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IEEE 28th Convention of Electrical and Electronics Engineers in Israel (IEEEI)

作者： Manevitz, Miriam Samson, Moshe Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-91905 Jerusalem Israel

ISBN: (纸本)9781479959877

One of the central problems in bioinformatics is de-novo finding of recurring motifs in the DNA. Since these motifs are preserved throughout evolution they probably have a significant biological role. One of the most widely used existing tools uses expectation maximization (EM) algorithm in order to learn the parameters of a statistical model based on partial data. One such method is based on assuming the Motif-data is generated by a Hidden Markov Model (HMM). This method is called the MEME algorithm. Despite its success, this method is in its essence a hill-climbing method, and as such, is known to be subject to being caught in local optima. In this work, we tackled the problem by using, instead, a genetic algorithm, and to search for the optimal probabilities of the HMM model. In certain occasions we succeeded in achieving better results using GA.

关键词： DNA biochemistry bioinformatics data mining evolution (biological) expectation-maximisation algorithm genetic algorithms genetics hidden Markov models learning (artificial intelligence) molecular biophysics molecular configurations probability DNA motif preservation EM algorithm HMM model MEME algorithm de novo motif finding evolution expectation maximization algorithm genetic algorithm hidden Markov model hill-climbing method local optima motif data generation optimal probability search parameter learning partial data recurring DNA motif finding statistical model Equations Genetic algorithms Hidden Markov models Mathematical model Sociology Statistics expectation-maximisation algorithm Hidden Markov models Genetic algorithm (GA) Bioinformatics molecular biology Parameter Learning statistical models (nuclear) Sociology Molecular Conformation DNA data mining biochemistry Mathematical Model Genetics evolution

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Differential correlation for sequencing data

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BMC Research Notes 2017年第1期10卷 1-9页

作者： Siska, Charlotte Kechris, Katerina Computational Bioscience Program Department of Pharmacology School of Medicine University of Colorado Anschutz Medical Campus Aurora CO United States Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus Aurora CO United States

Background: Several methods have been developed to identify differential correlation (DC) between pairs of molecular features from -omics studies. Most DC methods have only been tested with microarrays and other platforms producing continuous and Gaussian-like data. Sequencing data is in the form of counts, often modeled with a negative binomial distribution making it difficult to apply standard correlation metrics. We have developed an R package for identifying DC called Discordant which uses mixture models for correlations between features and the expectation maximization (EM) algorithm for fitting parameters of the mixture model. Several correlation metrics for sequencing data are provided and tested using simulations. Other extensions in the Discordant package include additional modeling for different types of differential correlation, and faster implementation, using a subsampling routine to reduce run-time and address the assumption of independence between molecular feature pairs. Results: With simulations and breast cancer miRNA-Seq and RNA-Seq data, we find that Spearman's correlation has the best performance among the tested correlation methods for identifying differential correlation. Application of Spearman's correlation in the Discordant method demonstrated the most power in ROC curves and sensitivity/specificity plots, and improved ability to identify experimentally validated breast cancer miRNA. We also considered including additional types of differential correlation, which showed a slight reduction in power due to the additional parameters that need to be estimated, but more versatility in applications. Finally, subsampling within the EM algorithm considerably decreased run-time with negligible effect on performance. Conclusions: A new method and R package called Discordant is presented for identifying differential correlation with sequencing data. Based on comparisons with different correlation metrics, this study suggests Spearman's correlation is

关键词： Posterior Probability Mixture Model expectation maximization algorithm Bullous Pemphigoid Median Absolute Deviation

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Empirical studies of cross-reference maximum likelihood estimate reconstruction for positron emission tomography

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Biomedical Engineering - Applications, Basis and Communications 2001年第1期13卷 1-7页

作者： Tu, K.-Y. Chen, T.-B. Lu, H.H.-S. Liu, R.-S. Chou, K.-L. Chen, C.-M. Chen, J.-C. Institute of Radiological Sciences National Yang-Ming University Taipei No.155 Li-Nong St. Sec. 2 Taiwan

Positron emission tomography (PET) can reveal subtle metabolic process, which is an important modality for diagnosis. However, spatial resolution of PET images is not as good as computed tomography (CT) or magnetic resonance imaging (MRI), which can show precise anatomical details. Our study is to improve image quality of PET using better reconstruction methods. In this paper, we use a new and efficient method to incorporate the correlated structural information obtained from MRI. A mean estimate smoothing the maximum likelihood estimate (MLE) locally within each region of interest is derived according to the boundaries provided by the structural information. Since the boundaries may not be correct, a penalized MLE using the mean estimate is sought. The resulting reconstruction is called a cross-reference maximum likelihood estimate (CRMLE). The CRMLE is obtained through a modified expectation maximization (EM) algorithm, which is shown to be computationally efficient by our phantom and clinical studies.

关键词： expectation maximization algorithm Maximum likelihood estimate Positron emission tomography

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Heterogeneous tail generalized common factor modeling

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Digital Finance 2023年第2期5卷 389-420页

作者： Hediger, Simon Näf, Jeffrey Paolella, Marc S. Polak, Paweł Department of Economics University of Zurich Zurich Switzerland Premedical Project Team INRIA Sophia-Antipolis Montpellier France Department of Banking and Finance University of Zurich Zurich Switzerland Swiss Finance Institute Zurich Switzerland Department of Applied Mathematics and Statistics Stony Brook University Stony Brook NY United States Institute for Advanced Computational Science Stony Brook University Stony Brook NY United States

A multivariate normal mean–variance heterogeneous tails mixture distribution is proposed for the joint distribution of financial factors and asset returns (referred to as Factor-HGH). The proposed latent variable model incorporates a Cholesky decomposition of the dispersion matrix to ensure a rich dependency structure for capturing the stylized facts of the data. It generalizes several existing model structures, with or without financial factors. It is further applicable in large dimensions due to a fast ECME estimation algorithm. The advantages of modelling financial factors and asset returns jointly under non-Gaussian errors are illustrated in an empirical comparison study between the proposed Factor-HGH model and classical financial factor models. While the results for the Fama–French 49 industry portfolios are in line with Gaussian-based models, in the case of highly tail heterogeneous cryptocurrencies, the portfolio based on the Factor-HGH model almost doubles the average return while keeping the volatility, the maximum drawdown, the turnover, and the expected shortfall at a low level. © The Author(s) 2023.

关键词： Asset pricing model C58 Cryptocurrencies expectation maximization algorithm Mixture distribution Portfolio optimization

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A memory-based theory of verbal cognition

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COGNITIVE SCIENCE 2005年第2期29卷 145-193页

作者： Dennis, S Univ Colorado Inst Cognit Sci Boulder CO 80301 USA

The syntagmatic paradigmatic model is a distributed, memory-based account of verbal processing. Built on a Bayesian interpretation of string edit theory, it characterizes the control of verbal cognition as the retrieval of sets of syntagmatic and paradigmatic constraints from sequential and relational long-term memory and the resolution of these constraints in working memory. Lexical information is extracted directly from text using a version of the expectation maximization algorithm. In this article, the model is described and then illustrated on a number of phenomena, including sentence processing, semantic categorization and rating, short-term serial recall, and analogical and logical inference. Subsequently, the model is used to answer questions about a corpus of tennis news articles taken from the Internet. The model's success demonstrates that it is possible to extract propositional information from naturally occurring text without employing a grammar, defining a set of heuristics, or specifying a priori a set of semantic roles.

关键词： sentence processing semantic memory short-term memory inference Bayesian string edit theory expectation maximization algorithm syntagmatic paradigmatic

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GraphHash: Topic-Aware Influence maximization Using Hash

GraphHash: Topic-Aware Influence Maximization Using Hash

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tang, Qian Guo, Peilin Guo, Jiaqi National University of Defense Technology School of Computer Science Changsha China

ISBN: (纸本)9798350362480

The influence maximization (IM) problem serve as a promising engine for the comprehension of social multimedia. However, current IM solutions often rely on fixed sampling and diffusion probabilities, which are insufficient to cater to the dynamic needs of online advertising, particularly in targeting users with diverse topic preferences. In addition, existing topic-aware IM solutions for online advertising are either time-consuming during the online query phase or burdened with substantial time and space overhead during the offline index construction phase. To solve the above problems, we propose GraphHash, a topic-aware target influence maximization solution that adopts a combination of offline and online phase. GraphHash constructs offline indexes by utilizing hash-based topic probabilities and selects the seed set with the maximal influence through intelligent indexing technique. In addition, we extend GraphHash using bottom-k sketching technology and apply it to different real-world graphs, achieving significant improvements of orders of magnitude over state-of-the-art baselines. © 2024 IEEE.

关键词： expectation maximization algorithm

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EM-TPACK-SMAR: An Evaluation of Teachers Comprehensive Literacy 2

EM-TPACK-SMAR: An Evaluation of Teachers Comprehensive Liter...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Shen, Yi Shanghai Communications Polytechnic Shanghai China

ISBN: (纸本)9798350372892

In these days, enrollment of students in education are rapidly increasing so, teaching quality evaluation need to be addressed. Education should have set of quality teaching activities, feedback method is time taking process, cannot assure demands of bright learning environment for evaluating teacher's comprehensive literacy. The expectation-maximization (EM)-Pedagogical Content Knowledge (TPACK)-Substitution Augmentation Modification and Redefinition (SMAR) perspective is proposed which taken input by Personal Knowledge Management (PKM) model where it involves sharing, application, innovation, acquitasation, identification and storage of knowledge. EM algorithm is employed to cluster multi dimensional Gaussian distribution and this used in two different perspectives i.e. TPACK and SMAR include observations of classrooms, lesson plan analysis, videos of classrooms. The proposed EM-TPACK-SMAR perspective model achieved high Precision and recall compared with existing methods i.e. precision 9 4. 3 2 % and recall 9 6. 0 1 %. © 2024 IEEE.

关键词： expectation maximization algorithm

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Poster: Optimizing ABS Deployment via LSTM-based Mobility and Data Traffic Demand Prediction 24

Poster: Optimizing ABS Deployment via LSTM-based Mobility an...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Nam, Jeehee Cho, Anna Park, Jihyun Kim, Chang Kyung Lee, SuKyoung Depart of Computer science Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798400706974

This paper proposes a proactive aerial base station (ABS) deployment framework for hotspots that optimizes the placement of ABSs based on mobility and data traffic demand prediction using a LSTM model. We design a greedy-based ABS deployment algorithm to solve the data rate maximization problem, which is known to be NP-hard. Simulation results demonstrate that the proposed algorithm achieves a higher data rate with fewer ABSs compared to benchmark methods. © 2024 Copyright held by the owner/author(s).

关键词： expectation maximization algorithm

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Risk assessment of water traffic accidents considering coupling effects 12

Risk assessment of water traffic accidents considering coupl...

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12th International Conference on Transportation and Traffic Engineering, ICTTE 2023

作者： Kaixuan, Zhang Hanwen, Fan College of Transportation Engineering Dalian Maritime University Dalian China

ISBN: (纸本)9781839539954

To maintain the safety of ship navigation and analyse the relationship between water traffic accidents and their influence factors, this article proposes a two-stage risk assessment model based on correlation analysis method and Bayesian network theory. In the first stage, preselecting the influence factors that affect the severity of water traffic accidents through literature review and expert opinions, considering the correlated characteristics of the influencing factors, Pearson correlation analysis method is used to analyse the relevance between the influencing factors;in the second stage, the Bayesian network theory is used to establish a risk assessment model for water traffic accidents, the parameters of the Bayesian network is trained based on the water traffic accident investigation reports, considering the problem of missing statistical data, the expectation maximization algorithm is used to compensate, then analyse coupling relationship between influencing factors in different scenarios by adjusting the states between different influencing factors. The research results indicate that the two most conflicting factors among single factors are ship factors and personal factors in order;the strongest coupling effect between any two factors is environmental factors and ship factors. The research results can provide theoretical reference for maritime departments to improve ship navigation safety. ©The Institution of Engineering & Technology 2023.

关键词： expectation maximization algorithm

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Channel Modeling Using Rayleigh and Rice Sum Approximation 26

Channel Modeling Using Rayleigh and Rice Sum Approximation

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2024 IEEE International Conference on Automation/26th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2024

作者： Carvajal, Rodrigo Orellana, Rafael Coronel, Maria Aguero, Juan C. Pontificia Universidad Católica de Valparaíso Escuela de Ingeniería Eléctrica Valparaíso Chile Universidad de Santiago de Chile Departamento de Ingeniería Eléctrica Santiago Chile Universidad Técnológica Metropolitana Departamento de Electricidad Santiago Chile Universidad Técnica Federico Santa María Departamento de Electrónica Valparaíso Chile

ISBN: (纸本)9798350378115

In this paper we develop an identification technique for the multipath wireless channel utilizing a discrete sum distribution approximation. We focus on three of the main distributions for the multi-path channel: Rayleigh, Rice and Nakagami-m. The distributions are approximated using a weighted sum of a Rayleigh and up to three distributions. The estimation procedure is based on the Maximum Likelihood approach and Akaike's Information Criterion. We show via simulations that when the channel is Rayleigh distributed, the estimation yields the correct Rayleigh distribution. When the channel distribution is Rice, the estimation yields one Rician term, whilst when the true channel distribution is Nakagami- m, the estimation yields an adequate approximation with a Rayleigh and Rice terms. © 2024 IEEE.

关键词： expectation maximization algorithm

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