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A Gaussian mixture model method for eigenvalue-based spectrum sensing with uncalibrated multiple antennas

SIGNAL PROCESSING 2022年 192卷 108404-108404页

作者： Majumder, Saikat Natl Inst Technol Raipur Dept Elect & Commun Engn GE Rd Raipur 492010 Chhattisgarh India

In this article, a novel spectrum sensing technique for cognitive radio (CR) is proposed where eigen-values of the covariance matrix of the received signal are used as features for detection with multiple uncalibrated antennas. In the proposed scheme, training decision vectors composed of eigenvalue com-ponents are approximated as Gaussian mixture model (GMM) and underlying distribution parameters are extracted using expectation-maximization (EM) algorithm. Using the obtained parameters, posterior probability of subsequent decision vectors are computed and the channel is classified as either occu-pied or unoccupied. This is different form existing spectrum sensing techniques where elements of the covariance matrix or its eigenvalues are reduced to a single decision statistic resulting in loss of use-ful discriminatory information. Proposed technique overcomes this limitation by forming decision vectors from the eigenvalue features and performing GMM based classification in multidimensional space. Simu-lation results also reveal that proposed method outperforms state-of-the-art techniques for detection of primary user (PU) signal using uncalibrated antennas. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Cognitive radio Spectrum sensing Eigenvalue Gaussian mixture model expectation-maximization algorithm Uncalibrated antennas

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Penetrance estimation of Alzheimer disease in SORL1 loss-of-function variant carriers using a family-based strategy and stratification by APOE genotypes (vol 14, 69, 2022)

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GENOME MEDICINE 2022年第1期14卷 69-69页

作者： Schramm, Catherine Charbonnier, Camille Zarea, Aline Lacour, Morgane Wallon, David Boland, Anne Deleuze, Jean-Francois Olaso, Robert Alarcon, Flora Campion, Dominique Nuel, Gregory Nicolas, Gael Normandie Univ UNIROUEN Dept Genet INSERM 22 Blvd Gambetta CS 76183 F-76000 Rouen France CNRMAJ Genom FHU G4 22 Blvd Gambetta CS 76183 F-76000 Rouen France Normandie Univ CHU Rouen UNIROUEN INSERMU1245Dept Neurol F-76000 Rouen France Univ Paris Saclay CEA Ctr Natl Rech Genom Humaine F-91057 Evry France Univ Paris MAP5 UMR CNRS 8145 F-75270 Paris France Rouvray Psychiat Hosp Dept Res F-76681 Sotteville Les Rouen France Sorbonne Univ LPSM CNRS 8001 F-75005 Paris France

Background Alzheimer disease (AD) is a common complex disorder with a high genetic component. Loss-of-function (LoF) SORL1 variants are one of the strongest AD genetic risk factors. Estimating their age-related penetrance is essential before putative use for genetic counseling or preventive trials. However, relative rarity and co-occurrence with the main AD risk factor, APOE-ε4, make such estimations difficult. Methods We proposed to estimate the age-related penetrance of SORL1-LoF variants through a survival framework by estimating the conditional instantaneous risk combining (i) a baseline for non-carriers of SORL1-LoF variants, stratified by APOE-ε4, derived from the Rotterdam study (N = 12,255), and (ii) an age-dependent proportional hazard effect for SORL1-LoF variants estimated from 27 extended pedigrees (including 307 relatives ≥ 40 years old, 45 of them having genotyping information) recruited from the French reference center for young Alzheimer patients. We embedded this model into an expectation-maximization algorithm to accommodate for missing genotypes. To correct for ascertainment bias, proband phenotypes were omitted. Then, we assessed if our penetrance curves were concordant with age distributions of APOE-ε4-stratified SORL1-LoF variant carriers detected among sequencing data of 13,007 cases and 10,182 controls from European and American case-control study consortia. Results SORL1-LoF variants penetrance curves reached 100% (95% confidence interval [99–100%]) by age 70 among APOE-ε4ε4 carriers only, compared with 56% [40–72%] and 37% [26–51%] in ε4 heterozygous carriers and ε4 non-carriers, respectively. These estimates were fully consistent with observed age distributions of SORL1-LoF variant carriers in case-control study data. Conclusions We conclude that SORL1-LoF variants should be interpreted in light of APOE genotypes for future clinical applications.

关键词： Alzheimer SORL1 APOE Penetrance Lifetime risk expectation-maximization algorithm Pedigree Missing genotypes

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Reliability analysis for multi-component systems considering stochastic dependency based on factor analysis

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年 169卷 108754-108754页

作者： Kong, Xuefeng Yang, Jun Li, Lei Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Reliability analysis for engineering systems with multiple components has gained increasing interest in recent. Most existing works assume that components follow identical degradation models and preset joint distributions or link functions to characterize the degradation interactions. However, distinct degradation characteristics of components are commonly observed in practice. Besides, the degradation interactions are usually complex and diverse, making the preset dependency structures not applicable. Confronted with the diverse degradation characteristics and complex degradation interactions, this paper offers a flexible reliability analysis framework for multi-component systems. First, a stochastic process-based general degradation model combining the Wiener process, Gamma process, and inverse Gaussian process is adopted to describe component degradation processes, and the factor analysis is employed to characterize the degradation interactions by seeking the latent common factors that dominate their interdependency. Thus the assumption of the identical degradation process and preset dependency structure can be relaxed, which enhances the robustness of the method. On this basis, we derive the explicit form of the system reliability function. An efficient expectation-maximization algorithm is then utilized for statistical inference to enable a fast computation. Finally, the superiority of the proposed method is demonstrated by two real case studies on lithium-ion battery packs.

关键词： Reliability analysis Multi-component systems Stochastic process Stochastic dependency Factor analysis expectation-maximization algorithm

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A Robust Point Set Registration Approach With Multiple Effective Constraints

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2020年第12期67卷 10931-10941页

作者： Sun, Jing Sun, Zhan-Li Lam, Kin-Man Zeng, Zhigang Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Anhui Univ Sch Elect Engn & Automat Hefei 230601 Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

How to accurately register point sets still remains a challenging task, due to some unfavorable factors. In this article, a robust point set registration approach is proposed based on the Gaussian mixture model (GMM) with multiple effective constraints. The GMM is established by wrapping a model point set to a target point set, via a spatial transformation. Instead of a displacement model, the spatial transformation is decomposed as two types of transformations, an affine transformation and a nonaffine deformation. For the affine transformation, a constraint term of the parameter vector is applied to improve the robustness and efficiency. In order to enforce the smoothness, the square norm of the kernel Hilbert space is adopted as a coherent constraint for the nonaffine deformation. Moreover, the manifold regularization is utilized as a constraint in the proposed model, to capture the spatial geometry of point sets. In addition, the expectation-maximization algorithm is developed to solve the unknown variables of the proposed model. Compared to the state-of-the-art approaches, the proposed model is more robust to deformation and rotation, due to the use of multiple effective constraints. Experimental results on several widely used data sets demonstrate the effectiveness of the proposed model.

关键词： Strain Shape Kernel Sun Gaussian mixture model Deformable models expectation-maximization algorithm Gaussian mixture model point set registration

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Robust algorithms for multiphase regression models

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APPLIED MATHEMATICAL MODELLING 2020年第Part2期77卷 1643-1661页

作者： Lu, Kang-Ping Chang, Shao-Tung Natl Taichung Univ Sci & Technol Dept Appl Stat Taichung Taiwan Natl Taiwan Normal Univ Dept Math Taipei Taiwan

This paper proposes a robust procedure for solving multiphase regression problems that is efficient enough to deal with data contaminated by atypical observations due to measurement errors or those drawn from heavy-tailed distributions. Incorporating the expectation and maximization algorithm with the M-estimation technique, we simultaneously derive robust estimates of the change-points and regression parameters, yet as the proposed method is still not resistant to high leverage outliers we further suggest a modified version by first moderately trimming those outliers and then implementing the new procedure for the trimmed data. This study sets up two robust algorithms using the Huber loss function and Tukey's biweight function to respectively replace the least squares criterion in the normality-based expectation and maximization algorithm, illustrating the effectiveness and superiority of the proposed algorithms through extensive simulations and sensitivity analyses. Experimental results show the ability of the proposed method to withstand outliers and heavy-tailed distributions. Moreover, as resistance to high leverage outliers is particularly important due to their devastating effect on fitting a regression model to data, various real-world applications show the practicability of this approach. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Multiphase regression models Change-point Outliers Robust regression expectation-maximization algorithm

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Morphological Multi-Cell Discrimination for Robust Cell Segmentation

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IEEE ACCESS 2020年 8卷 49837-49847页

作者： Lee, Hyun-Gyu Lee, Sang-Chul Inha Univ Dept Comp Engn Incheon 22212 South Korea DoAI Inc Yongin 17097 South Korea

Cell segmentation is challenging owing to the existence of various experimental configurations, cell shapes that cannot be mathematically defined, and ambiguous cell boundaries. We propose a cell segmentation method using cell region discriminator and multi-cell discriminator trained using heterogeneous machine-learning techniques such as logistic regression, expectation-maximization, and support vector machine (SVM). The cell-region discriminator identifies the regions where cells are found in images obtained from microscopes via a secondary logistic regressor, and its features use statistical information as well as the distribution of neighbor intensities. The SVM-based multi-cell discriminator determines whether multiple cells are present in the region detected by the cell-region discriminator and whether the region should be divided using the expectation-maximization algorithm. We suggest features for the boundary sectional area and the least square error for cell surface fitting to train the multi-cell discriminator. Using the features and the SVM, the multi-cell discriminator can be trained without overfitting, even for small training data. During this process, the proposed convex cell surface enhances the clustering performance. In experiments, our method based on two discriminators stably divided connected cells even when the contrast between a cell and the background area was small, and it outperformed state-of-the-art methods in terms of cell detection and segmentation accuracy.

关键词： Image segmentation machine learning regression analysis support vector machine expectation-maximization algorithm

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Estimation and Prediction for the Generalized Half Normal Distribution under Hybrid Censoring

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JOURNAL OF TESTING AND EVALUATION 2020年第2期48卷 1071-1094页

作者： Sultana, Farha Tripathi, Yogesh Mani Indian Inst Technol Patna Dept Math Bihta 801106 Bihar India

In this article, we make estimation and prediction inferences for the generalized half normal distribution. The maximum likelihood and Bayes estimators of unknown parameters are obtained based on hybrid Type I censored samples. We obtain asymptotic intervals using the observed Fisher information matrix and also construct bootstrap intervals of unknown parameters. Bayes estimators are obtained under the squared error loss function using different approximation methods. We also construct the highest posterior density intervals of unknown parameters. Further one- and two-sample predictors and prediction intervals of censored observations are discussed. A Monte Carlo simulation study is conducted to compare the performance of the proposed methods. We further analyze a real data set for illustrative purposes. Finally, conclusions are presented.

关键词： Bayes estimates expectation-maximization algorithm hybrid Type I censoring importance sampling scheme Bayesian prediction Tierney and Kadane method

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Noise can speed backpropagation learning and deep bidirectional pretraining

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NEURAL NETWORKS 2020年 129卷 359-384页

作者： Kosko, Bart Audhkhasi, Kartik Osoba, Osonde Univ Southern Calif Dept Elect & Comp Engn Signal & Image Proc Inst Los Angeles CA 90089 USA RAND Corp Santa Monica CA 90401 USA Google Inc New York NY USA

We show that the backpropagation algorithm is a special case of the generalized expectation-maximization (EM) algorithm for iterative maximum likelihood estimation. We then apply the recent result that carefully chosen noise can speed the average convergence of the EM algorithm as it climbs a hill of probability or log-likelihood. Then injecting such noise can speed the average convergence of the backpropagation algorithm for both the training and pretraining of multilayer neural networks. The beneficial noise adds to the hidden and visible neurons and related parameters. The noise also applies to regularized regression networks. This beneficial noise is just that noise that makes the current signal more probable. We show that such noise also tends to improve classification accuracy. The geometry of the noise-benefit region depends on the probability structure of the neurons in a given layer. The noise-benefit region in noise space lies above the noisy-EM (NEM) hyperplane for classification and involves a hypersphere for regression. Simulations demonstrate these noise benefits using MNIST digit classification. The NEM noise benefits substantially exceed those of simply adding blind noise to the neural network. We further prove that the noise speed-up applies to the deep bidirectional pretraining of neural-network bidirectional associative memories (BAMs) or their functionally equivalent restricted Boltzmann machines. We then show that learning with basic contrastive divergence also reduces to generalized EM for an energy-based network probability. The optimal noise adds to the input visible neurons of a BAM in stacked layers of trained BAMs. Global stability of generalized BAMs guarantees rapid convergence in pretraining where neural signals feed back between contiguous layers. Bipolar coding of inputs further improves pretraining performance. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Backpropagation Noise benefit Stochastic resonance expectation-maximization algorithm Bidirectional associative memory Contrastive divergence

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Survival analysis for a new compounded bivariate failure time distribution in shock and competing risk models via an EM algorithm

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2020年第21期49卷 5123-5153页

作者： Shoaee, Shirin Khorram, Esmaile Shahid Beheshti Univ Fac Math Sci Dept Actuarial Sci Tehran Iran Amirkabir Univ Technol Fac Math & Comp Sci Dept Stat Tehran Iran

In this paper, two bivariate models based on the proposed methods of Marshall and Olkin are introduced. In the first model, the new bivariate distribution is presented based on the proposed method of Marshall and Olkin (1967) which has natural interpretations, and it can be applied in fatal shock models or in competing risks models. In the second model, the proposed method of Marshall and Olkin (1997) is generalized to bivariate case and a new bivariate distribution is introduced. We call these new distributions as the bivariate Gompertz (BGP) distribution and bivariate Gompertz-geometric (BGPG) distribution, respectively. Moreover, the BGP model can be obtained as a special case of the BGPG model. Then, we present various properties of the new bivariate models. In this regard, the joint and conditional density functions, the joint cumulative distribution function can be obtained in compact forms. Also, the aging properties and the bivariate hazard gradient are discussed. This model has five unknown parameters and the maximum likelihood estimators cannot be obtained in explicit form. We propose to use the EM algorithm to compute the maximum likelihood estimators of the unknown parameters, and it is computationally quite tractable. Also, Monte Carlo simulations are performed to investigate the effectiveness of the proposed algorithm. Finally, we analyze three real data sets for illustrative purposes.

关键词： Bivariate model competing risks model expectation-maximization algorithm gompertz distribution shock model

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Handling multi-mapped reads in RNA-seq

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2020年 18卷 1569-1576页

作者： Deschamps-Francoeur, Gabrielle Simoneau, Joel Scott, Michelle S. Univ Sherbrooke Fac Med & Sci Sante Dept Biochim & Genom Fonct Sherbrooke PQ J1E 4K8 Canada

Many eukaryotic genomes harbour large numbers of duplicated sequences, of diverse biotypes, resulting from several mechanisms including recombination, whole genome duplication and retro-transposition. Such repeated sequences complicate gene/transcript quantification during RNA-seq analysis due to reads mapping to more than one locus, sometimes involving genes embedded in other genes. Genes of different biotypes have dissimilar levels of sequence duplication, with long-noncoding RNAs and messenger RNAs sharing less sequence similarity to other genes than biotypes encoding shorter RNAs. Many strategies have been elaborated to handle these multi-mapped reads, resulting in increased accuracy in gene/transcript quantification, although separate tools are typically used to estimate the abundance of short and long genes due to their dissimilar characteristics. This review discusses the mechanisms leading to sequence duplication, the biotypes affected, the computational strategies employed to deal with multi-mapped reads and the challenges that still remain to be overcome. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： RNA-seq Multi-mapped reads Duplicated genes Noncoding RNAs Gene isoforms expectation-maximization algorithm

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