parameter resulting from the competition between (transverse) momentum conservation and longitudinal phase matching. We examine the features of the Schmidt eigenmodes, and indicate how entanglement can be enhanced by suitable mode selection methods.     

A Simplified Numerical Simulation Method Considering Active Devices for Opto-Electronic Mixed Modules

A simplified simulation method based on the FDTD technique that can handle active devices is proposed. This method well suits the electrical crosstalk analysis of multi-channel integrated, opto-electronic mixed modules. We apply this method to an 8-channel integrated super-compact high-sensitivity optical module. The results show good agreement between simulations and measurements.     

On bosonization of 2d conformal field theories

We show how bosonic (free field) representations for so-called degenerate conformal theories are built by singular vectors in Verma modules. Based on this construction, general expressions of conformal blocks are proposed. As an example, we describe new modules for the SL(2) Wess-Zumino-Witten model. They are, in fact, the simplest nontrivial modules in a full set of bosonized highest weight representations of the ŝl₂ algebra. The Verma and Wakimoto modules appear as boundary modules of this set. Our construction also yields a new kind of bosonization in 2d conformal field theories.     

Frequency Bandwidth Estimation of TO Packaging Techniques for Laser Modules

Two simple methods for estimating the potential modulation bandwidth of TO packaging technique are presented. The first method is based upon the comparison of the measured frequency responses of the laser diodes and the TO laser modules, and the second is from the equivalent circuit for the test fixture, the TO header, the submount and the bonding wire. It is shown that the TO packaging techniques used in the experiments can potentially achieve a frequency bandwidth of over 10.5 GHz, and the two proposed methods give similar results.     

Selection of the Optimal Number of Topics for LDA Topic Model—Taking Patent Policy Analysis as an Example

This study constructs a comprehensive index to effectively judge the optimal number of topics in the LDA topic model. Based on the requirements for selecting the number of topics, a comprehensive judgment index of perplexity, isolation, stability, and coincidence is constructed to select the number of topics. This method provides four advantages to selecting the optimal number of topics: (1) good predictive ability, (2) high isolation between topics, (3) no duplicate topics, and (4) repeatability. First, we use three general datasets to compare our proposed method with existing methods, and the results show that the optimal topic number selection method has better selection results. Then, we collected the patent policies of various provinces and cities in China (excluding Hong Kong, Macao, and Taiwan) as datasets. By using the optimal topic number selection method proposed in this study, we can classify patent policies well.     

近红外光谱定量校正模型适用性研究

近红外光谱作为一种依靠模型进行分析的技术,对测定的样品进行模型适用性判断是得到可靠分析结果的前提。对于通过校正集样本近红外光谱测量和标准方法测定的基础数据依靠因子分析技术建立的多元校正模型,提出将因子分析与Mahalanobis距离相结合判断定量模型适用性的方法,以近红外光谱测定柴油十六烷值为例,对影响模型适用性判断的一些因素进行了讨论。