Recovery of ribosomal small subunit genes by assembly of short read community DNA sequence data generally fails, making taxonomic characterization difficult. Here, we solve this problem with a novel iterative method, ...
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Recovery of ribosomal small subunit genes by assembly of short read community DNA sequence data generally fails, making taxonomic characterization difficult. Here, we solve this problem with a novel iterative method, based on the expectation maximization algorithm, that reconstructs full-length small subunit gene sequences and provides estimates of relative taxon abundances. We apply the method to natural and simulated microbial communities, and correctly recover community structure from known and previously unreported rRNA gene sequences. An implementation of the method is freely available at https://***/csmiller/EMIRGE.
This letter considers the channel estimation for subband orthogonal frequency division multiple access (OFDMA) operating over highly time-frequency selective (i.e.,doubly selective) *** on general basis expansion mode...
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This letter considers the channel estimation for subband orthogonal frequency division multiple access (OFDMA) operating over highly time-frequency selective (i.e.,doubly selective) *** on general basis expansion model,two expectationmaximization-type channel estimation methods are proposed for subband OFDMA in order to overcome channel time-frequency *** proposed method is compared with conventional approach and is shown to be superior to the conventional one.
L. Sendur and I. W. Selesnick suggest four jointly non-Gaussian bivariate models to characterize the dependency between a coefficient and its parent, and respectively derive the corresponding MAP estimators based on n...
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ISBN:
(纸本)9780819489326
L. Sendur and I. W. Selesnick suggest four jointly non-Gaussian bivariate models to characterize the dependency between a coefficient and its parent, and respectively derive the corresponding MAP estimators based on noisy wavelet coefficients in detail in [6]. Among the four models, the second is a mixture model and it is quite complicated to evaluate parameters, so L. Sendur and I. W. Selesnick didn't give a concrete method. In this letter, a concrete mixture bivariate model will be described by drawing inspiration from Model 2. expectationmaximization (EM) algorithm is employed to find the parameters of new model. The simulation results show that the values of PSNR have a bit improvement compared with Model 1. The results can be viewed as a supplementary of model 2 in [6].
This paper attempts to quantify the impact of traffic incidents on travel time reliability using a newly proposed multi-state travel time reliability model. Given that the multi-state travel time reliability model pro...
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ISBN:
(纸本)9781457721977
This paper attempts to quantify the impact of traffic incidents on travel time reliability using a newly proposed multi-state travel time reliability model. Given that the multi-state travel time reliability model provides significantly better fits when compared to using a single-mode density function, it is possible to quantify the incident impacts more accurately. In order to obtain travel times, the study simulates weekday traffic on a section of I-66 over 17 days, once with incidents and once without them, using the INTEGRATION microscopic traffic simulation software. The simulated travel time data sets are then used to fit a three-state travel time reliability model (three normal distributions) to calibrate the parameters of the density function using the expectationmaximization (EM) algorithm. The study demonstrates that incidents do not introduce an additional component distribution when congestion has already onset;instead they increase the mean travel time and variability in travel times for the congested conditions. For instance, the 90th percentile travel time of the second component distribution increases by up to 93 percent. Additionally, the study addresses technical issues related to the calibration and interpretation of the model from a practical standpoint.
An Unsupervised Color Image Segmentation algorithm by using Finite Gaussian Mixture Model (GMM) is proposed in this paper. The parameters of GMM are estimated using expectationmaximization (EM) algorithm. A novel tec...
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ISBN:
(纸本)9783642227851
An Unsupervised Color Image Segmentation algorithm by using Finite Gaussian Mixture Model (GMM) is proposed in this paper. The parameters of GMM are estimated using expectationmaximization (EM) algorithm. A novel technique for initializing the EM algorithm is presented. The algorithm runs in two stages. The first stage performs an initial segmentation which initializes the EM algorithm. The second stage is the parameter estimation phase using EM algorithm. The scheme is computationally efficient in terms of space and time complexity.
Traffic speed is one of the most important quantities for travel information systems. Accurate speed forecasting can help in trip planning by allowing travelers to avoid the congested routes, either by choosing the al...
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ISBN:
(纸本)9781457721977
Traffic speed is one of the most important quantities for travel information systems. Accurate speed forecasting can help in trip planning by allowing travelers to avoid the congested routes, either by choosing the alternative routes or by changing the departure time. It is also helpful for traffic monitoring, control, and planning. An important feature of traffic is that it consists of free flow and congested regimes, which have significantly different properties. Training a single traffic speed predictor for both regimes typically results in suboptimal accuracy. To address this problem, a mixture of experts algorithm which consists of two regime-specific linear predictors and a decision tree gating function was developed. A generalized expectation maximization algorithm was used to train the linear predictors and the decision tree. The proposed algorithm was evaluated on a 5-mile stretch of I35 highway in Minneapolis containing 10 single loop detector stations, with prediction horizons ranging from 5 minutes to one hour ahead. Experimental results showed that mixture of experts approach outperforms several popular benchmark approaches.
Background: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 ...
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Background: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an expectationmaximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k). Results: We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. Conclusions: By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasi
One of the key prerequisite for a scalable, effective and efficient sensor network is the utilization of low-cost, low-overhead and high-resilient fault-inference techniques. To this end, we propose an intelligent age...
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One of the key prerequisite for a scalable, effective and efficient sensor network is the utilization of low-cost, low-overhead and high-resilient fault-inference techniques. To this end, we propose an intelligent agent system with a problem solving capability to address the issue of fault inference in sensor network environments. The intelligent agent system is designed and implemented at base-station side. The core of the agent system - problem solver - implements a fault-detection inference engine which harnesses expectationmaximization (EM) algorithm to estimate fault probabilities of sensor nodes. To validate the correctness and effectiveness of the intelligent agent system, a set of experiments in a wireless sensor testbed are conducted. The experimental results show that our intelligent agent system is able to precisely estimate the fault probability of sensor nodes.
We consider a bidimensional Ornstein-Uhlenbeck process to describe the tissue microvascularization in anti-cancer therapy. Data are discrete, partial and noisy observations of this stochastic differential equation (SD...
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We consider a bidimensional Ornstein-Uhlenbeck process to describe the tissue microvascularization in anti-cancer therapy. Data are discrete, partial and noisy observations of this stochastic differential equation (SDE). Our aim is to estimate the SDE parameters. We use the main advantage of a one-dimensional observation to obtain an easy way to compute the exact likelihood using the Kalman filter recursion, which allows to implement an easy numerical maximization of the likelihood. Furthermore, we establish the link between the observations and an ARMA process and we deduce the asymptotic properties of the maximum likelihood estimator. We show that this ARMA property can be generalized to a higher dimensional underlying Ornstein-Uhlenbeck diffusion. We compare this estimator with the one obtained by the well-known expectation maximization algorithm on simulated data. Our estimation methods can be directly applied to other biological contexts such as drug pharmacokinetics or hormone secretions.
Gastric emptying studies are of great interest in human and veterinary medical research to evaluate effects of medications or diets for promoting gastrointestinal motility and to examine unintended side-effects of new...
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Gastric emptying studies are of great interest in human and veterinary medical research to evaluate effects of medications or diets for promoting gastrointestinal motility and to examine unintended side-effects of new or existing medications, diets, or procedures. Summarizing gastric emptying data is important to allow easier comparison between treatments or groups of subjects and comparisons of results among studies. The standard method for assessing gastric emptying is by using scintigraphy and summarizing the nonlinear emptying of the radioisotope. A popular model for fitting gastric emptying data is the power exponential model. This model can only describes a globally decreasing pattern and thus has the limitation of poorly describing localized intragastric events that can occur during emptying. Hence, we develop a new model for gastric emptying studies to improve population and individual inferences using a mixture of nonlinear mixed effects models. One mixture component is based on a power exponential model which captures globally decreasing patterns. The other is based on a locally extended power exponential model which captures both local bumping and rapid decay. We refer to this mixture model as a two-component nonlinear mixed effects model. The parameters in our model have clear graphical interpretations that provide a more accurate representation and summary of the curves of gastric emptying pattern. Two methods are developed to fit our proposed model: one is the mixture of an expectation maximization algorithm and a global two-stage method and the other is the mixture of an expectation maximization algorithm and the Monte Carlo expectation maximization algorithm. We compare our methods using simulation, showing that the two approaches are comparable to one another. For estimating the variance and covariance matrix, the second approach appears approximately more efficient and is also numerically more stable in some cases. Our new model and approaches are
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