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A Novel Judge Mechanism to Enhance the Performance of Google Blog Search

JOURNAL OF INTERNET TECHNOLOGY 2018年第4期19卷 981-993页

作者： Chen, Lin-Chih Natl Dong Hwa Univ Dept Informat Management Shoufeng Township Hualien County Taiwan

In recent years, the online blogging community is growing bigger as the social network service. When it is growing, the blog posts are increasing day by day. Generally speaking, people were using the blog search engines to search and recommend potentially interesting blog posts. When people search from the blog search engines, they were faced with two major problems: synonymy (two different terms with the same meaning) and polysemy (a term with different meanings). In this paper, we use two semantic analysis methods, Latent Semantic Indexing (LSI) and Probabilistic LSI (PLSI), to solve these two problems. LSI uses singular value decomposition as the fundamental method to capture the synonymous relationship between terms. PLSI uses the expectation-maximization algorithm for parameter estimation to additionally deal with the problem of polysemy. Although PLSI can gracefully deal with these two semantic problems, it needs a huge computing time. To solve the problem of computing time, in this paper, we propose a novel termination mechanism to dynamically determine the required number of iterations for PLSI. According to the experiment results, the result derived from our mechanism can not only deal with these two semantic problems but also reach a cost-effective solution.

关键词： Blog posts Google blog search Web 2.0 expectation-maximization algorithm Semantic problems

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Hybrid system of ART and RBF neural networks for online clustering

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APPLIED SOFT COMPUTING 2017年 58卷 1-10页

作者： Bielecki, Andrzej Wojcik, Mateusz AGH Univ Sci & Technol Fac Elect Engn Automat Comp Sci & Biomed Engn Chair Appl Comp Sci Al Mickiewicza 30 PL-30059 Krakow Poland

An online clustering task is considered for machine state monitoring purpose. In the previous authors researches a classical ART-2 network was tested for online classification of operational states in the context of a wind turbine monitoring. Some drawbacks, however, were found when a data stream size had been increased. This case is investigated in this paper. Classical ART-2 network can cluster data incorrectly when data points are compared by using Euclidean distance. Furthermore, ART-2 network can lose accuracy when data stream is processed for long time. The way of improving the ART-2 network is considered and two main steps of that are taken. At first, the stereographic projection is implemented. At the second step, a new type of hybrid neural system which consists of ART-2 and RBF networks with data processed by using the stereographic projection is introduced. Tests contained elementary scenarios for low-dimensional cases as well as higher dimensional real data from wind turbine monitoring. All the tests implied that an efficient system for online clustering had been found. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Online classification Hybrid system ART-2 neural network RBF neural network expectation-maximization algorithm

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Hapl-o-Mat: open-source software for HLA haplotype frequency estimation from ambiguous and heterogeneous data

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BMC BIOINFORMATICS 2017年第1期18卷 1-10页

作者： Schaefer, Christian Schmidt, Alexander H. Sauter, Juergen DKMS Gemeinnutzige GmbH Kressbach 1 D-72072 Tubingen Germany

Background: Knowledge of HLA haplotypes is helpful in many settings as disease association studies, population genetics, or hematopoietic stem cell transplantation. Regarding the recruitment of unrelated hematopoietic stem cell donors, HLA haplotype frequencies of specific populations are used to optimize both donor searches for individual patients and strategic donor registry planning. However, the estimation of haplotype frequencies from HLA genotyping data is challenged by the large amount of genotype data, the complex HLA nomenclature, and the heterogeneous and ambiguous nature of typing records. Results: To meet these challenges, we have developed the open-source software Hapl-o-Mat. It estimates haplotype frequencies from population data including an arbitrary number of loci using an expectation-maximization algorithm. Its key features are the processing of different HLA typing resolutions within a given population sample and the handling of ambiguities recorded via multiple allele codes or genotype list strings. Implemented in C++, Hapl-o-Mat facilitates efficient haplotype frequency estimation from large amounts of genotype data. We demonstrate its accuracy and performance on the basis of artificial and real genotype data. Conclusions: Hapl-o-Mat is a versatile and efficient software for HLA haplotype frequency estimation. Its capability of processing various forms of HLA genotype data allows for a straightforward haplotype frequency estimation from typing records usually found in stem cell donor registries.

关键词： HLA Immunogenetics Population genetics Bioinformatics Haplotype expectation-maximization algorithm Open-source software

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Statistical inference for masked interval data with Weibull distribution under simple step-stress test and tampered failure rate model

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2017年第6期231卷 654-665页

作者： Azizi, Fariba Haghighi, Firoozeh Ghadiri, Elahe Torabi, Leila Univ Tehran Dept Math Stat & Comp Sci Tehran Iran

In this article, we consider the planning of simple step-stress life test in the presence of competing risks with the possibility of masked causes of failure and interval censoring. It is assumed that the competing risks have independent Weibull distribution with the common shape parameter, but different scale parameters and a tampered failure rate model is hold. Maximum likelihood estimates of the model parameters are obtained for both cases, with and without masking. An expectation-maximization algorithm is used to estimate maximum likelihood estimates based on the masked data. For the case without masking, the Fisher information matrix is derived, and the problem of choosing the optimal test plan is considered using variance-optimality as well as determinant-optimality criteria. Through Monte Carlo simulations, the precision of the estimates is assessed and the optimal test plans are compared. Finally, the results are illustrated with an example.

关键词： Competing risks determinant optimality expectation-maximization algorithm interval censoring masked data optimal plan tampered failure rate model variance optimality Weibull distribution

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Semiparametric time-to-event modeling in the presence of a latent progression event

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BIOMETRICS 2017年第2期73卷 463-472页

作者： Rice, John D. Tsodikov, Alex Univ Michigan Dept Biostat Ann Arbor MI 48109 USA

In cancer research, interest frequently centers on factors influencing a latent event that must precede a terminal event. In practice it is often impossible to observe the latent event precisely, making inference about this process difficult. To address this problem, we propose a joint model for the unobserved time to the latent and terminal events, with the two events linked by the baseline hazard. Covariates enter the model parametrically as linear combinations that multiply, respectively, the hazard for the latent event and the hazard for the terminal event conditional on the latent one. We derive the partial likelihood estimators for this problem assuming the latent event is observed, and propose a profile likelihood-based method for estimation when the latent event is unobserved. The baseline hazard in this case is estimated nonparametrically using the EM algorithm, which allows for closed-form Breslow-type estimators at each iteration, bringing improved computational efficiency and stability compared with maximizing the marginal likelihood directly. We present simulation studies to illustrate the finite-sample properties of the method;its use in practice is demonstrated in the analysis of a prostate cancer data set.

关键词： expectation-maximization algorithm Semiparametric methods Survival analysis

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Stomped-t: A novel probability distribution for rough-probabilistic clustering

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INFORMATION SCIENCES 2017年 421卷 104-125页

作者： Banerjee, Abhirup Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata India

Rough clustering is one of the principal research areas in data mining, machine learning, pattern recognition, and bioinformatics. Among different variants of rough clustering, rough-probabilistic clustering is a new concept introduced recently. In rough-probabilistic clustering, a class is defined as the union of two disjoint regions, namely, a crisp lower approximation region and a probabilistic boundary region. In this regard, stomped normal (SN) distribution provides a statistical modeling of the data set in rough-probabilistic clustering framework. The SN distribution models the central tendency, dispersion, and width of the lower approximation region of each class using its mean, variance, and width parameter, respectively. However, it does not take into consideration the property of kurtosis of the class distribution, which controls the concentration of values around mean and shape of the tail of data distribution. In this background, a novel probability distribution, named stomped-t (St) distribution, is introduced in the paper for rough-probabilistic clustering. The proposed probability distribution incorporates the property of kurtosis into the SN framework. The proposed St probability distribution is then integrated within the rough-probabilistic clustering framework for precise and robust clustering of the data. The efficacy of the proposed clustering algorithm is demonstrated for unsupervised data clustering and image segmentation problems, along with a comparative performance analysis with related algorithms. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Rough-probabilistic clustering Stomped normal distribution Image segmentation Hidden Markov random field model expectation-maximization algorithm

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ExomeHMM: A Hidden Markov Model for Detecting Copy Number Variation Using Whole-Exome Sequencing Data

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CURRENT BIOINFORMATICS 2017年第2期12卷 147-155页

作者： Guo, Cheng Yu, Zhenhua Wang, Minghui Li, Ao Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Anhui Peoples R China Univ Sci & Technol China Res Ctr Biomed Engn Hefei Anhui Peoples R China

Background: Copy number variations (CNVs), including amplification and deletion, are alterations of DNA copy number compared to a reference genome. CNVs play a crucial role in tumourigenesis and progression, including amplification of oncogenes and deletion of tumor suppressor genes that may significantly increase the risk of cancer. CNVs are also reported to be closely related with non-cancer diseases, such as Down syndrome, Parkinson disease, and Alzheimer disease. Objective: Whole-exome sequencing (WES) has been successfully applied to the discovery of gene mutations as well as clinical diagnosis. But it is quite challenging to evaluate the copy number using WES data due to read depth bias, exons' distribution pattern and normal cell contamination. Our aim is develop an efficient method to overcome these challenges and detect CNVs using WES data. Method: In this study, we present ExomeHMM, a hidden Markov model (HMM) based CNV detecting algorithm. ExomeHMM exploits relative read depth, a ratio based signal, to mitigate read depth distortion and employs exponential attenuated transition matrix to handle sparsely and non-uniformly distributed exons. expectation-maximization algorithm is used to optimize parameters for the proposed model. Finally, we use standard Viterbi algorithm to infer the copy number of exons. Results: Using previously identified CNVs in 1000 Genome Project data as golden standard, ExomeHMM achieves the highest F-score among the four methods compared in this study. When applied to triple-negative breast cancer data, ExomeHMM is capable to find abnormal genes that are significantly associated with breast cancer. Conclusion: In conclusion, ExomeHMM is a suitable tool for CNV detections in both healthy samples as well as clinic tumor samples on whole-exome sequencing data.

关键词： Copy number variation expectation-maximization algorithm hidden Markov model next generation sequencing viterbi algorithm whole-exome sequencing

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Joint modeling of linear degradation and failure time data with masked causes of failure under simple step-stress test

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2018年第8期88卷 1603-1615页

作者： Azizi, Fariba Haghighi, Firoozeh Univ Tehran Sch Math Stat & Comp Sci Coll Sci Tehran Iran

In this paper, we propose a method to model the relationship between degradation and failure time for a simple step-stress test where the underlying degradation path is linear and different causes of failure are possible. It is assumed that the intensity function depends only on the degradation value. No assumptions are made about the distribution of the failure times. A simple step-stress test is used to induce failure experimentally and a tampered failure rate model is proposed to describe the effect of the changing stress on the intensities. We assume that some of the products that fail during the test have a cause of failure that is only known to belong to a certain subset of all possible failures. This case is known as masking. In the presence of masking, the maximum likelihood estimates of the model parameters are obtained through the expectation-maximization algorithm by treating the causes of failure as missing values. The effect of incomplete information on the estimation of parameters is studied through a Monte-Carlo simulation. Finally, a real-world example is analysed to illustrate the application of the proposed methods.

关键词： expectation-maximization algorithm cause of failure intensity linear degradation path masked data reliability function

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Robust generative asymmetric GMM for brain MR image segmentation

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 151卷 123-138页

作者： Ji, Zexuan Xia, Yong Zheng, Yuhui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Northwestern Polytech Univ Sch Comp Sci Shaanxi Key Lab Speech & Image Informat Proc SAII Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci CMCC Xian 710072 Shaanxi Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

Background and objectives: Accurate segmentation of brain tissues from magnetic resonance (MR) images based on the unsupervised statistical models such as Gaussian mixture model (GMM) has been widely studied during last decades. However, most GMM based segmentation methods suffer from limited accuracy due to the influences of noise and intensity inhomogeneity in brain MR images. To further improve the accuracy for brain MR image segmentation, this paper presents a Robust Generative Asymmetric GMM (RGAGMM) for simultaneous brain MR image segmentation and intensity inhomogeneity correction. Method: First, we develop an asymmetric distribution to fit the data shapes, and thus construct a spatial constrained asymmetric model. Then, we incorporate two pseudo-likelihood quantities and bias field estimation into the model's log-likelihood, aiming to exploit the neighboring priors of within-cluster and between-cluster and to alleviate the impact of intensity inhomogeneity, respectively. Finally, an expectation maximization algorithm is derived to iteratively maximize the approximation of the data log-likelihood function to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. Results: To demonstrate the performances of the proposed algorithm, we first applied the proposed algorithm to a synthetic brain MR image to show the intermediate illustrations and the estimated distribution of the proposed algorithm. The next group of experiments is carried out in clinical 3T-weighted brain MR images which contain quite serious intensity inhomogeneity and noise. Then we quantitatively compare our algorithm to state-of-the-art segmentation approaches by using Dice coefficient (DC) on benchmark images obtained from IBSR and BrainWeb with different level of noise and intensity inhomogeneity. The comparison results on various brain MR images demonstrate the superior performances of the proposed algorithm in dealing with the noise and intensity i

关键词： Gaussian mixture model Asymmetric distribution expectation-maximization algorithm Brain MR image segmentation Markov random fields Intensity inhomogeneity

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Poles tracking of weakly nonlinear structures using a Bayesian smoothing method

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2017年 84卷 136-151页

作者： Stephan, Cyrille Festjens, Hugo Renaud, Franck Dion, Jean-Luc Off Natl Etud & Rech Aerosp F-92322 Chatillon France SUPMECA St Ouen St Ouen France

This paper describes a method for the identification and the tracking of poles of a weakly nonlinear structure from its free responses. This method is based on a model of multichannel damped sines whose parameters evolve over time. Their variations are approximated in discrete time by a nonlinear state space model. States are estimated by an iterative process which couples a two-pass Bayesian smoother with an expectation maximization (EM) algorithm. The method is applied on numerical and experimental cases. As a result, accurate frequency and damping estimates are obtained as a function of amplitude. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Modal testing Frequency Damping Identification Unscented Kalman filter expectation-maximization algorithm

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