Risk aversion and risk seeking in multicriteria forest management: a Markov decision process approach

Treesearch

Joseph Buongiorno; Mo Zhou; Craig Johnston

2017-01-01

Markov decision process models were extended to reflect some consequences of the risk attitude of forestry decision makers. One approach consisted of maximizing the expected value of a criterion subject to an upper bound on the variance or, symmetrically, minimizing the variance subject to a lower bound on the expected value.  The other method used the certainty...

Replica analysis for the duality of the portfolio optimization problem

NASA Astrophysics Data System (ADS)

Shinzato, Takashi

2016-11-01

In the present paper, the primal-dual problem consisting of the investment risk minimization problem and the expected return maximization problem in the mean-variance model is discussed using replica analysis. As a natural extension of the investment risk minimization problem under only a budget constraint that we analyzed in a previous study, we herein consider a primal-dual problem in which the investment risk minimization problem with budget and expected return constraints is regarded as the primal problem, and the expected return maximization problem with budget and investment risk constraints is regarded as the dual problem. With respect to these optimal problems, we analyze a quenched disordered system involving both of these optimization problems using the approach developed in statistical mechanical informatics and confirm that both optimal portfolios can possess the primal-dual structure. Finally, the results of numerical simulations are shown to validate the effectiveness of the proposed method.

Replica analysis for the duality of the portfolio optimization problem.

PubMed

Shinzato, Takashi

2016-11-01

In the present paper, the primal-dual problem consisting of the investment risk minimization problem and the expected return maximization problem in the mean-variance model is discussed using replica analysis. As a natural extension of the investment risk minimization problem under only a budget constraint that we analyzed in a previous study, we herein consider a primal-dual problem in which the investment risk minimization problem with budget and expected return constraints is regarded as the primal problem, and the expected return maximization problem with budget and investment risk constraints is regarded as the dual problem. With respect to these optimal problems, we analyze a quenched disordered system involving both of these optimization problems using the approach developed in statistical mechanical informatics and confirm that both optimal portfolios can possess the primal-dual structure. Finally, the results of numerical simulations are shown to validate the effectiveness of the proposed method.

A Probability Based Framework for Testing the Missing Data Mechanism

ERIC Educational Resources Information Center

Lin, Johnny Cheng-Han

2013-01-01

Many methods exist for imputing missing data but fewer methods have been proposed to test the missing data mechanism. Little (1988) introduced a multivariate chi-square test for the missing completely at random data mechanism (MCAR) that compares observed means for each pattern with expectation-maximization (EM) estimated means. As an alternative,…

Effects of Missing Data Methods in Structural Equation Modeling with Nonnormal Longitudinal Data

ERIC Educational Resources Information Center

Shin, Tacksoo; Davison, Mark L.; Long, Jeffrey D.

2009-01-01

The purpose of this study is to investigate the effects of missing data techniques in longitudinal studies under diverse conditions. A Monte Carlo simulation examined the performance of 3 missing data methods in latent growth modeling: listwise deletion (LD), maximum likelihood estimation using the expectation and maximization algorithm with a…

Autonomous entropy-based intelligent experimental design

NASA Astrophysics Data System (ADS)

Malakar, Nabin Kumar

2011-07-01

The aim of this thesis is to explore the application of probability and information theory in experimental design, and to do so in a way that combines what we know about inference and inquiry in a comprehensive and consistent manner. Present day scientific frontiers involve data collection at an ever-increasing rate. This requires that we find a way to collect the most relevant data in an automated fashion. By following the logic of the scientific method, we couple an inference engine with an inquiry engine to automate the iterative process of scientific learning. The inference engine involves Bayesian machine learning techniques to estimate model parameters based upon both prior information and previously collected data, while the inquiry engine implements data-driven exploration. By choosing an experiment whose distribution of expected results has the maximum entropy, the inquiry engine selects the experiment that maximizes the expected information gain. The coupled inference and inquiry engines constitute an autonomous learning method for scientific exploration. We apply it to a robotic arm to demonstrate the efficacy of the method. Optimizing inquiry involves searching for an experiment that promises, on average, to be maximally informative. If the set of potential experiments is described by many parameters, the search involves a high-dimensional entropy space. In such cases, a brute force search method will be slow and computationally expensive. We develop an entropy-based search algorithm, called nested entropy sampling, to select the most informative experiment. This helps to reduce the number of computations necessary to find the optimal experiment. We also extended the method of maximizing entropy, and developed a method of maximizing joint entropy so that it could be used as a principle of collaboration between two robots. This is a major achievement of this thesis, as it allows the information-based collaboration between two robotic units towards a same goal in an automated fashion.

The Effect of Missing Data Handling Methods on Goodness of Fit Indices in Confirmatory Factor Analysis

ERIC Educational Resources Information Center

Köse, Alper

2014-01-01

The primary objective of this study was to examine the effect of missing data on goodness of fit statistics in confirmatory factor analysis (CFA). For this aim, four missing data handling methods; listwise deletion, full information maximum likelihood, regression imputation and expectation maximization (EM) imputation were examined in terms of…

Measuring the volume of brain tumour and determining its location in T2-weighted MRI images using hidden Markov random field: expectation maximization algorithm

NASA Astrophysics Data System (ADS)

Mat Jafri, Mohd. Zubir; Abdulbaqi, Hayder Saad; Mutter, Kussay N.; Mustapha, Iskandar Shahrim; Omar, Ahmad Fairuz

2017-06-01

A brain tumour is an abnormal growth of tissue in the brain. Most tumour volume measurement processes are carried out manually by the radiographer and radiologist without relying on any auto program. This manual method is a timeconsuming task and may give inaccurate results. Treatment, diagnosis, signs and symptoms of the brain tumours mainly depend on the tumour volume and its location. In this paper, an approach is proposed to improve volume measurement of brain tumors as well as using a new method to determine the brain tumour location. The current study presents a hybrid method that includes two methods. One method is hidden Markov random field - expectation maximization (HMRFEM), which employs a positive initial classification of the image. The other method employs the threshold, which enables the final segmentation. In this method, the tumour volume is calculated using voxel dimension measurements. The brain tumour location was determined accurately in T2- weighted MRI image using a new algorithm. According to the results, this process was proven to be more useful compared to the manual method. Thus, it provides the possibility of calculating the volume and determining location of a brain tumour.

Network clustering and community detection using modulus of families of loops.

PubMed

Shakeri, Heman; Poggi-Corradini, Pietro; Albin, Nathan; Scoglio, Caterina

2017-01-01

We study the structure of loops in networks using the notion of modulus of loop families. We introduce an alternate measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link usage optimally. We propose weighting networks using these expected link usages to improve classical community detection algorithms. We show that the proposed method enhances the performance of certain algorithms, such as spectral partitioning and modularity maximization heuristics, on standard benchmarks.

Impacts of Maximizing Tendencies on Experience-Based Decisions.

PubMed

Rim, Hye Bin

2017-06-01

Previous research on risky decisions has suggested that people tend to make different choices depending on whether they acquire the information from personally repeated experiences or from statistical summary descriptions. This phenomenon, called as a description-experience gap, was expected to be moderated by the individual difference in maximizing tendencies, a desire towards maximizing decisional outcome. Specifically, it was hypothesized that maximizers' willingness to engage in extensive information searching would lead maximizers to make experience-based decisions as payoff distributions were given explicitly. A total of 262 participants completed four decision problems. Results showed that maximizers, compared to non-maximizers, drew more samples before making a choice but reported lower confidence levels on both the accuracy of knowledge gained from experiences and the likelihood of satisfactory outcomes. Additionally, maximizers exhibited smaller description-experience gaps than non-maximizers as expected. The implications of the findings and unanswered questions for future research were discussed.

Visual attention distracter insertion for improved EEG rapid serial visual presentation (RSVP) target stimuli detection

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Huber, David J.; Martin, Kevin

2017-05-01

This paper† describes a technique in which we improve upon the prior performance of the Rapid Serial Visual Presentation (RSVP) EEG paradigm for image classification though the insertion of visual attention distracters and overall sequence reordering based upon the expected ratio of rare to common "events" in the environment and operational context. Inserting distracter images maintains the ratio of common events to rare events at an ideal level, maximizing the rare event detection via P300 EEG response to the RSVP stimuli. The method has two steps: first, we compute the optimal number of distracters needed for an RSVP stimuli based on the desired sequence length and expected number of targets and insert the distracters into the RSVP sequence, and then we reorder the RSVP sequence to maximize P300 detection. We show that by reducing the ratio of target events to nontarget events using this method, we can allow RSVP sequences with more targets without sacrificing area under the ROC curve (azimuth).

Stochastic Approximation Methods for Latent Regression Item Response Models

ERIC Educational Resources Information Center

von Davier, Matthias; Sinharay, Sandip

2010-01-01

This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates…

Model-based clustering for RNA-seq data.

PubMed

Si, Yaqing; Liu, Peng; Li, Pinghua; Brutnell, Thomas P

2014-01-15

RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis. In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models. An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org

Using an EM Covariance Matrix to Estimate Structural Equation Models with Missing Data: Choosing an Adjusted Sample Size to Improve the Accuracy of Inferences

ERIC Educational Resources Information Center

Enders, Craig K.; Peugh, James L.

2004-01-01

Two methods, direct maximum likelihood (ML) and the expectation maximization (EM) algorithm, can be used to obtain ML parameter estimates for structural equation models with missing data (MD). Although the 2 methods frequently produce identical parameter estimates, it may be easier to satisfy missing at random assumptions using EM. However, no…

Direct Importance Estimation with Gaussian Mixture Models

NASA Astrophysics Data System (ADS)

Yamada, Makoto; Sugiyama, Masashi

The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method — which we call the Gaussian mixture KLIEP (GM-KLIEP) — is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.

Active inference and epistemic value.

PubMed

Friston, Karl; Rigoli, Francesco; Ognibene, Dimitri; Mathys, Christoph; Fitzgerald, Thomas; Pezzulo, Giovanni

2015-01-01

We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms.

Metal-induced streak artifact reduction using iterative reconstruction algorithms in x-ray computed tomography image of the dentoalveolar region.

PubMed

Dong, Jian; Hayakawa, Yoshihiko; Kannenberg, Sven; Kober, Cornelia

2013-02-01

The objective of this study was to reduce metal-induced streak artifact on oral and maxillofacial x-ray computed tomography (CT) images by developing the fast statistical image reconstruction system using iterative reconstruction algorithms. Adjacent CT images often depict similar anatomical structures in thin slices. So, first, images were reconstructed using the same projection data of an artifact-free image. Second, images were processed by the successive iterative restoration method where projection data were generated from reconstructed image in sequence. Besides the maximum likelihood-expectation maximization algorithm, the ordered subset-expectation maximization algorithm (OS-EM) was examined. Also, small region of interest (ROI) setting and reverse processing were applied for improving performance. Both algorithms reduced artifacts instead of slightly decreasing gray levels. The OS-EM and small ROI reduced the processing duration without apparent detriments. Sequential and reverse processing did not show apparent effects. Two alternatives in iterative reconstruction methods were effective for artifact reduction. The OS-EM algorithm and small ROI setting improved the performance. Copyright © 2012 Elsevier Inc. All rights reserved.

Nonlinear spatio-temporal filtering of dynamic PET data using a four-dimensional Gaussian filter and expectation-maximization deconvolution

NASA Astrophysics Data System (ADS)

Floberg, J. M.; Holden, J. E.

2013-02-01

We introduce a method for denoising dynamic PET data, spatio-temporal expectation-maximization (STEM) filtering, that combines four-dimensional Gaussian filtering with EM deconvolution. The initial Gaussian filter suppresses noise at a broad range of spatial and temporal frequencies and EM deconvolution quickly restores the frequencies most important to the signal. We aim to demonstrate that STEM filtering can improve variance in both individual time frames and in parametric images without introducing significant bias. We evaluate STEM filtering with a dynamic phantom study, and with simulated and human dynamic PET studies of a tracer with reversible binding behaviour, [C-11]raclopride, and a tracer with irreversible binding behaviour, [F-18]FDOPA. STEM filtering is compared to a number of established three and four-dimensional denoising methods. STEM filtering provides substantial improvements in variance in both individual time frames and in parametric images generated with a number of kinetic analysis techniques while introducing little bias. STEM filtering does bias early frames, but this does not affect quantitative parameter estimates. STEM filtering is shown to be superior to the other simple denoising methods studied. STEM filtering is a simple and effective denoising method that could be valuable for a wide range of dynamic PET applications.

Adaption of the temporal correlation coefficient calculation for temporal networks (applied to a real-world pig trade network).

PubMed

Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim

2016-01-01

The average topological overlap of two graphs of two consecutive time steps measures the amount of changes in the edge configuration between the two snapshots. This value has to be zero if the edge configuration changes completely and one if the two consecutive graphs are identical. Current methods depend on the number of nodes in the network or on the maximal number of connected nodes in the consecutive time steps. In the first case, this methodology breaks down if there are nodes with no edges. In the second case, it fails if the maximal number of active nodes is larger than the maximal number of connected nodes. In the following, an adaption of the calculation of the temporal correlation coefficient and of the topological overlap of the graph between two consecutive time steps is presented, which shows the expected behaviour mentioned above. The newly proposed adaption uses the maximal number of active nodes, i.e. the number of nodes with at least one edge, for the calculation of the topological overlap. The three methods were compared with the help of vivid example networks to reveal the differences between the proposed notations. Furthermore, these three calculation methods were applied to a real-world network of animal movements in order to detect influences of the network structure on the outcome of the different methods.

What's wrong with hazard-ranking systems? An expository note.

PubMed

Cox, Louis Anthony Tony

2009-07-01

Two commonly recommended principles for allocating risk management resources to remediate uncertain hazards are: (1) select a subset to maximize risk-reduction benefits (e.g., maximize the von Neumann-Morgenstern expected utility of the selected risk-reducing activities), and (2) assign priorities to risk-reducing opportunities and then select activities from the top of the priority list down until no more can be afforded. When different activities create uncertain but correlated risk reductions, as is often the case in practice, then these principles are inconsistent: priority scoring and ranking fails to maximize risk-reduction benefits. Real-world risk priority scoring systems used in homeland security and terrorism risk assessment, environmental risk management, information system vulnerability rating, business risk matrices, and many other important applications do not exploit correlations among risk-reducing opportunities or optimally diversify risk-reducing investments. As a result, they generally make suboptimal risk management recommendations. Applying portfolio optimization methods instead of risk prioritization ranking, rating, or scoring methods can achieve greater risk-reduction value for resources spent.

Functioning in the Real World: Using Storytelling to Improve Validity in the Assessment of Executive Functions.

PubMed

Annotti, Lee A; Teglasi, Hedwig

2017-01-01

Real-world contexts differ in the clarity of expectations for desired responses, as do assessment procedures, ranging along a continuum from maximal conditions that provide well-defined expectations to typical conditions that provide ill-defined expectations. Executive functions guide effective social interactions, but relations between them have not been studied with measures that are matched in the clarity of response expectations. In predicting teacher-rated social competence (SC) from kindergarteners' performance on tasks of executive functions (EFs), we found better model-data fit indexes when both measures were similar in the clarity of response expectations for the child. The maximal EF measure, the Developmental Neuropsychological Assessment, presents well-defined response expectations, and the typical EF measure, 5 scales from the Thematic Apperception Test (TAT), presents ill-defined response expectations (i.e., Abstraction, Perceptual Integration, Cognitive-Experiential Integration, and Associative Thinking). To assess SC under maximal and typical conditions, we used 2 teacher-rated questionnaires, with items, respectively, that emphasize well-defined and ill-defined expectations: the Behavior Rating Inventory: Behavioral Regulation Index and the Social Skills Improvement System: Social Competence Scale. Findings suggest that matching clarity of expectations improves generalization across measures and highlight the usefulness of the TAT to measure EF.

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation.

PubMed

Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

2013-04-01

Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers.

Detection of delamination defects in CFRP materials using ultrasonic signal processing.

PubMed

Benammar, Abdessalem; Drai, Redouane; Guessoum, Abderrezak

2008-12-01

In this paper, signal processing techniques are tested for their ability to resolve echoes associated with delaminations in carbon fiber-reinforced polymer multi-layered composite materials (CFRP) detected by ultrasonic methods. These methods include split spectrum processing (SSP) and the expectation-maximization (EM) algorithm. A simulation study on defect detection was performed, and results were validated experimentally on CFRP with and without delamination defects taken from aircraft. Comparison of the methods for their ability to resolve echoes are made.

A Bayesian approach for incorporating economic factors in sample size design for clinical trials of individual drugs and portfolios of drugs.

PubMed

Patel, Nitin R; Ankolekar, Suresh

2007-11-30

Classical approaches to clinical trial design ignore economic factors that determine economic viability of a new drug. We address the choice of sample size in Phase III trials as a decision theory problem using a hybrid approach that takes a Bayesian view from the perspective of a drug company and a classical Neyman-Pearson view from the perspective of regulatory authorities. We incorporate relevant economic factors in the analysis to determine the optimal sample size to maximize the expected profit for the company. We extend the analysis to account for risk by using a 'satisficing' objective function that maximizes the chance of meeting a management-specified target level of profit. We extend the models for single drugs to a portfolio of clinical trials and optimize the sample sizes to maximize the expected profit subject to budget constraints. Further, we address the portfolio risk and optimize the sample sizes to maximize the probability of achieving a given target of expected profit.

Parameter Estimation of Multiple Frequency-Hopping Signals with Two Sensors

PubMed Central

Pan, Jin; Ma, Boyuan

2018-01-01

This paper essentially focuses on parameter estimation of multiple wideband emitting sources with time-varying frequencies, such as two-dimensional (2-D) direction of arrival (DOA) and signal sorting, with a low-cost circular synthetic array (CSA) consisting of only two rotating sensors. Our basic idea is to decompose the received data, which is a superimposition of phase measurements from multiple sources into separated groups and separately estimate the DOA associated with each source. Motivated by joint parameter estimation, we propose to adopt the expectation maximization (EM) algorithm in this paper; our method involves two steps, namely, the expectation-step (E-step) and the maximization (M-step). In the E-step, the correspondence of each signal with its emitting source is found. Then, in the M-step, the maximum-likelihood (ML) estimates of the DOA parameters are obtained. These two steps are iteratively and alternatively executed to jointly determine the DOAs and sort multiple signals. Closed-form DOA estimation formulae are developed by ML estimation based on phase data, which also realize an optimal estimation. Directional ambiguity is also addressed by another ML estimation method based on received complex responses. The Cramer-Rao lower bound is derived for understanding the estimation accuracy and performance comparison. The verification of the proposed method is demonstrated with simulations. PMID:29617323

Optimization in the utility maximization framework for conservation planning: a comparison of solution procedures in a study of multifunctional agriculture

PubMed Central

Stoms, David M.; Davis, Frank W.

2014-01-01

Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management. PMID:25538868

Optimization in the utility maximization framework for conservation planning: a comparison of solution procedures in a study of multifunctional agriculture

USGS Publications Warehouse

Kreitler, Jason R.; Stoms, David M.; Davis, Frank W.

2014-01-01

Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management.

Self-Averaging Property of Minimal Investment Risk of Mean-Variance Model.

PubMed

Shinzato, Takashi

2015-01-01

In portfolio optimization problems, the minimum expected investment risk is not always smaller than the expected minimal investment risk. That is, using a well-known approach from operations research, it is possible to derive a strategy that minimizes the expected investment risk, but this strategy does not always result in the best rate of return on assets. Prior to making investment decisions, it is important to an investor to know the potential minimal investment risk (or the expected minimal investment risk) and to determine the strategy that will maximize the return on assets. We use the self-averaging property to analyze the potential minimal investment risk and the concentrated investment level for the strategy that gives the best rate of return. We compare the results from our method with the results obtained by the operations research approach and with those obtained by a numerical simulation using the optimal portfolio. The results of our method and the numerical simulation are in agreement, but they differ from that of the operations research approach.

Forecasting continuously increasing life expectancy: what implications?

PubMed

Le Bourg, Eric

2012-04-01

It has been proposed that life expectancy could linearly increase in the next decades and that median longevity of the youngest birth cohorts could reach 105 years or more. These forecasts have been criticized but it seems that their implications for future maximal lifespan (i.e. the lifespan of the last survivors) have not been considered. These implications make these forecasts untenable and it is less risky to hypothesize that life expectancy and maximal lifespan will reach an asymptotic limit in some decades from now. Copyright © 2012 Elsevier B.V. All rights reserved.

Multiway spectral community detection in networks

NASA Astrophysics Data System (ADS)

Zhang, Xiao; Newman, M. E. J.

2015-11-01

One of the most widely used methods for community detection in networks is the maximization of the quality function known as modularity. Of the many maximization techniques that have been used in this context, some of the most conceptually attractive are the spectral methods, which are based on the eigenvectors of the modularity matrix. Spectral algorithms have, however, been limited, by and large, to the division of networks into only two or three communities, with divisions into more than three being achieved by repeated two-way division. Here we present a spectral algorithm that can directly divide a network into any number of communities. The algorithm makes use of a mapping from modularity maximization to a vector partitioning problem, combined with a fast heuristic for vector partitioning. We compare the performance of this spectral algorithm with previous approaches and find it to give superior results, particularly in cases where community sizes are unbalanced. We also give demonstrative applications of the algorithm to two real-world networks and find that it produces results in good agreement with expectations for the networks studied.

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

PubMed Central

Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

2013-01-01

Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers. PMID:24098863

Network Modeling and Energy-Efficiency Optimization for Advanced Machine-to-Machine Sensor Networks

PubMed Central

Jung, Sungmo; Kim, Jong Hyun; Kim, Seoksoo

2012-01-01

Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods. PMID:23202190

Decision Making Analysis: Critical Factors-Based Methodology

DTIC Science & Technology

2010-04-01

the pitfalls associated with current wargaming methods such as assuming a western view of rational values in decision - making regardless of the cultures...Utilization theory slightly expands the rational decision making model as it states that “actors try to maximize their expected utility by weighing the...items to categorize the decision - making behavior of political leaders which tend to demonstrate either a rational or cognitive leaning. Leaders

Volume versus value maximization illustrated for Douglas-fir with thinning

Treesearch

Kurt H. Riitters; J. Douglas Brodie; Chiang Kao

1982-01-01

Economic and physical criteria for selecting even-aged rotation lengths are reviewed with examples of their optimizations. To demonstrate the trade-off between physical volume, economic return, and stand diameter, examples of thinning regimes for maximizing volume, forest rent, and soil expectation are compared with an example of maximizing volume without thinning. The...

Generalized expectation-maximization segmentation of brain MR images

NASA Astrophysics Data System (ADS)

Devalkeneer, Arnaud A.; Robe, Pierre A.; Verly, Jacques G.; Phillips, Christophe L. M.

2006-03-01

Manual segmentation of medical images is unpractical because it is time consuming, not reproducible, and prone to human error. It is also very difficult to take into account the 3D nature of the images. Thus, semi- or fully-automatic methods are of great interest. Current segmentation algorithms based on an Expectation- Maximization (EM) procedure present some limitations. The algorithm by Ashburner et al., 2005, does not allow multichannel inputs, e.g. two MR images of different contrast, and does not use spatial constraints between adjacent voxels, e.g. Markov random field (MRF) constraints. The solution of Van Leemput et al., 1999, employs a simplified model (mixture coefficients are not estimated and only one Gaussian is used by tissue class, with three for the image background). We have thus implemented an algorithm that combines the features of these two approaches: multichannel inputs, intensity bias correction, multi-Gaussian histogram model, and Markov random field (MRF) constraints. Our proposed method classifies tissues in three iterative main stages by way of a Generalized-EM (GEM) algorithm: (1) estimation of the Gaussian parameters modeling the histogram of the images, (2) correction of image intensity non-uniformity, and (3) modification of prior classification knowledge by MRF techniques. The goal of the GEM algorithm is to maximize the log-likelihood across the classes and voxels. Our segmentation algorithm was validated on synthetic data (with the Dice metric criterion) and real data (by a neurosurgeon) and compared to the original algorithms by Ashburner et al. and Van Leemput et al. Our combined approach leads to more robust and accurate segmentation.

Reconstruction of electrical impedance tomography (EIT) images based on the expectation maximum (EM) method.

PubMed

Wang, Qi; Wang, Huaxiang; Cui, Ziqiang; Yang, Chengyi

2012-11-01

Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Noise-enhanced clustering and competitive learning algorithms.

PubMed

Osoba, Osonde; Kosko, Bart

2013-01-01

Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning. Copyright © 2012 Elsevier Ltd. All rights reserved.

An Expectation-Maximization Algorithm for Amplitude Estimation of Saturated Optical Transient Signals.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kagie, Matthew J.; Lanterman, Aaron D.

2017-12-01

This paper addresses parameter estimation for an optical transient signal when the received data has been right-censored. We develop an expectation-maximization (EM) algorithm to estimate the amplitude of a Poisson intensity with a known shape in the presence of additive background counts, where the measurements are subject to saturation effects. We compare the results of our algorithm with those of an EM algorithm that is unaware of the censoring.

PEM-PCA: a parallel expectation-maximization PCA face recognition architecture.

PubMed

Rujirakul, Kanokmon; So-In, Chakchai; Arnonkijpanich, Banchar

2014-01-01

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages' complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

Choices in recreational water quality monitoring: new opportunities and health risk trade-offs.

PubMed

Nevers, Meredith B; Byappanahalli, Muruleedhara N; Whitman, Richard L

2013-04-02

With the recent release of new recreational water quality monitoring criteria, there are more options for regulatory agencies seeking to protect beachgoers from waterborne pathogens. Included are methods that can reduce analytical time, providing timelier estimates of water quality, but the application of these methods has not been examined at most beaches for expectation of health risk and management decisions. In this analysis, we explore health and monitoring outcomes expected at Lake Michigan beaches using protocols for indicator bacteria including culturable Escherichia coli (E. coli; EC), culturable enterococci (ENT), and enterococci as analyzed by qPCR (QENT). Correlations between method results were generally high, except at beaches with historically high concentrations of EC. The "beach action value" was exceeded most often when using EC or ENT as the target indicator; QENT exceeded the limit far less frequently. Measured water quality between years was varied. Although methods with equivalent health expectation have been established, the lack of relationship among method outcomes and annual changes in mean indicator bacteria concentrations complicates the decision-making process. The monitoring approach selected by beach managers may be a combination of available tools that maximizes timely health protection, cost efficiency, and collaboration among beach jurisdictions.

Choices in recreational water quality monitoring: new opportunities and health risk trade-offs

USGS Publications Warehouse

Nevers, Meredith B.; Byappanahalli, Muruleedhara N.; Whitman, Richard L.

2013-01-01

With the recent release of new recreational water quality monitoring criteria, there are more options for regulatory agencies seeking to protect beachgoers from waterborne pathogens. Included are methods that can reduce analytical time, providing timelier estimates of water quality, but the application of these methods has not been examined at most beaches for expectation of health risk and management decisions. In this analysis, we explore health and monitoring outcomes expected at Lake Michigan beaches using protocols for indicator bacteria including culturable Escherichia coli (E. coli; EC), culturable enterococci (ENT), and enterococci as analyzed by qPCR (QENT). Correlations between method results were generally high, except at beaches with historically high concentrations of EC. The “beach action value” was exceeded most often when using EC or ENT as the target indicator; QENT exceeded the limit far less frequently. Measured water quality between years was varied. Although methods with equivalent health expectation have been established, the lack of relationship among method outcomes and annual changes in mean indicator bacteria concentrations complicates the decision-making process. The monitoring approach selected by beach managers may be a combination of available tools that maximizes timely health protection, cost efficiency, and collaboration among beach jurisdictions.

A Gap-Filling Procedure for Hydrologic Data Based on Kalman Filtering and Expectation Maximization: Application to Data from the Wireless Sensor Networks of the Sierra Nevada

NASA Astrophysics Data System (ADS)

Coogan, A.; Avanzi, F.; Akella, R.; Conklin, M. H.; Bales, R. C.; Glaser, S. D.

2017-12-01

Automatic meteorological and snow stations provide large amounts of information at dense temporal resolution, but data quality is often compromised by noise and missing values. We present a new gap-filling and cleaning procedure for networks of these stations based on Kalman filtering and expectation maximization. Our method utilizes a multi-sensor, regime-switching Kalman filter to learn a latent process that captures dependencies between nearby stations and handles sharp changes in snowfall rate. Since the latent process is inferred using observations across working stations in the network, it can be used to fill in large data gaps for a malfunctioning station. The procedure was tested on meteorological and snow data from Wireless Sensor Networks (WSN) in the American River basin of the Sierra Nevada. Data include air temperature, relative humidity, and snow depth from dense networks of 10 to 12 stations within 1 km2 swaths. Both wet and dry water years have similar data issues. Data with artificially created gaps was used to quantify the method's performance. Our multi-sensor approach performs better than a single-sensor one, especially with large data gaps, as it learns and exploits the dominant underlying processes in snowpack at each site.

Optimal joint detection and estimation that maximizes ROC-type curves

PubMed Central

Wunderlich, Adam; Goossens, Bart; Abbey, Craig K.

2017-01-01

Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown, potentially-multiple, number of signals in each observation. PMID:27093544

Optimal Joint Detection and Estimation That Maximizes ROC-Type Curves.

PubMed

Wunderlich, Adam; Goossens, Bart; Abbey, Craig K

2016-09-01

Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown, potentially-multiple, number of signals in each observation.

Eliciting end-user expectations to guide the implementation process of a new electronic health record: A case study using concept mapping.

PubMed

Joukes, Erik; Cornet, Ronald; de Bruijne, Martine C; de Keizer, Nicolette F

2016-03-01

To evaluate the usability of concept mapping to elicit the expectations of healthcare professionals regarding the implementation of a new electronic health record (EHR). These expectations need to be taken into account during the implementation process to maximize the chance of success of the EHR. Two university hospitals in Amsterdam, The Netherlands, in the preparation phase of jointly implementing a new EHR. During this study the hospitals had different methods of documenting patient information (legacy EHR vs. paper-based records). Concept mapping was used to determine and classify the expectations of healthcare professionals regarding the implementation of a new EHR. A multidisciplinary group of 46 healthcare professionals from both university hospitals participated in this study. Expectations were elicited in focus groups, their relevance and feasibility were assessed through a web-questionnaire. Nonmetric multidimensional scaling and clustering methods were used to identify clusters of expectations. We found nine clusters of expectations, each covering an important topic to enable the healthcare professionals to work properly with the new EHR once implemented: usability, data use and reuse, facility conditions, data registration, support, training, internal communication, patients, and collaboration. Average importance and feasibility of each of the clusters was high. Concept mapping is an effective method to find topics that, according to healthcare professionals, are important to consider during the implementation of a new EHR. The method helps to combine the input of a large group of stakeholders at limited efforts. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Self-Averaging Property of Minimal Investment Risk of Mean-Variance Model

PubMed Central

Shinzato, Takashi

2015-01-01

In portfolio optimization problems, the minimum expected investment risk is not always smaller than the expected minimal investment risk. That is, using a well-known approach from operations research, it is possible to derive a strategy that minimizes the expected investment risk, but this strategy does not always result in the best rate of return on assets. Prior to making investment decisions, it is important to an investor to know the potential minimal investment risk (or the expected minimal investment risk) and to determine the strategy that will maximize the return on assets. We use the self-averaging property to analyze the potential minimal investment risk and the concentrated investment level for the strategy that gives the best rate of return. We compare the results from our method with the results obtained by the operations research approach and with those obtained by a numerical simulation using the optimal portfolio. The results of our method and the numerical simulation are in agreement, but they differ from that of the operations research approach. PMID:26225761

Robust radio interferometric calibration using the t-distribution

NASA Astrophysics Data System (ADS)

Kazemi, S.; Yatawatta, S.

2013-10-01

A major stage of radio interferometric data processing is calibration or the estimation of systematic errors in the data and the correction for such errors. A stochastic error (noise) model is assumed, and in most cases, this underlying model is assumed to be Gaussian. However, outliers in the data due to interference or due to errors in the sky model would have adverse effects on processing based on a Gaussian noise model. Most of the shortcomings of calibration such as the loss in flux or coherence, and the appearance of spurious sources, could be attributed to the deviations of the underlying noise model. In this paper, we propose to improve the robustness of calibration by using a noise model based on Student's t-distribution. Student's t-noise is a special case of Gaussian noise when the variance is unknown. Unlike Gaussian-noise-model-based calibration, traditional least-squares minimization would not directly extend to a case when we have a Student's t-noise model. Therefore, we use a variant of the expectation-maximization algorithm, called the expectation-conditional maximization either algorithm, when we have a Student's t-noise model and use the Levenberg-Marquardt algorithm in the maximization step. We give simulation results to show the robustness of the proposed calibration method as opposed to traditional Gaussian-noise-model-based calibration, especially in preserving the flux of weaker sources that are not included in the calibration model.

Modeling Adversaries in Counterterrorism Decisions Using Prospect Theory.

PubMed

Merrick, Jason R W; Leclerc, Philip

2016-04-01

Counterterrorism decisions have been an intense area of research in recent years. Both decision analysis and game theory have been used to model such decisions, and more recently approaches have been developed that combine the techniques of the two disciplines. However, each of these approaches assumes that the attacker is maximizing its utility. Experimental research shows that human beings do not make decisions by maximizing expected utility without aid, but instead deviate in specific ways such as loss aversion or likelihood insensitivity. In this article, we modify existing methods for counterterrorism decisions. We keep expected utility as the defender's paradigm to seek for the rational decision, but we use prospect theory to solve for the attacker's decision to descriptively model the attacker's loss aversion and likelihood insensitivity. We study the effects of this approach in a critical decision, whether to screen containers entering the United States for radioactive materials. We find that the defender's optimal decision is sensitive to the attacker's levels of loss aversion and likelihood insensitivity, meaning that understanding such descriptive decision effects is important in making such decisions. © 2014 Society for Risk Analysis.

The indexing ambiguity in serial femtosecond crystallography (SFX) resolved using an expectation maximization algorithm.

PubMed

Liu, Haiguang; Spence, John C H

2014-11-01

Crystallographic auto-indexing algorithms provide crystal orientations and unit-cell parameters and assign Miller indices based on the geometric relations between the Bragg peaks observed in diffraction patterns. However, if the Bravais symmetry is higher than the space-group symmetry, there will be multiple indexing options that are geometrically equivalent, and hence many ways to merge diffraction intensities from protein nanocrystals. Structure factor magnitudes from full reflections are required to resolve this ambiguity but only partial reflections are available from each XFEL shot, which must be merged to obtain full reflections from these 'stills'. To resolve this chicken-and-egg problem, an expectation maximization algorithm is described that iteratively constructs a model from the intensities recorded in the diffraction patterns as the indexing ambiguity is being resolved. The reconstructed model is then used to guide the resolution of the indexing ambiguity as feedback for the next iteration. Using both simulated and experimental data collected at an X-ray laser for photosystem I in the P63 space group (which supports a merohedral twinning indexing ambiguity), the method is validated.

Introducing health gains in location-allocation models: A stochastic model for planning the delivery of long-term care

NASA Astrophysics Data System (ADS)

Cardoso, T.; Oliveira, M. D.; Barbosa-Póvoa, A.; Nickel, S.

2015-05-01

Although the maximization of health is a key objective in health care systems, location-allocation literature has not yet considered this dimension. This study proposes a multi-objective stochastic mathematical programming approach to support the planning of a multi-service network of long-term care (LTC), both in terms of services location and capacity planning. This approach is based on a mixed integer linear programming model with two objectives - the maximization of expected health gains and the minimization of expected costs - with satisficing levels in several dimensions of equity - namely, equity of access, equity of utilization, socioeconomic equity and geographical equity - being imposed as constraints. The augmented ε-constraint method is used to explore the trade-off between these conflicting objectives, with uncertainty in the demand and delivery of care being accounted for. The model is applied to analyze the (re)organization of the LTC network currently operating in the Great Lisbon region in Portugal for the 2014-2016 period. Results show that extending the network of LTC is a cost-effective investment.

Superresolution radar imaging based on fast inverse-free sparse Bayesian learning for multiple measurement vectors

NASA Astrophysics Data System (ADS)

He, Xingyu; Tong, Ningning; Hu, Xiaowei

2018-01-01

Compressive sensing has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. By exploiting the block sparse structure of the target image, sparse solution for multiple measurement vectors (MMV) can be applied in ISAR imaging and a substantial performance improvement can be achieved. As an effective sparse recovery method, sparse Bayesian learning (SBL) for MMV involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size. To address this problem, we develop a fast inverse-free (IF) SBL method for MMV. A relaxed evidence lower bound (ELBO), which is computationally more amiable than the traditional ELBO used by SBL, is obtained by invoking fundamental property for smooth functions. A variational expectation-maximization scheme is then employed to maximize the relaxed ELBO, and a computationally efficient IF-MSBL algorithm is proposed. Numerical results based on simulated and real data show that the proposed method can reconstruct row sparse signal accurately and obtain clear superresolution ISAR images. Moreover, the running time and computational complexity are reduced to a great extent compared with traditional SBL methods.

Reliability analysis based on the losses from failures.

PubMed

Todinov, M T

2006-04-01

The conventional reliability analysis is based on the premise that increasing the reliability of a system will decrease the losses from failures. On the basis of counterexamples, it is demonstrated that this is valid only if all failures are associated with the same losses. In case of failures associated with different losses, a system with larger reliability is not necessarily characterized by smaller losses from failures. Consequently, a theoretical framework and models are proposed for a reliability analysis, linking reliability and the losses from failures. Equations related to the distributions of the potential losses from failure have been derived. It is argued that the classical risk equation only estimates the average value of the potential losses from failure and does not provide insight into the variability associated with the potential losses. Equations have also been derived for determining the potential and the expected losses from failures for nonrepairable and repairable systems with components arranged in series, with arbitrary life distributions. The equations are also valid for systems/components with multiple mutually exclusive failure modes. The expected losses given failure is a linear combination of the expected losses from failure associated with the separate failure modes scaled by the conditional probabilities with which the failure modes initiate failure. On this basis, an efficient method for simplifying complex reliability block diagrams has been developed. Branches of components arranged in series whose failures are mutually exclusive can be reduced to single components with equivalent hazard rate, downtime, and expected costs associated with intervention and repair. A model for estimating the expected losses from early-life failures has also been developed. For a specified time interval, the expected losses from early-life failures are a sum of the products of the expected number of failures in the specified time intervals covering the early-life failures region and the expected losses given failure characterizing the corresponding time intervals. For complex systems whose components are not logically arranged in series, discrete simulation algorithms and software have been created for determining the losses from failures in terms of expected lost production time, cost of intervention, and cost of replacement. Different system topologies are assessed to determine the effect of modifications of the system topology on the expected losses from failures. It is argued that the reliability allocation in a production system should be done to maximize the profit/value associated with the system. Consequently, a method for setting reliability requirements and reliability allocation maximizing the profit by minimizing the total cost has been developed. Reliability allocation that maximizes the profit in case of a system consisting of blocks arranged in series is achieved by determining for each block individually the reliabilities of the components in the block that minimize the sum of the capital, operation costs, and the expected losses from failures. A Monte Carlo simulation based net present value (NPV) cash-flow model has also been proposed, which has significant advantages to cash-flow models based on the expected value of the losses from failures per time interval. Unlike these models, the proposed model has the capability to reveal the variation of the NPV due to different number of failures occurring during a specified time interval (e.g., during one year). The model also permits tracking the impact of the distribution pattern of failure occurrences and the time dependence of the losses from failures.

Very Slow Search and Reach: Failure to Maximize Expected Gain in an Eye-Hand Coordination Task

PubMed Central

Zhang, Hang; Morvan, Camille; Etezad-Heydari, Louis-Alexandre; Maloney, Laurence T.

2012-01-01

We examined an eye-hand coordination task where optimal visual search and hand movement strategies were inter-related. Observers were asked to find and touch a target among five distractors on a touch screen. Their reward for touching the target was reduced by an amount proportional to how long they took to locate and reach to it. Coordinating the eye and the hand appropriately would markedly reduce the search-reach time. Using statistical decision theory we derived the sequence of interrelated eye and hand movements that would maximize expected gain and we predicted how hand movements should change as the eye gathered further information about target location. We recorded human observers' eye movements and hand movements and compared them with the optimal strategy that would have maximized expected gain. We found that most observers failed to adopt the optimal search-reach strategy. We analyze and describe the strategies they did adopt. PMID:23071430

Deterministic annealing for density estimation by multivariate normal mixtures

NASA Astrophysics Data System (ADS)

Kloppenburg, Martin; Tavan, Paul

1997-03-01

An approach to maximum-likelihood density estimation by mixtures of multivariate normal distributions for large high-dimensional data sets is presented. Conventionally that problem is tackled by notoriously unstable expectation-maximization (EM) algorithms. We remove these instabilities by the introduction of soft constraints, enabling deterministic annealing. Our developments are motivated by the proof that algorithmically stable fuzzy clustering methods that are derived from statistical physics analogs are special cases of EM procedures.

Characteristic differences in essential oil composition of six Zanthoxylum bungeanum Maxim. (Rutaceae) cultivars and their biological significance.

PubMed

Liu, Shu-Ming; Wang, Shi-Jun; Song, Si-Yao; Zou, Yong; Wang, Jun-Ru; Sun, Bing-Yin

Great variations have been found in composition and content of the essential oil of Zanthoxylum bungeanum Maxim. (Rutaceae), resulting from various factors such as harvest time, drying and extraction methods (Huang et al., 2006; Shao et al., 2013), solvent and herbal parts used (Zhang, 1996; Cao and Zhang, 2010; Wang et al., 2011). However, in terms of artificial introduction and cultivation, there is little research on the chemical composition of essential oil extracted from Z. bungeanum Maxim. cultivars, which have been introduced from different origins. In this study, the composition and content of essential oil from six cultivars (I-VI) have been investigated. They were introduced and cultivated for 11 years in the same cultivation conditions. Cultivars were as followings: Qin'an (I) cultivar originally introduced from Qin'an City in Gansu Province; Dahongpao A (II) from She County in Hebei Province; Dahongpao B (III) from Fuping County; Dahongpao C (IV) from Tongchuan City; Meifengjiao (V) from Feng County; and, Shizitou (VI) from Hancheng City, in Shaanxi Province, China. This research is expected to provide a theoretical basis for further introduction, cultivation, and commercial development of Z. bungeanum Maxim.

Field use of maximal sprint speed by collared lizards (Crotaphytus collaris): compensation and sexual selection.

PubMed

Husak, Jerry F; Fox, Stanley F

2006-09-01

To understand how selection acts on performance capacity, the ecological role of the performance trait being measured must be determined. Knowing if and when an animal uses maximal performance capacity may give insight into what specific selective pressures may be acting on performance, because individuals are expected to use close to maximal capacity only in contexts important to survival or reproductive success. Furthermore, if an ecological context is important, poor performers are expected to compensate behaviorally. To understand the relative roles of natural and sexual selection on maximal sprint speed capacity we measured maximal sprint speed of collared lizards (Crotaphytus collaris) in the laboratory and field-realized sprint speed for the same individuals in three different contexts (foraging, escaping a predator, and responding to a rival intruder). Females used closer to maximal speed while escaping predators than in the other contexts. Adult males, on the other hand, used closer to maximal speed while responding to an unfamiliar male intruder tethered within their territory. Sprint speeds during foraging attempts were far below maximal capacity for all lizards. Yearlings appeared to compensate for having lower absolute maximal capacity by using a greater percentage of their maximal capacity while foraging and escaping predators than did adults of either sex. We also found evidence for compensation within age and sex classes, where slower individuals used a greater percentage of their maximal capacity than faster individuals. However, this was true only while foraging and escaping predators and not while responding to a rival. Collared lizards appeared to choose microhabitats near refugia such that maximal speed was not necessary to escape predators. Although natural selection for predator avoidance cannot be ruled out as a selective force acting on locomotor performance in collared lizards, intrasexual selection for territory maintenance may be more important for territorial males.

Statistical Learning Theory for High Dimensional Prediction: Application to Criterion-Keyed Scale Development

PubMed Central

Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul

2016-01-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257

Orbit Clustering Based on Transfer Cost

NASA Technical Reports Server (NTRS)

Gustafson, Eric D.; Arrieta-Camacho, Juan J.; Petropoulos, Anastassios E.

2013-01-01

We propose using cluster analysis to perform quick screening for combinatorial global optimization problems. The key missing component currently preventing cluster analysis from use in this context is the lack of a useable metric function that defines the cost to transfer between two orbits. We study several proposed metrics and clustering algorithms, including k-means and the expectation maximization algorithm. We also show that proven heuristic methods such as the Q-law can be modified to work with cluster analysis.

Image segmentation using hidden Markov Gauss mixture models.

PubMed

Pyun, Kyungsuk; Lim, Johan; Won, Chee Sun; Gray, Robert M

2007-07-01

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.

Confronting Diversity in the Community College Classroom: Six Maxims for Good Teaching.

ERIC Educational Resources Information Center

Gillett-Karam, Rosemary

1992-01-01

Emphasizes the leadership role of community college faculty in developing critical teaching strategies focusing attention on the needs of women and minorities. Describes six maxims of teaching excellence: engaging students' desire to learn, increasing opportunities, eliminating obstacles, empowering students through high expectations, offering…

A Bayesian truth serum for subjective data.

PubMed

Prelec, Drazen

2004-10-15

Subjective judgments, an essential information source for science and policy, are problematic because there are no public criteria for assessing judgmental truthfulness. I present a scoring method for eliciting truthful subjective data in situations where objective truth is unknowable. The method assigns high scores not to the most common answers but to the answers that are more common than collectively predicted, with predictions drawn from the same population. This simple adjustment in the scoring criterion removes all bias in favor of consensus: Truthful answers maximize expected score even for respondents who believe that their answer represents a minority view.

Multimodal Hierarchical Dirichlet Process-Based Active Perception by a Robot

PubMed Central

Taniguchi, Tadahiro; Yoshino, Ryo; Takano, Toshiaki

2018-01-01

In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an active perception for MHDP method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback–Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive a Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The numerical experiment using the synthetic data shows that the proposed method can work appropriately even when the number of actions is large and a set of target objects involves objects categorized into multiple classes. The results support our theoretical outcomes. PMID:29872389

Multimodal Hierarchical Dirichlet Process-Based Active Perception by a Robot.

PubMed

Taniguchi, Tadahiro; Yoshino, Ryo; Takano, Toshiaki

2018-01-01

In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an active perception for MHDP method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback-Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive a Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The numerical experiment using the synthetic data shows that the proposed method can work appropriately even when the number of actions is large and a set of target objects involves objects categorized into multiple classes. The results support our theoretical outcomes.

Model-Based Clustering of Regression Time Series Data via APECM -- An AECM Algorithm Sung to an Even Faster Beat

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Wei-Chen; Maitra, Ranjan

2011-01-01

We propose a model-based approach for clustering time series regression data in an unsupervised machine learning framework to identify groups under the assumption that each mixture component follows a Gaussian autoregressive regression model of order p. Given the number of groups, the traditional maximum likelihood approach of estimating the parameters using the expectation-maximization (EM) algorithm can be employed, although it is computationally demanding. The somewhat fast tune to the EM folk song provided by the Alternating Expectation Conditional Maximization (AECM) algorithm can alleviate the problem to some extent. In this article, we develop an alternative partial expectation conditional maximization algorithmmore » (APECM) that uses an additional data augmentation storage step to efficiently implement AECM for finite mixture models. Results on our simulation experiments show improved performance in both fewer numbers of iterations and computation time. The methodology is applied to the problem of clustering mutual funds data on the basis of their average annual per cent returns and in the presence of economic indicators.« less

Load controller and method to enhance effective capacity of a photovoltaic power supply using a dynamically determined expected peak loading

DOEpatents

Perez, Richard

2005-05-03

A load controller and method are provided for maximizing effective capacity of a non-controllable, renewable power supply coupled to a variable electrical load also coupled to a conventional power grid. Effective capacity is enhanced by monitoring power output of the renewable supply and loading, and comparing the loading against the power output and a load adjustment threshold determined from an expected peak loading. A value for a load adjustment parameter is calculated by subtracting the renewable supply output and the load adjustment parameter from the current load. This value is then employed to control the variable load in an amount proportional to the value of the load control parameter when the parameter is within a predefined range. By so controlling the load, the effective capacity of the non-controllable, renewable power supply is increased without any attempt at operational feedback control of the renewable supply.

Estimates of genetic and environmental (co)variances for first lactation on milk yield, survival, and calving interval.

PubMed

Dong, M C; van Vleck, L D

1989-03-01

Variance and covariance components for milk yield, survival to second freshening, calving interval in first lactation were estimated by REML with the expectation and maximization algorithm for an animal model which included herd-year-season effects. Cows without calving interval but with milk yield were included. Each of the four data sets of 15 herds included about 3000 Holstein cows. Relationships across herds were ignored to enable inversion of the coefficient matrix of mixed model equations. Quadratics and their expectations were accumulated herd by herd. Heritability of milk yield (.32) agrees with reports by same methods. Heritabilities of survival (.11) and calving interval(.15) are slightly larger and genetic correlations smaller than results from different methods of estimation. Genetic correlation between milk yield and calving interval (.09) indicates genetic ability to produce more milk is lightly associated with decreased fertility.

Unified anomaly suppression and boundary extraction in laser radar range imagery based on a joint curve-evolution and expectation-maximization algorithm.

PubMed

Feng, Haihua; Karl, William Clem; Castañon, David A

2008-05-01

In this paper, we develop a new unified approach for laser radar range anomaly suppression, range profiling, and segmentation. This approach combines an object-based hybrid scene model for representing the range distribution of the field and a statistical mixture model for the range data measurement noise. The image segmentation problem is formulated as a minimization problem which jointly estimates the target boundary together with the target region range variation and background range variation directly from the noisy and anomaly-filled range data. This formulation allows direct incorporation of prior information concerning the target boundary, target ranges, and background ranges into an optimal reconstruction process. Curve evolution techniques and a generalized expectation-maximization algorithm are jointly employed as an efficient solver for minimizing the objective energy, resulting in a coupled pair of object and intensity optimization tasks. The method directly and optimally extracts the target boundary, avoiding a suboptimal two-step process involving image smoothing followed by boundary extraction. Experiments are presented demonstrating that the proposed approach is robust to anomalous pixels (missing data) and capable of producing accurate estimation of the target boundary and range values from noisy data.

Covariance estimation in Terms of Stokes Parameters with Application to Vector Sensor Imaging

DTIC Science & Technology

2016-12-15

S. Klein, “HF Vector Sensor for Radio Astronomy : Ground Testing Results,” in AIAA SPACE 2016, ser. AIAA SPACE Forum, American Institute of... astronomy ,” in 2016 IEEE Aerospace Conference, Mar. 2016, pp. 1–17. doi: 10.1109/ AERO.2016.7500688. [4] K.-C. Ho, K.-C. Tan, and B. T. G. Tan, “Estimation of...Statistical Imaging in Radio Astronomy via an Expectation-Maximization Algorithm for Structured Covariance Estimation,” in Statistical Methods in Imaging: IN

Mission-Related Execution and Planning Through Quality of Service Methods

DTIC Science & Technology

2010-06-01

which maximizes a mission effectiveness functions is the ideal driver of QoS mechanisms. Service Quality Quality of Service may also exist in other...However, service quality is the originating concept of QoS and is the level of performance which one entity expects from another, including non-IT SoSs... Service quality may also be reflected in the context of a system’s purpose or an organization’s mission. Putting level of service values and

Analyzing Sub-Classifications of Glaucoma via SOM Based Clustering of Optic Nerve Images.

PubMed

Yan, Sanjun; Abidi, Syed Sibte Raza; Artes, Paul Habib

2005-01-01

We present a data mining framework to cluster optic nerve images obtained by Confocal Scanning Laser Tomography (CSLT) in normal subjects and patients with glaucoma. We use self-organizing maps and expectation maximization methods to partition the data into clusters that provide insights into potential sub-classification of glaucoma based on morphological features. We conclude that our approach provides a first step towards a better understanding of morphological features in optic nerve images obtained from glaucoma patients and healthy controls.

Load controller and method to enhance effective capacity of a photovotaic power supply using a dynamically determined expected peak loading

DOEpatents

Perez, Richard

2003-04-01

A load controller and method are provided for maximizing effective capacity of a non-controllable, renewable power supply coupled to a variable electrical load also coupled to a conventional power grid. Effective capacity is enhanced by monitoring power output of the renewable supply and loading, and comparing the loading against the power output and a load adjustment threshold determined from an expected peak loading. A value for a load adjustment parameter is calculated by subtracting the renewable supply output and the load adjustment parameter from the current load. This value is then employed to control the variable load in an amount proportional to the value of the load control parameter when the parameter is within a predefined range. By so controlling the load, the effective capacity of the non-controllable, renewable power supply is increased without any attempt at operational feedback control of the renewable supply. The expected peak loading of the variable load can be dynamically determined within a defined time interval with reference to variations in the variable load.

Multi-ray-based system matrix generation for 3D PET reconstruction

NASA Astrophysics Data System (ADS)

Moehrs, Sascha; Defrise, Michel; Belcari, Nicola; DelGuerra, Alberto; Bartoli, Antonietta; Fabbri, Serena; Zanetti, Gianluigi

2008-12-01

Iterative image reconstruction algorithms for positron emission tomography (PET) require a sophisticated system matrix (model) of the scanner. Our aim is to set up such a model offline for the YAP-(S)PET II small animal imaging tomograph in order to use it subsequently with standard ML-EM (maximum-likelihood expectation maximization) and OSEM (ordered subset expectation maximization) for fully three-dimensional image reconstruction. In general, the system model can be obtained analytically, via measurements or via Monte Carlo simulations. In this paper, we present the multi-ray method, which can be considered as a hybrid method to set up the system model offline. It incorporates accurate analytical (geometric) considerations as well as crystal depth and crystal scatter effects. At the same time, it has the potential to model seamlessly other physical aspects such as the positron range. The proposed method is based on multiple rays which are traced from/to the detector crystals through the image volume. Such a ray-tracing approach itself is not new; however, we derive a novel mathematical formulation of the approach and investigate the positioning of the integration (ray-end) points. First, we study single system matrix entries and show that the positioning and weighting of the ray-end points according to Gaussian integration give better results compared to equally spaced integration points (trapezoidal integration), especially if only a small number of integration points (rays) are used. Additionally, we show that, for a given variance of the single matrix entries, the number of rays (events) required to calculate the whole matrix is a factor of 20 larger when using a pure Monte-Carlo-based method. Finally, we analyse the quality of the model by reconstructing phantom data from the YAP-(S)PET II scanner.

Evidential analysis of difference images for change detection of multitemporal remote sensing images

NASA Astrophysics Data System (ADS)

Chen, Yin; Peng, Lijuan; Cremers, Armin B.

2018-03-01

In this article, we develop two methods for unsupervised change detection in multitemporal remote sensing images based on Dempster-Shafer's theory of evidence (DST). In most unsupervised change detection methods, the probability of difference image is assumed to be characterized by mixture models, whose parameters are estimated by the expectation maximization (EM) method. However, the main drawback of the EM method is that it does not consider spatial contextual information, which may entail rather noisy detection results with numerous spurious alarms. To remedy this, we firstly develop an evidence theory based EM method (EEM) which incorporates spatial contextual information in EM by iteratively fusing the belief assignments of neighboring pixels to the central pixel. Secondly, an evidential labeling method in the sense of maximizing a posteriori probability (MAP) is proposed in order to further enhance the detection result. It first uses the parameters estimated by EEM to initialize the class labels of a difference image. Then it iteratively fuses class conditional information and spatial contextual information, and updates labels and class parameters. Finally it converges to a fixed state which gives the detection result. A simulated image set and two real remote sensing data sets are used to evaluate the two evidential change detection methods. Experimental results show that the new evidential methods are comparable to other prevalent methods in terms of total error rate.

The Self in Decision Making and Decision Implementation.

ERIC Educational Resources Information Center

Beach, Lee Roy; Mitchell, Terence R.

Since the early 1950's the principal prescriptive model in the psychological study of decision making has been maximization of Subjective Expected Utility (SEU). This SEU maximization has come to be regarded as a description of how people go about making decisions. However, while observed decision processes sometimes resemble the SEU model,…

Coding for Parallel Links to Maximize the Expected Value of Decodable Messages

NASA Technical Reports Server (NTRS)

Klimesh, Matthew A.; Chang, Christopher S.

2011-01-01

When multiple parallel communication links are available, it is useful to consider link-utilization strategies that provide tradeoffs between reliability and throughput. Interesting cases arise when there are three or more available links. Under the model considered, the links have known probabilities of being in working order, and each link has a known capacity. The sender has a number of messages to send to the receiver. Each message has a size and a value (i.e., a worth or priority). Messages may be divided into pieces arbitrarily, and the value of each piece is proportional to its size. The goal is to choose combinations of messages to send on the links so that the expected value of the messages decodable by the receiver is maximized. There are three parts to the innovation: (1) Applying coding to parallel links under the model; (2) Linear programming formulation for finding the optimal combinations of messages to send on the links; and (3) Algorithms for assisting in finding feasible combinations of messages, as support for the linear programming formulation. There are similarities between this innovation and methods developed in the field of network coding. However, network coding has generally been concerned with either maximizing throughput in a fixed network, or robust communication of a fixed volume of data. In contrast, under this model, the throughput is expected to vary depending on the state of the network. Examples of error-correcting codes that are useful under this model but which are not needed under previous models have been found. This model can represent either a one-shot communication attempt, or a stream of communications. Under the one-shot model, message sizes and link capacities are quantities of information (e.g., measured in bits), while under the communications stream model, message sizes and link capacities are information rates (e.g., measured in bits/second). This work has the potential to increase the value of data returned from spacecraft under certain conditions.

The impact of temperature changes on vector-borne disease transmission: Culicoides midges and bluetongue virus.

PubMed

Brand, Samuel P C; Keeling, Matt J

2017-03-01

It is a long recognized fact that climatic variations, especially temperature, affect the life history of biting insects. This is particularly important when considering vector-borne diseases, especially in temperate regions where climatic fluctuations are large. In general, it has been found that most biological processes occur at a faster rate at higher temperatures, although not all processes change in the same manner. This differential response to temperature, often considered as a trade-off between onward transmission and vector life expectancy, leads to the total transmission potential of an infected vector being maximized at intermediate temperatures. Here we go beyond the concept of a static optimal temperature, and mathematically model how realistic temperature variation impacts transmission dynamics. We use bluetongue virus (BTV), under UK temperatures and transmitted by Culicoides midges, as a well-studied example where temperature fluctuations play a major role. We first consider an optimal temperature profile that maximizes transmission, and show that this is characterized by a warm day to maximize biting followed by cooler weather to maximize vector life expectancy. This understanding can then be related to recorded representative temperature patterns for England, the UK region which has experienced BTV cases, allowing us to infer historical transmissibility of BTV, as well as using forecasts of climate change to predict future transmissibility. Our results show that when BTV first invaded northern Europe in 2006 the cumulative transmission intensity was higher than any point in the last 50 years, although with climate change such high risks are the expected norm by 2050. Such predictions would indicate that regular BTV epizootics should be expected in the UK in the future. © 2017 The Author(s).

On the role of budget sufficiency, cost efficiency, and uncertainty in species management

USGS Publications Warehouse

van der Burg, Max Post; Bly, Bartholomew B.; Vercauteren, Tammy; Grand, James B.; Tyre, Andrew J.

2014-01-01

Many conservation planning frameworks rely on the assumption that one should prioritize locations for management actions based on the highest predicted conservation value (i.e., abundance, occupancy). This strategy may underperform relative to the expected outcome if one is working with a limited budget or the predicted responses are uncertain. Yet, cost and tolerance to uncertainty rarely become part of species management plans. We used field data and predictive models to simulate a decision problem involving western burrowing owls (Athene cunicularia hypugaea) using prairie dog colonies (Cynomys ludovicianus) in western Nebraska. We considered 2 species management strategies: one maximized abundance and the other maximized abundance in a cost-efficient way. We then used heuristic decision algorithms to compare the 2 strategies in terms of how well they met a hypothetical conservation objective. Finally, we performed an info-gap decision analysis to determine how these strategies performed under different budget constraints and uncertainty about owl response. Our results suggested that when budgets were sufficient to manage all sites, the maximizing strategy was optimal and suggested investing more in expensive actions. This pattern persisted for restricted budgets up to approximately 50% of the sufficient budget. Below this budget, the cost-efficient strategy was optimal and suggested investing in cheaper actions. When uncertainty in the expected responses was introduced, the strategy that maximized abundance remained robust under a sufficient budget. Reducing the budget induced a slight trade-off between expected performance and robustness, which suggested that the most robust strategy depended both on one's budget and tolerance to uncertainty. Our results suggest that wildlife managers should explicitly account for budget limitations and be realistic about their expected levels of performance.

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

PubMed

Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R

2016-12-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Learning about Expectation Violation from Prediction Error Paradigms – A Meta-Analysis on Brain Processes Following a Prediction Error

PubMed Central

D’Astolfo, Lisa; Rief, Winfried

2017-01-01

Modifying patients’ expectations by exposing them to expectation violation situations (thus maximizing the difference between the expected and the actual situational outcome) is proposed to be a crucial mechanism for therapeutic success for a variety of different mental disorders. However, clinical observations suggest that patients often maintain their expectations regardless of experiences contradicting their expectations. It remains unclear which information processing mechanisms lead to modification or persistence of patients’ expectations. Insight in the processing could be provided by Neuroimaging studies investigating prediction error (PE, i.e., neuronal reactions to non-expected stimuli). Two methods are often used to investigate the PE: (1) paradigms, in which participants passively observe PEs (”passive” paradigms) and (2) paradigms, which encourage a behavioral adaptation following a PE (“active” paradigms). These paradigms are similar to the methods used to induce expectation violations in clinical settings: (1) the confrontation with an expectation violation situation and (2) an enhanced confrontation in which the patient actively challenges his expectation. We used this similarity to gain insight in the different neuronal processing of the two PE paradigms. We performed a meta-analysis contrasting neuronal activity of PE paradigms encouraging a behavioral adaptation following a PE and paradigms enforcing passiveness following a PE. We found more neuronal activity in the striatum, the insula and the fusiform gyrus in studies encouraging behavioral adaptation following a PE. Due to the involvement of reward assessment and avoidance learning associated with the striatum and the insula we propose that the deliberate execution of action alternatives following a PE is associated with the integration of new information into previously existing expectations, therefore leading to an expectation change. While further research is needed to directly assess expectations of participants, this study provides new insights into the information processing mechanisms following an expectation violation. PMID:28804467

Learning about Expectation Violation from Prediction Error Paradigms - A Meta-Analysis on Brain Processes Following a Prediction Error.

PubMed

D'Astolfo, Lisa; Rief, Winfried

2017-01-01

Modifying patients' expectations by exposing them to expectation violation situations (thus maximizing the difference between the expected and the actual situational outcome) is proposed to be a crucial mechanism for therapeutic success for a variety of different mental disorders. However, clinical observations suggest that patients often maintain their expectations regardless of experiences contradicting their expectations. It remains unclear which information processing mechanisms lead to modification or persistence of patients' expectations. Insight in the processing could be provided by Neuroimaging studies investigating prediction error (PE, i.e., neuronal reactions to non-expected stimuli). Two methods are often used to investigate the PE: (1) paradigms, in which participants passively observe PEs ("passive" paradigms) and (2) paradigms, which encourage a behavioral adaptation following a PE ("active" paradigms). These paradigms are similar to the methods used to induce expectation violations in clinical settings: (1) the confrontation with an expectation violation situation and (2) an enhanced confrontation in which the patient actively challenges his expectation. We used this similarity to gain insight in the different neuronal processing of the two PE paradigms. We performed a meta-analysis contrasting neuronal activity of PE paradigms encouraging a behavioral adaptation following a PE and paradigms enforcing passiveness following a PE. We found more neuronal activity in the striatum, the insula and the fusiform gyrus in studies encouraging behavioral adaptation following a PE. Due to the involvement of reward assessment and avoidance learning associated with the striatum and the insula we propose that the deliberate execution of action alternatives following a PE is associated with the integration of new information into previously existing expectations, therefore leading to an expectation change. While further research is needed to directly assess expectations of participants, this study provides new insights into the information processing mechanisms following an expectation violation.

Radiation detection method and system using the sequential probability ratio test

DOEpatents

Nelson, Karl E [Livermore, CA; Valentine, John D [Redwood City, CA; Beauchamp, Brock R [San Ramon, CA

2007-07-17

A method and system using the Sequential Probability Ratio Test to enhance the detection of an elevated level of radiation, by determining whether a set of observations are consistent with a specified model within a given bounds of statistical significance. In particular, the SPRT is used in the present invention to maximize the range of detection, by providing processing mechanisms for estimating the dynamic background radiation, adjusting the models to reflect the amount of background knowledge at the current point in time, analyzing the current sample using the models to determine statistical significance, and determining when the sample has returned to the expected background conditions.

Blood detection in wireless capsule endoscopy using expectation maximization clustering

NASA Astrophysics Data System (ADS)

Hwang, Sae; Oh, JungHwan; Cox, Jay; Tang, Shou Jiang; Tibbals, Harry F.

2006-03-01

Wireless Capsule Endoscopy (WCE) is a relatively new technology (FDA approved in 2002) allowing doctors to view most of the small intestine. Other endoscopies such as colonoscopy, upper gastrointestinal endoscopy, push enteroscopy, and intraoperative enteroscopy could be used to visualize up to the stomach, duodenum, colon, and terminal ileum, but there existed no method to view most of the small intestine without surgery. With the miniaturization of wireless and camera technologies came the ability to view the entire gestational track with little effort. A tiny disposable video capsule is swallowed, transmitting two images per second to a small data receiver worn by the patient on a belt. During an approximately 8-hour course, over 55,000 images are recorded to a worn device and then downloaded to a computer for later examination. Typically, a medical clinician spends more than two hours to analyze a WCE video. Research has been attempted to automatically find abnormal regions (especially bleeding) to reduce the time needed to analyze the videos. The manufacturers also provide the software tool to detect the bleeding called Suspected Blood Indicator (SBI), but its accuracy is not high enough to replace human examination. It was reported that the sensitivity and the specificity of SBI were about 72% and 85%, respectively. To address this problem, we propose a technique to detect the bleeding regions automatically utilizing the Expectation Maximization (EM) clustering algorithm. Our experimental results indicate that the proposed bleeding detection method achieves 92% and 98% of sensitivity and specificity, respectively.

Interval-based reconstruction for uncertainty quantification in PET

NASA Astrophysics Data System (ADS)

Kucharczak, Florentin; Loquin, Kevin; Buvat, Irène; Strauss, Olivier; Mariano-Goulart, Denis

2018-02-01

A new directed interval-based tomographic reconstruction algorithm, called non-additive interval based expectation maximization (NIBEM) is presented. It uses non-additive modeling of the forward operator that provides intervals instead of single-valued projections. The detailed approach is an extension of the maximum likelihood—expectation maximization algorithm based on intervals. The main motivation for this extension is that the resulting intervals have appealing properties for estimating the statistical uncertainty associated with the reconstructed activity values. After reviewing previously published theoretical concepts related to interval-based projectors, this paper describes the NIBEM algorithm and gives examples that highlight the properties and advantages of this interval valued reconstruction.

The benefits of social influence in optimized cultural markets.

PubMed

Abeliuk, Andrés; Berbeglia, Gerardo; Cebrian, Manuel; Van Hentenryck, Pascal

2015-01-01

Social influence has been shown to create significant unpredictability in cultural markets, providing one potential explanation why experts routinely fail at predicting commercial success of cultural products. As a result, social influence is often presented in a negative light. Here, we show the benefits of social influence for cultural markets. We present a policy that uses product quality, appeal, position bias and social influence to maximize expected profits in the market. Our computational experiments show that our profit-maximizing policy leverages social influence to produce significant performance benefits for the market, while our theoretical analysis proves that our policy outperforms in expectation any policy not displaying social signals. Our results contrast with earlier work which focused on showing the unpredictability and inequalities created by social influence. Not only do we show for the first time that, under our policy, dynamically showing consumers positive social signals increases the expected profit of the seller in cultural markets. We also show that, in reasonable settings, our profit-maximizing policy does not introduce significant unpredictability and identifies "blockbusters". Overall, these results shed new light on the nature of social influence and how it can be leveraged for the benefits of the market.

Optimal Investment Under Transaction Costs: A Threshold Rebalanced Portfolio Approach

NASA Astrophysics Data System (ADS)

Tunc, Sait; Donmez, Mehmet Ali; Kozat, Suleyman Serdar

2013-06-01

We study optimal investment in a financial market having a finite number of assets from a signal processing perspective. We investigate how an investor should distribute capital over these assets and when he should reallocate the distribution of the funds over these assets to maximize the cumulative wealth over any investment period. In particular, we introduce a portfolio selection algorithm that maximizes the expected cumulative wealth in i.i.d. two-asset discrete-time markets where the market levies proportional transaction costs in buying and selling stocks. We achieve this using "threshold rebalanced portfolios", where trading occurs only if the portfolio breaches certain thresholds. Under the assumption that the relative price sequences have log-normal distribution from the Black-Scholes model, we evaluate the expected wealth under proportional transaction costs and find the threshold rebalanced portfolio that achieves the maximal expected cumulative wealth over any investment period. Our derivations can be readily extended to markets having more than two stocks, where these extensions are pointed out in the paper. As predicted from our derivations, we significantly improve the achieved wealth over portfolio selection algorithms from the literature on historical data sets.

Matching Pupils and Teachers to Maximize Expected Outcomes.

ERIC Educational Resources Information Center

Ward, Joe H., Jr.; And Others

To achieve a good teacher-pupil match, it is necessary (1) to predict the learning outcomes that will result when each student is instructed by each teacher, (2) to use the predicted performance to compute an Optimality Index for each teacher-pupil combination to indicate the quality of each combination toward maximizing learning for all students,…

Globally optimal trial design for local decision making.

PubMed

Eckermann, Simon; Willan, Andrew R

2009-02-01

Value of information methods allows decision makers to identify efficient trial design following a principle of maximizing the expected value to decision makers of information from potential trial designs relative to their expected cost. However, in health technology assessment (HTA) the restrictive assumption has been made that, prospectively, there is only expected value of sample information from research commissioned within jurisdiction. This paper extends the framework for optimal trial design and decision making within jurisdiction to allow for optimal trial design across jurisdictions. This is illustrated in identifying an optimal trial design for decision making across the US, the UK and Australia for early versus late external cephalic version for pregnant women presenting in the breech position. The expected net gain from locally optimal trial designs of US$0.72M is shown to increase to US$1.14M with a globally optimal trial design. In general, the proposed method of globally optimal trial design improves on optimal trial design within jurisdictions by: (i) reflecting the global value of non-rival information; (ii) allowing optimal allocation of trial sample across jurisdictions; (iii) avoiding market failure associated with free-rider effects, sub-optimal spreading of fixed costs and heterogeneity of trial information with multiple trials. Copyright (c) 2008 John Wiley & Sons, Ltd.

Towards Robust Designs Via Multiple-Objective Optimization Methods

NASA Technical Reports Server (NTRS)

Man Mohan, Rai

2006-01-01

Fabricating and operating complex systems involves dealing with uncertainty in the relevant variables. In the case of aircraft, flow conditions are subject to change during operation. Efficiency and engine noise may be different from the expected values because of manufacturing tolerances and normal wear and tear. Engine components may have a shorter life than expected because of manufacturing tolerances. In spite of the important effect of operating- and manufacturing-uncertainty on the performance and expected life of the component or system, traditional aerodynamic shape optimization has focused on obtaining the best design given a set of deterministic flow conditions. Clearly it is important to both maintain near-optimal performance levels at off-design operating conditions, and, ensure that performance does not degrade appreciably when the component shape differs from the optimal shape due to manufacturing tolerances and normal wear and tear. These requirements naturally lead to the idea of robust optimal design wherein the concept of robustness to various perturbations is built into the design optimization procedure. The basic ideas involved in robust optimal design will be included in this lecture. The imposition of the additional requirement of robustness results in a multiple-objective optimization problem requiring appropriate solution procedures. Typically the costs associated with multiple-objective optimization are substantial. Therefore efficient multiple-objective optimization procedures are crucial to the rapid deployment of the principles of robust design in industry. Hence the companion set of lecture notes (Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks ) deals with methodology for solving multiple-objective Optimization problems efficiently, reliably and with little user intervention. Applications of the methodologies presented in the companion lecture to robust design will be included here. The evolutionary method (DE) is first used to solve a relatively difficult problem in extended surface heat transfer wherein optimal fin geometries are obtained for different safe operating base temperatures. The objective of maximizing the safe operating base temperature range is in direct conflict with the objective of maximizing fin heat transfer. This problem is a good example of achieving robustness in the context of changing operating conditions. The evolutionary method is then used to design a turbine airfoil; the two objectives being reduced sensitivity of the pressure distribution to small changes in the airfoil shape and the maximization of the trailing edge wedge angle with the consequent increase in airfoil thickness and strength. This is a relevant example of achieving robustness to manufacturing tolerances and wear and tear in the presence of other objectives.

Influence of taekwondo as security martial arts training on anaerobic threshold, cardiorespiratory fitness, and blood lactate recovery.

PubMed

Kim, Dae-Young; Seo, Byoung-Do; Choi, Pan-Am

2014-04-01

[Purpose] This study was conducted to determine the influence of Taekwondo as security martial arts training on anaerobic threshold, cardiorespiratory fitness, and blood lactate recovery. [Subjects and Methods] Fourteen healthy university students were recruited and divided into an exercise group and a control group (n = 7 in each group). The subjects who participated in the experiment were subjected to an exercise loading test in which anaerobic threshold, value of ventilation, oxygen uptake, maximal oxygen uptake, heart rate, and maximal values of ventilation / heart rate were measured during the exercise, immediately after maximum exercise loading, and at 1, 3, 5, 10, and 15 min of recovery. [Results] At the anaerobic threshold time point, the exercise group showed a significantly longer time to reach anaerobic threshold. The exercise group showed significantly higher values for the time to reach VO2max, maximal values of ventilation, maximal oxygen uptake and maximal values of ventilation / heart rate. Significant changes were observed in the value of ventilation volumes at the 1- and 5-min recovery time points within the exercise group; oxygen uptake and maximal oxygen uptake were significantly different at the 5- and 10-min time points; heart rate was significantly different at the 1- and 3-min time points; and maximal values of ventilation / heart rate was significantly different at the 5-min time point. The exercise group showed significant decreases in blood lactate levels at the 15- and 30-min recovery time points. [Conclusion] The study results revealed that Taekwondo as a security martial arts training increases the maximal oxygen uptake and anaerobic threshold and accelerates an individual's recovery to the normal state of cardiorespiratory fitness and blood lactate level. These results are expected to contribute to the execution of more effective security services in emergencies in which violence can occur.

Value of information analysis optimizing future trial design from a pilot study on catheter securement devices.

PubMed

Tuffaha, Haitham W; Reynolds, Heather; Gordon, Louisa G; Rickard, Claire M; Scuffham, Paul A

2014-12-01

Value of information analysis has been proposed as an alternative to the standard hypothesis testing approach, which is based on type I and type II errors, in determining sample sizes for randomized clinical trials. However, in addition to sample size calculation, value of information analysis can optimize other aspects of research design such as possible comparator arms and alternative follow-up times, by considering trial designs that maximize the expected net benefit of research, which is the difference between the expected cost of the trial and the expected value of additional information. To apply value of information methods to the results of a pilot study on catheter securement devices to determine the optimal design of a future larger clinical trial. An economic evaluation was performed using data from a multi-arm randomized controlled pilot study comparing the efficacy of four types of catheter securement devices: standard polyurethane, tissue adhesive, bordered polyurethane and sutureless securement device. Probabilistic Monte Carlo simulation was used to characterize uncertainty surrounding the study results and to calculate the expected value of additional information. To guide the optimal future trial design, the expected costs and benefits of the alternative trial designs were estimated and compared. Analysis of the value of further information indicated that a randomized controlled trial on catheter securement devices is potentially worthwhile. Among the possible designs for the future trial, a four-arm study with 220 patients/arm would provide the highest expected net benefit corresponding to 130% return-on-investment. The initially considered design of 388 patients/arm, based on hypothesis testing calculations, would provide lower net benefit with return-on-investment of 79%. Cost-effectiveness and value of information analyses were based on the data from a single pilot trial which might affect the accuracy of our uncertainty estimation. Another limitation was that different follow-up durations for the larger trial were not evaluated. The value of information approach allows efficient trial design by maximizing the expected net benefit of additional research. This approach should be considered early in the design of randomized clinical trials. © The Author(s) 2014.

Anatomically-Aided PET Reconstruction Using the Kernel Method

PubMed Central

Hutchcroft, Will; Wang, Guobao; Chen, Kevin T.; Catana, Ciprian; Qi, Jinyi

2016-01-01

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest (ROI) quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization (EM) algorithm. PMID:27541810

Anatomically-aided PET reconstruction using the kernel method.

PubMed

Hutchcroft, Will; Wang, Guobao; Chen, Kevin T; Catana, Ciprian; Qi, Jinyi

2016-09-21

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.

Anatomically-aided PET reconstruction using the kernel method

NASA Astrophysics Data System (ADS)

Hutchcroft, Will; Wang, Guobao; Chen, Kevin T.; Catana, Ciprian; Qi, Jinyi

2016-09-01

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.

Adaptive Baseline Enhances EM-Based Policy Search: Validation in a View-Based Positioning Task of a Smartphone Balancer

PubMed Central

Wang, Jiexin; Uchibe, Eiji; Doya, Kenji

2017-01-01

EM-based policy search methods estimate a lower bound of the expected return from the histories of episodes and iteratively update the policy parameters using the maximum of a lower bound of expected return, which makes gradient calculation and learning rate tuning unnecessary. Previous algorithms like Policy learning by Weighting Exploration with the Returns, Fitness Expectation Maximization, and EM-based Policy Hyperparameter Exploration implemented the mechanisms to discard useless low-return episodes either implicitly or using a fixed baseline determined by the experimenter. In this paper, we propose an adaptive baseline method to discard worse samples from the reward history and examine different baselines, including the mean, and multiples of SDs from the mean. The simulation results of benchmark tasks of pendulum swing up and cart-pole balancing, and standing up and balancing of a two-wheeled smartphone robot showed improved performances. We further implemented the adaptive baseline with mean in our two-wheeled smartphone robot hardware to test its performance in the standing up and balancing task, and a view-based approaching task. Our results showed that with adaptive baseline, the method outperformed the previous algorithms and achieved faster, and more precise behaviors at a higher successful rate. PMID:28167910

Modeling multiple time series annotations as noisy distortions of the ground truth: An Expectation-Maximization approach.

PubMed

Gupta, Rahul; Audhkhasi, Kartik; Jacokes, Zach; Rozga, Agata; Narayanan, Shrikanth

2018-01-01

Studies of time-continuous human behavioral phenomena often rely on ratings from multiple annotators. Since the ground truth of the target construct is often latent, the standard practice is to use ad-hoc metrics (such as averaging annotator ratings). Despite being easy to compute, such metrics may not provide accurate representations of the underlying construct. In this paper, we present a novel method for modeling multiple time series annotations over a continuous variable that computes the ground truth by modeling annotator specific distortions. We condition the ground truth on a set of features extracted from the data and further assume that the annotators provide their ratings as modification of the ground truth, with each annotator having specific distortion tendencies. We train the model using an Expectation-Maximization based algorithm and evaluate it on a study involving natural interaction between a child and a psychologist, to predict confidence ratings of the children's smiles. We compare and analyze the model against two baselines where: (i) the ground truth in considered to be framewise mean of ratings from various annotators and, (ii) each annotator is assumed to bear a distinct time delay in annotation and their annotations are aligned before computing the framewise mean.

Data imputation analysis for Cosmic Rays time series

NASA Astrophysics Data System (ADS)

Fernandes, R. C.; Lucio, P. S.; Fernandez, J. H.

2017-05-01

The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II - runs the bootstrap Expectation Maximization algorithm, MICE - runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI - an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.

Robust prediction of consensus secondary structures using averaged base pairing probability matrices.

PubMed

Kiryu, Hisanori; Kin, Taishin; Asai, Kiyoshi

2007-02-15

Recent transcriptomic studies have revealed the existence of a considerable number of non-protein-coding RNA transcripts in higher eukaryotic cells. To investigate the functional roles of these transcripts, it is of great interest to find conserved secondary structures from multiple alignments on a genomic scale. Since multiple alignments are often created using alignment programs that neglect the special conservation patterns of RNA secondary structures for computational efficiency, alignment failures can cause potential risks of overlooking conserved stem structures. We investigated the dependence of the accuracy of secondary structure prediction on the quality of alignments. We compared three algorithms that maximize the expected accuracy of secondary structures as well as other frequently used algorithms. We found that one of our algorithms, called McCaskill-MEA, was more robust against alignment failures than others. The McCaskill-MEA method first computes the base pairing probability matrices for all the sequences in the alignment and then obtains the base pairing probability matrix of the alignment by averaging over these matrices. The consensus secondary structure is predicted from this matrix such that the expected accuracy of the prediction is maximized. We show that the McCaskill-MEA method performs better than other methods, particularly when the alignment quality is low and when the alignment consists of many sequences. Our model has a parameter that controls the sensitivity and specificity of predictions. We discussed the uses of that parameter for multi-step screening procedures to search for conserved secondary structures and for assigning confidence values to the predicted base pairs. The C++ source code that implements the McCaskill-MEA algorithm and the test dataset used in this paper are available at http://www.ncrna.org/papers/McCaskillMEA/. Supplementary data are available at Bioinformatics online.

Data Unfolding with Wiener-SVD Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, W.; Li, X.; Qian, X.

Here, data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.

Data Unfolding with Wiener-SVD Method

DOE PAGES

Tang, W.; Li, X.; Qian, X.; ...

2017-10-04

Here, data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.

Faster Mass Spectrometry-based Protein Inference: Junction Trees are More Efficient than Sampling and Marginalization by Enumeration

PubMed Central

Serang, Oliver; Noble, William Stafford

2012-01-01

The problem of identifying the proteins in a complex mixture using tandem mass spectrometry can be framed as an inference problem on a graph that connects peptides to proteins. Several existing protein identification methods make use of statistical inference methods for graphical models, including expectation maximization, Markov chain Monte Carlo, and full marginalization coupled with approximation heuristics. We show that, for this problem, the majority of the cost of inference usually comes from a few highly connected subgraphs. Furthermore, we evaluate three different statistical inference methods using a common graphical model, and we demonstrate that junction tree inference substantially improves rates of convergence compared to existing methods. The python code used for this paper is available at http://noble.gs.washington.edu/proj/fido. PMID:22331862

Text Classification for Intelligent Portfolio Management

DTIC Science & Technology

2002-05-01

years including nearest neighbor classification [15], naive Bayes with EM (Ex- pectation Maximization) [11] [13], Winnow with active learning [10... Active Learning and Expectation Maximization (EM). In particular, active learning is used to actively select documents for labeling, then EM assigns...generalization with active learning . Machine Learning, 15(2):201–221, 1994. [3] I. Dagan and P. Engelson. Committee-based sampling for training

Probabilistic performance estimators for computational chemistry methods: The empirical cumulative distribution function of absolute errors

NASA Astrophysics Data System (ADS)

Pernot, Pascal; Savin, Andreas

2018-06-01

Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the mean signed and mean unsigned errors, do not inform end-users on the expected amplitude of prediction errors attached to these methods. We show that, the distributions of model errors being neither normal nor zero-centered, these error statistics cannot be used to infer prediction error probabilities. To overcome this limitation, we advocate for the use of more informative statistics, based on the empirical cumulative distribution function of unsigned errors, namely, (1) the probability for a new calculation to have an absolute error below a chosen threshold and (2) the maximal amplitude of errors one can expect with a chosen high confidence level. Those statistics are also shown to be well suited for benchmarking and ranking studies. Moreover, the standard error on all benchmarking statistics depends on the size of the reference dataset. Systematic publication of these standard errors would be very helpful to assess the statistical reliability of benchmarking conclusions.

The Design of Mechatronics Simulator for Improving the Quality of Student Learning Course in Mechatronics

NASA Astrophysics Data System (ADS)

Kustija, J.; Hasbullah; Somantri, Y.

2018-02-01

Learning course on mechatronics specifically the Department of Electrical Engineering Education FPTK UPI still using simulation-aided instructional materials and software. It is still not maximizing students’ competencies in mechatronics courses required to skilfully manipulate the real will are implemented both in industry and in educational institutions. The purpose of this study is to submit a design of mechatronic simulator to improve student learning outcomes at the course mechatronics viewed aspects of cognitive and psychomotor. Learning innovation products resulting from this study is expected to be a reference and a key pillar for all academic units at UPI in implementing the learning environment. The method used in this research is quantitative method with the approach of Research and Development (R and D). Steps being taken in this study includes a preliminary study, design and testing of the design of mechatronic simulator that will be used in the course of mechatronics in DPTE FPTK UPI. Results of mechatronic design simulator which has been in testing using simulation modules and is expected to motivate students to improve the quality of learning good study results in the course of mechatronic expected to be realized.

When Does Reward Maximization Lead to Matching Law?

PubMed Central

Sakai, Yutaka; Fukai, Tomoki

2008-01-01

What kind of strategies subjects follow in various behavioral circumstances has been a central issue in decision making. In particular, which behavioral strategy, maximizing or matching, is more fundamental to animal's decision behavior has been a matter of debate. Here, we prove that any algorithm to achieve the stationary condition for maximizing the average reward should lead to matching when it ignores the dependence of the expected outcome on subject's past choices. We may term this strategy of partial reward maximization “matching strategy”. Then, this strategy is applied to the case where the subject's decision system updates the information for making a decision. Such information includes subject's past actions or sensory stimuli, and the internal storage of this information is often called “state variables”. We demonstrate that the matching strategy provides an easy way to maximize reward when combined with the exploration of the state variables that correctly represent the crucial information for reward maximization. Our results reveal for the first time how a strategy to achieve matching behavior is beneficial to reward maximization, achieving a novel insight into the relationship between maximizing and matching. PMID:19030101

A multistage stochastic programming model for a multi-period strategic expansion of biofuel supply chain under evolving uncertainties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Fei; Huang, Yongxi

Here, we develop a multistage, stochastic mixed-integer model to support biofuel supply chain expansion under evolving uncertainties. By utilizing the block-separable recourse property, we reformulate the multistage program in an equivalent two-stage program and solve it using an enhanced nested decomposition method with maximal non-dominated cuts. We conduct extensive numerical experiments and demonstrate the application of the model and algorithm in a case study based on the South Carolina settings. The value of multistage stochastic programming method is also explored by comparing the model solution with the counterparts of an expected value based deterministic model and a two-stage stochastic model.

Deep neural network and noise classification-based speech enhancement

NASA Astrophysics Data System (ADS)

Shi, Wenhua; Zhang, Xiongwei; Zou, Xia; Han, Wei

2017-07-01

In this paper, a speech enhancement method using noise classification and Deep Neural Network (DNN) was proposed. Gaussian mixture model (GMM) was employed to determine the noise type in speech-absent frames. DNN was used to model the relationship between noisy observation and clean speech. Once the noise type was determined, the corresponding DNN model was applied to enhance the noisy speech. GMM was trained with mel-frequency cepstrum coefficients (MFCC) and the parameters were estimated with an iterative expectation-maximization (EM) algorithm. Noise type was updated by spectrum entropy-based voice activity detection (VAD). Experimental results demonstrate that the proposed method could achieve better objective speech quality and smaller distortion under stationary and non-stationary conditions.

A multistage stochastic programming model for a multi-period strategic expansion of biofuel supply chain under evolving uncertainties

DOE PAGES

Xie, Fei; Huang, Yongxi

2018-02-04

Here, we develop a multistage, stochastic mixed-integer model to support biofuel supply chain expansion under evolving uncertainties. By utilizing the block-separable recourse property, we reformulate the multistage program in an equivalent two-stage program and solve it using an enhanced nested decomposition method with maximal non-dominated cuts. We conduct extensive numerical experiments and demonstrate the application of the model and algorithm in a case study based on the South Carolina settings. The value of multistage stochastic programming method is also explored by comparing the model solution with the counterparts of an expected value based deterministic model and a two-stage stochastic model.

Work Placement in UK Undergraduate Programmes. Student Expectations and Experiences.

ERIC Educational Resources Information Center

Leslie, David; Richardson, Anne

1999-01-01

A survey of 189 pre- and 106 post-sandwich work-experience students in tourism suggested that potential benefits were not being maximized. Students needed better preparation for the work experience, especially in terms of their expectations. The work experience needed better design, and the role of industry tutors needed clarification. (SK)

Career Preference among Universities' Faculty: Literature Review

ERIC Educational Resources Information Center

Alenzi, Faris Q.; Salem, Mohamed L.

2007-01-01

Why do people enter academic life? What are their expectations? How can they maximize their experience and achievements, both short- and long-term? How much should they move towards commercialization? What can they do to improve their career? How much autonomy can they reasonably expect? What are the key issues for academics and aspiring academics…

Picking battles wisely: plant behaviour under competition.

PubMed

Novoplansky, Ariel

2009-06-01

Plants are limited in their ability to choose their neighbours, but they are able to orchestrate a wide spectrum of rational competitive behaviours that increase their prospects to prevail under various ecological settings. Through the perception of neighbours, plants are able to anticipate probable competitive interactions and modify their competitive behaviours to maximize their long-term gains. Specifically, plants can minimize competitive encounters by avoiding their neighbours; maximize their competitive effects by aggressively confronting their neighbours; or tolerate the competitive effects of their neighbours. However, the adaptive values of these non-mutually exclusive options are expected to depend strongly on the plants' evolutionary background and to change dynamically according to their past development, and relative sizes and vigour. Additionally, the magnitude of competitive responsiveness is expected to be positively correlated with the reliability of the environmental information regarding the expected competitive interactions and the expected time left for further plastic modifications. Concurrent competition over external and internal resources and morphogenetic signals may enable some plants to increase their efficiency and external competitive performance by discriminately allocating limited resources to their more promising organs at the expense of failing or less successful organs.

Modeling Individual Patient Preferences for Colorectal Cancer Screening Based on Their Tolerance for Complications Risk.

PubMed

Taksler, Glen B; Perzynski, Adam T; Kattan, Michael W

2017-04-01

Recommendations for colorectal cancer screening encourage patients to choose among various screening methods based on individual preferences for benefits, risks, screening frequency, and discomfort. We devised a model to illustrate how individuals with varying tolerance for screening complications risk might decide on their preferred screening strategy. We developed a discrete-time Markov mathematical model that allowed hypothetical individuals to maximize expected lifetime utility by selecting screening method, start age, stop age, and frequency. Individuals could choose from stool-based testing every 1 to 3 years, flexible sigmoidoscopy every 1 to 20 years with annual stool-based testing, colonoscopy every 1 to 20 years, or no screening. We compared the life expectancy gained from the chosen strategy with the life expectancy available from a benchmark strategy of decennial colonoscopy. For an individual at average risk of colorectal cancer who was risk neutral with respect to screening complications (and therefore was willing to undergo screening if it would actuarially increase life expectancy), the model predicted that he or she would choose colonoscopy every 10 years, from age 53 to 73 years, consistent with national guidelines. For a similar individual who was moderately averse to screening complications risk (and therefore required a greater increase in life expectancy to accept potential risks of colonoscopy), the model predicted that he or she would prefer flexible sigmoidoscopy every 12 years with annual stool-based testing, with 93% of the life expectancy benefit of decennial colonoscopy. For an individual with higher risk aversion, the model predicted that he or she would prefer 2 lifetime flexible sigmoidoscopies, 20 years apart, with 70% of the life expectancy benefit of decennial colonoscopy. Mathematical models may formalize how individuals with different risk attitudes choose between various guideline-recommended colorectal cancer screening strategies.

Sensitivity evaluation of dynamic speckle activity measurements using clustering methods.

PubMed

Etchepareborda, Pablo; Federico, Alejandro; Kaufmann, Guillermo H

2010-07-01

We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi-Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.

Presenting evidence and summary measures to best inform societal decisions when comparing multiple strategies.

PubMed

Eckermann, Simon; Willan, Andrew R

2011-07-01

Multiple strategy comparisons in health technology assessment (HTA) are becoming increasingly important, with multiple alternative therapeutic actions, combinations of therapies and diagnostic and genetic testing alternatives. Comparison under uncertainty of incremental cost, effects and cost effectiveness across more than two strategies is conceptually and practically very different from that for two strategies, where all evidence can be summarized in a single bivariate distribution on the incremental cost-effectiveness plane. Alternative methods for comparing multiple strategies in HTA have been developed in (i) presenting cost and effects on the cost-disutility plane and (ii) summarizing evidence with multiple strategy cost-effectiveness acceptability (CEA) and expected net loss (ENL) curves and frontiers. However, critical questions remain for the analyst and decision maker of how these techniques can be best employed across multiple strategies to (i) inform clinical and cost inference in presenting evidence, and (ii) summarize evidence of cost effectiveness to inform societal reimbursement decisions where preferences may be risk neutral or somewhat risk averse under the Arrow-Lind theorem. We critically consider how evidence across multiple strategies can be best presented and summarized to inform inference and societal reimbursement decisions, given currently available methods. In the process, we make a number of important original findings. First, in presenting evidence for multiple strategies, the joint distribution of costs and effects on the cost-disutility plane with associated flexible comparators varying across replicates for cost and effect axes ensure full cost and effect inference. Such inference is usually confounded on the cost-effectiveness plane with comparison relative to a fixed origin and axes. Second, in summarizing evidence for risk-neutral societal decision making, ENL curves and frontiers are shown to have advantages over the CEA frontier in directly presenting differences in expected net benefit (ENB). The CEA frontier, while identifying strategies that maximize ENB, only presents their probability of maximizing net benefit (NB) and, hence, fails to explain why strategies maximize ENB at any given threshold value. Third, in summarizing evidence for somewhat risk-averse societal decision making, trade-offs between the strategy maximizing ENB and other potentially optimal strategies with higher probability of maximizing NB should be presented over discrete threshold values where they arise. However, the probabilities informing these trade-offs and associated discrete threshold value regions should be derived from bilateral CEA curves to prevent confounding by other strategies inherent in multiple strategy CEA curves. Based on these findings, a series of recommendations are made for best presenting and summarizing cost-effectiveness evidence for reimbursement decisions when comparing multiple strategies, which are contrasted with advice for comparing two strategies. Implications for joint research and reimbursement decisions are also discussed.

How do we assign punishment? The impact of minimal and maximal standards on the evaluation of deviants.

PubMed

Kessler, Thomas; Neumann, Jörg; Mummendey, Amélie; Berthold, Anne; Schubert, Thomas; Waldzus, Sven

2010-09-01

To explain the determinants of negative behavior toward deviants (e.g., punishment), this article examines how people evaluate others on the basis of two types of standards: minimal and maximal. Minimal standards focus on an absolute cutoff point for appropriate behavior; accordingly, the evaluation of others varies dichotomously between acceptable or unacceptable. Maximal standards focus on the degree of deviation from that standard; accordingly, the evaluation of others varies gradually from positive to less positive. This framework leads to the prediction that violation of minimal standards should elicit punishment regardless of the degree of deviation, whereas punishment in response to violations of maximal standards should depend on the degree of deviation. Four studies assessed or manipulated the type of standard and degree of deviation displayed by a target. Results consistently showed the expected interaction between type of standard (minimal and maximal) and degree of deviation on punishment behavior.

Subjective evaluation of HEVC in mobile devices

NASA Astrophysics Data System (ADS)

Garcia, Ray; Kalva, Hari

2013-03-01

Mobile compute environments provide a unique set of user needs and expectations that designers must consider. With increased multimedia use in mobile environments, video encoding methods within the smart phone market segment are key factors that contribute to positive user experience. Currently available display resolutions and expected cellular bandwidth are major factors the designer must consider when determining which encoding methods should be supported. The desired goal is to maximize the consumer experience, reduce cost, and reduce time to market. This paper presents a comparative evaluation of the quality of user experience when HEVC and AVC/H.264 video coding standards were used. The goal of the study was to evaluate any improvements in user experience when using HEVC. Subjective comparisons were made between H.264/AVC and HEVC encoding standards in accordance with Doublestimulus impairment scale (DSIS) as defined by ITU-R BT.500-13. Test environments are based on smart phone LCD resolutions and expected cellular bit rates, such as 200kbps and 400kbps. Subjective feedback shows both encoding methods are adequate at 400kbps constant bit rate. However, a noticeable consumer experience gap was observed for 200 kbps. Significantly less H.264 subjective quality is noticed with video sequences that have multiple objects moving and no single point of visual attraction. Video sequences with single points of visual attraction or few moving objects tended to have higher H.264 subjective quality.

Societal preferences for distributive justice in the allocation of health care resources: a latent class discrete choice experiment.

PubMed

Skedgel, Chris; Wailoo, Allan; Akehurst, Ron

2015-01-01

Economic theory suggests that resources should be allocated in a way that produces the greatest outputs, on the grounds that maximizing output allows for a redistribution that could benefit everyone. In health care, this is known as QALY (quality-adjusted life-year) maximization. This justification for QALY maximization may not hold, though, as it is difficult to reallocate health. Therefore, the allocation of health care should be seen as a matter of distributive justice as well as efficiency. A discrete choice experiment was undertaken to test consistency with the principles of QALY maximization and to quantify the willingness to trade life-year gains for distributive justice. An empirical ethics process was used to identify attributes that appeared relevant and ethically justified: patient age, severity (decomposed into initial quality and life expectancy), final health state, duration of benefit, and distributional concerns. Only 3% of respondents maximized QALYs with every choice, but scenarios with larger aggregate QALY gains were chosen more often and a majority of respondents maximized QALYs in a majority of their choices. However, respondents also appeared willing to prioritize smaller gains to preferred groups over larger gains to less preferred groups. Marginal analyses found a statistically significant preference for younger patients and a wider distribution of gains, as well as an aversion to patients with the shortest life expectancy or a poor final health state. These results support the existence of an equity-efficiency tradeoff and suggest that well-being could be enhanced by giving priority to programs that best satisfy societal preferences. Societal preferences could be incorporated through the use of explicit equity weights, although more research is required before such weights can be used in priority setting. © The Author(s) 2014.

Flexible nano- and microliter injections on a single liquid chromatography-mass spectrometry system: Minimizing sample preparation and maximizing linear dynamic range.

PubMed

Lubin, Arnaud; Sheng, Sheng; Cabooter, Deirdre; Augustijns, Patrick; Cuyckens, Filip

2017-11-17

Lack of knowledge on the expected concentration range or insufficient linear dynamic range of the analytical method applied are common challenges for the analytical scientist. Samples that are above the upper limit of quantification are typically diluted and reanalyzed. The analysis of undiluted highly concentrated samples can cause contamination of the system, while the dilution step is time consuming and as the case for any sample preparation step, also potentially leads to precipitation, adsorption or degradation of the analytes. Copyright © 2017 Elsevier B.V. All rights reserved.

From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder

PubMed Central

Venkataraman, Archana; Kubicki, Marek; Golland, Polina

2014-01-01

We propose a novel approach to identify the foci of a neurological disorder based on anatomical and functional connectivity information. Specifically, we formulate a generative model that characterizes the network of abnormal functional connectivity emanating from the affected foci. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. We employ the variational expectation-maximization algorithm to fit the model and subsequently identify both the afflicted regions and the differences in connectivity induced by the disorder. We demonstrate our method on a population study of schizophrenia. PMID:23864168

Effect of filters and reconstruction algorithms on I-124 PET in Siemens Inveon PET scanner

NASA Astrophysics Data System (ADS)

Ram Yu, A.; Kim, Jin Su

2015-10-01

Purpose: To assess the effects of filtering and reconstruction on Siemens I-124 PET data. Methods: A Siemens Inveon PET was used. Spatial resolution of I-124 was measured to a transverse offset of 50 mm from the center FBP, 2D ordered subset expectation maximization (OSEM2D), 3D re-projection algorithm (3DRP), and maximum a posteriori (MAP) methods were tested. Non-uniformity (NU), recovery coefficient (RC), and spillover ratio (SOR) parameterized image quality. Mini deluxe phantom data of I-124 was also assessed. Results: Volumetric resolution was 7.3 mm3 from the transverse FOV center when FBP reconstruction algorithms with ramp filter was used. MAP yielded minimal NU with β =1.5. OSEM2D yielded maximal RC. SOR was below 4% for FBP with ramp, Hamming, Hanning, or Shepp-Logan filters. Based on the mini deluxe phantom results, an FBP with Hanning or Parzen filters, or a 3DRP with Hanning filter yielded feasible I-124 PET data.Conclusions: Reconstruction algorithms and filters were compared. FBP with Hanning or Parzen filters, or 3DRP with Hanning filter yielded feasible data for quantifying I-124 PET.

Expectation-maximization of the potential of mean force and diffusion coefficient in Langevin dynamics from single molecule FRET data photon by photon.

PubMed

Haas, Kevin R; Yang, Haw; Chu, Jhih-Wei

2013-12-12

The dynamics of a protein along a well-defined coordinate can be formally projected onto the form of an overdamped Lagevin equation. Here, we present a comprehensive statistical-learning framework for simultaneously quantifying the deterministic force (the potential of mean force, PMF) and the stochastic force (characterized by the diffusion coefficient, D) from single-molecule Förster-type resonance energy transfer (smFRET) experiments. The likelihood functional of the Langevin parameters, PMF and D, is expressed by a path integral of the latent smFRET distance that follows Langevin dynamics and realized by the donor and the acceptor photon emissions. The solution is made possible by an eigen decomposition of the time-symmetrized form of the corresponding Fokker-Planck equation coupled with photon statistics. To extract the Langevin parameters from photon arrival time data, we advance the expectation-maximization algorithm in statistical learning, originally developed for and mostly used in discrete-state systems, to a general form in the continuous space that allows for a variational calculus on the continuous PMF function. We also introduce the regularization of the solution space in this Bayesian inference based on a maximum trajectory-entropy principle. We use a highly nontrivial example with realistically simulated smFRET data to illustrate the application of this new method.

MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates.

PubMed

Ortiz-Rosario, Alexis; Adeli, Hojjat; Buford, John A

2017-01-15

Researchers often rely on simple methods to identify involvement of neurons in a particular motor task. The historical approach has been to inspect large groups of neurons and subjectively separate neurons into groups based on the expertise of the investigator. In cases where neuron populations are small it is reasonable to inspect these neuronal recordings and their firing rates carefully to avoid data omissions. In this paper, a new methodology is presented for automatic objective classification of neurons recorded in association with behavioral tasks into groups. By identifying characteristics of neurons in a particular group, the investigator can then identify functional classes of neurons based on their relationship to the task. The methodology is based on integration of a multiple signal classification (MUSIC) algorithm to extract relevant features from the firing rate and an expectation-maximization Gaussian mixture algorithm (EM-GMM) to cluster the extracted features. The methodology is capable of identifying and clustering similar firing rate profiles automatically based on specific signal features. An empirical wavelet transform (EWT) was used to validate the features found in the MUSIC pseudospectrum and the resulting signal features captured by the methodology. Additionally, this methodology was used to inspect behavioral elements of neurons to physiologically validate the model. This methodology was tested using a set of data collected from awake behaving non-human primates. Copyright © 2016 Elsevier B.V. All rights reserved.

A stochastic estimation procedure for intermittently-observed semi-Markov multistate models with back transitions.

PubMed

Aralis, Hilary; Brookmeyer, Ron

2017-01-01

Multistate models provide an important method for analyzing a wide range of life history processes including disease progression and patient recovery following medical intervention. Panel data consisting of the states occupied by an individual at a series of discrete time points are often used to estimate transition intensities of the underlying continuous-time process. When transition intensities depend on the time elapsed in the current state and back transitions between states are possible, this intermittent observation process presents difficulties in estimation due to intractability of the likelihood function. In this manuscript, we present an iterative stochastic expectation-maximization algorithm that relies on a simulation-based approximation to the likelihood function and implement this algorithm using rejection sampling. In a simulation study, we demonstrate the feasibility and performance of the proposed procedure. We then demonstrate application of the algorithm to a study of dementia, the Nun Study, consisting of intermittently-observed elderly subjects in one of four possible states corresponding to intact cognition, impaired cognition, dementia, and death. We show that the proposed stochastic expectation-maximization algorithm substantially reduces bias in model parameter estimates compared to an alternative approach used in the literature, minimal path estimation. We conclude that in estimating intermittently observed semi-Markov models, the proposed approach is a computationally feasible and accurate estimation procedure that leads to substantial improvements in back transition estimates.

Layered motion segmentation and depth ordering by tracking edges.

PubMed

Smith, Paul; Drummond, Tom; Cipolla, Roberto

2004-04-01

This paper presents a new Bayesian framework for motion segmentation--dividing a frame from an image sequence into layers representing different moving objects--by tracking edges between frames. Edges are found using the Canny edge detector, and the Expectation-Maximization algorithm is then used to fit motion models to these edges and also to calculate the probabilities of the edges obeying each motion model. The edges are also used to segment the image into regions of similar color. The most likely labeling for these regions is then calculated by using the edge probabilities, in association with a Markov Random Field-style prior. The identification of the relative depth ordering of the different motion layers is also determined, as an integral part of the process. An efficient implementation of this framework is presented for segmenting two motions (foreground and background) using two frames. It is then demonstrated how, by tracking the edges into further frames, the probabilities may be accumulated to provide an even more accurate and robust estimate, and segment an entire sequence. Further extensions are then presented to address the segmentation of more than two motions. Here, a hierarchical method of initializing the Expectation-Maximization algorithm is described, and it is demonstrated that the Minimum Description Length principle may be used to automatically select the best number of motion layers. The results from over 30 sequences (demonstrating both two and three motions) are presented and discussed.

Three-class ROC analysis--the equal error utility assumption and the optimality of three-class ROC surface using the ideal observer.

PubMed

He, Xin; Frey, Eric C

2006-08-01

Previously, we have developed a decision model for three-class receiver operating characteristic (ROC) analysis based on decision theory. The proposed decision model maximizes the expected decision utility under the assumption that incorrect decisions have equal utilities under the same hypothesis (equal error utility assumption). This assumption reduced the dimensionality of the "general" three-class ROC analysis and provided a practical figure-of-merit to evaluate the three-class task performance. However, it also limits the generality of the resulting model because the equal error utility assumption will not apply for all clinical three-class decision tasks. The goal of this study was to investigate the optimality of the proposed three-class decision model with respect to several other decision criteria. In particular, besides the maximum expected utility (MEU) criterion used in the previous study, we investigated the maximum-correctness (MC) (or minimum-error), maximum likelihood (ML), and Nyman-Pearson (N-P) criteria. We found that by making assumptions for both MEU and N-P criteria, all decision criteria lead to the previously-proposed three-class decision model. As a result, this model maximizes the expected utility under the equal error utility assumption, maximizes the probability of making correct decisions, satisfies the N-P criterion in the sense that it maximizes the sensitivity of one class given the sensitivities of the other two classes, and the resulting ROC surface contains the maximum likelihood decision operating point. While the proposed three-class ROC analysis model is not optimal in the general sense due to the use of the equal error utility assumption, the range of criteria for which it is optimal increases its applicability for evaluating and comparing a range of diagnostic systems.

Tug-Of-War Model for Two-Bandit Problem

NASA Astrophysics Data System (ADS)

Kim, Song-Ju; Aono, Masashi; Hara, Masahiko

The amoeba of the true slime mold Physarum polycephalum shows high computational capabilities. In the so-called amoeba-based computing, some computing tasks including combinatorial optimization are performed by the amoeba instead of a digital computer. We expect that there must be problems living organisms are good at solving. The “multi-armed bandit problem” would be the one of such problems. Consider a number of slot machines. Each of the machines has an arm which gives a player a reward with a certain probability when pulled. The problem is to determine the optimal strategy for maximizing the total reward sum after a certain number of trials. To maximize the total reward sum, it is necessary to judge correctly and quickly which machine has the highest reward probability. Therefore, the player should explore many machines to gather much knowledge on which machine is the best, but should not fail to exploit the reward from the known best machine. We consider that living organisms follow some efficient method to solve the problem.

Robust statistical reconstruction for charged particle tomography

DOEpatents

Schultz, Larry Joe; Klimenko, Alexei Vasilievich; Fraser, Andrew Mcleod; Morris, Christopher; Orum, John Christopher; Borozdin, Konstantin N; Sossong, Michael James; Hengartner, Nicolas W

2013-10-08

Systems and methods for charged particle detection including statistical reconstruction of object volume scattering density profiles from charged particle tomographic data to determine the probability distribution of charged particle scattering using a statistical multiple scattering model and determine a substantially maximum likelihood estimate of object volume scattering density using expectation maximization (ML/EM) algorithm to reconstruct the object volume scattering density. The presence of and/or type of object occupying the volume of interest can be identified from the reconstructed volume scattering density profile. The charged particle tomographic data can be cosmic ray muon tomographic data from a muon tracker for scanning packages, containers, vehicles or cargo. The method can be implemented using a computer program which is executable on a computer.

Physical renormalization condition for de Sitter QED

NASA Astrophysics Data System (ADS)

Hayashinaka, Takahiro; Xue, She-Sheng

2018-05-01

We considered a new renormalization condition for the vacuum expectation values of the scalar and spinor currents induced by a homogeneous and constant electric field background in de Sitter spacetime. Following a semiclassical argument, the condition named maximal subtraction imposes the exponential suppression on the massive charged particle limit of the renormalized currents. The maximal subtraction changes the behaviors of the induced currents previously obtained by the conventional minimal subtraction scheme. The maximal subtraction is favored for a couple of physically decent predictions including the identical asymptotic behavior of the scalar and spinor currents, the removal of the IR hyperconductivity from the scalar current, and the finite current for the massless fermion.

Applying Probabilistic Decision Models to Clinical Trial Design

PubMed Central

Smith, Wade P; Phillips, Mark H

2018-01-01

Clinical trial design most often focuses on a single or several related outcomes with corresponding calculations of statistical power. We consider a clinical trial to be a decision problem, often with competing outcomes. Using a current controversy in the treatment of HPV-positive head and neck cancer, we apply several different probabilistic methods to help define the range of outcomes given different possible trial designs. Our model incorporates the uncertainties in the disease process and treatment response and the inhomogeneities in the patient population. Instead of expected utility, we have used a Markov model to calculate quality adjusted life expectancy as a maximization objective. Monte Carlo simulations over realistic ranges of parameters are used to explore different trial scenarios given the possible ranges of parameters. This modeling approach can be used to better inform the initial trial design so that it will more likely achieve clinical relevance. PMID:29888075

Simultaneous 99mtc/111in spect reconstruction using accelerated convolution-based forced detection monte carlo

NASA Astrophysics Data System (ADS)

Karamat, Muhammad I.; Farncombe, Troy H.

2015-10-01

Simultaneous multi-isotope Single Photon Emission Computed Tomography (SPECT) imaging has a number of applications in cardiac, brain, and cancer imaging. The major concern however, is the significant crosstalk contamination due to photon scatter between the different isotopes. The current study focuses on a method of crosstalk compensation between two isotopes in simultaneous dual isotope SPECT acquisition applied to cancer imaging using 99mTc and 111In. We have developed an iterative image reconstruction technique that simulates the photon down-scatter from one isotope into the acquisition window of a second isotope. Our approach uses an accelerated Monte Carlo (MC) technique for the forward projection step in an iterative reconstruction algorithm. The MC estimated scatter contamination of a radionuclide contained in a given projection view is then used to compensate for the photon contamination in the acquisition window of other nuclide. We use a modified ordered subset-expectation maximization (OS-EM) algorithm named simultaneous ordered subset-expectation maximization (Sim-OSEM), to perform this step. We have undertaken a number of simulation tests and phantom studies to verify this approach. The proposed reconstruction technique was also evaluated by reconstruction of experimentally acquired phantom data. Reconstruction using Sim-OSEM showed very promising results in terms of contrast recovery and uniformity of object background compared to alternative reconstruction methods implementing alternative scatter correction schemes (i.e., triple energy window or separately acquired projection data). In this study the evaluation is based on the quality of reconstructed images and activity estimated using Sim-OSEM. In order to quantitate the possible improvement in spatial resolution and signal to noise ratio (SNR) observed in this study, further simulation and experimental studies are required.

A simple method for measurement of maximal downstroke power on friction-loaded cycle ergometer.

PubMed

Morin, Jean-Benoît; Belli, Alain

2004-01-01

The aim of this study was to propose and validate a post-hoc correction method to obtain maximal power values taking into account inertia of the flywheel during sprints on friction-loaded cycle ergometers. This correction method was obtained from a basic postulate of linear deceleration-time evolution during the initial phase (until maximal power) of a sprint and included simple parameters as flywheel inertia, maximal velocity, time to reach maximal velocity and friction force. The validity of this model was tested by comparing measured and calculated maximal power values for 19 sprint bouts performed by five subjects against 0.6-1 N kg(-1) friction loads. Non-significant differences between measured and calculated maximal power (1151+/-169 vs. 1148+/-170 W) and a mean error index of 1.31+/-1.20% (ranging from 0.09% to 4.20%) showed the validity of this method. Furthermore, the differences between measured maximal power and power neglecting inertia (20.4+/-7.6%, ranging from 9.5% to 33.2%) emphasized the usefulness of power correcting in studies about anaerobic power which do not include inertia, and also the interest of this simple post-hoc method.

Recovery of high-quality RNA from laser capture microdissected human and rodent pancreas.

PubMed

Butler, Alexandra E; Matveyenko, Aleksey V; Kirakossian, David; Park, Johanna; Gurlo, Tatyana; Butler, Peter C

Laser capture microdissection (LCM) is a powerful method to isolate specific populations of cells for subsequent analysis such as gene expression profiling, for example, microarrays or ribonucleic (RNA)-Seq. This technique has been applied to frozen as well as formalin-fixed, paraffin-embedded (FFPE) specimens with variable outcomes regarding quality and quantity of extracted RNA. The goal of the study was to develop the methods to isolate high-quality RNA from islets of Langerhans and pancreatic duct glands (PDG) isolated by LCM. We report an optimized protocol for frozen sections to minimize RNA degradation and maximize recovery of expected transcripts from the samples using quantitative real-time polymerase chain reaction (RT-PCR) by adding RNase inhibitors at multiple steps during the experiment. This technique reproducibly delivered intact RNA (RIN values 6-7). Using quantitative RT-PCR, the expected profiles of insulin, glucagon, mucin6 (Muc6), and cytokeratin-19 (CK-19) mRNA in PDGs and pancreatic islets were detected. The described experimental protocol for frozen pancreas tissue might also be useful for other tissues with moderate to high levels of intrinsic ribonuclease (RNase) activity.

Comparison of methods for H*(10) calculation from measured LaBr3(Ce) detector spectra.

PubMed

Vargas, A; Cornejo, N; Camp, A

2018-07-01

The Universitat Politecnica de Catalunya (UPC) and the Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT) have evaluated methods based on stripping, conversion coefficients and Maximum Likelihood Estimation using Expectation Maximization (ML-EM) in calculating the H*(10) rates from photon pulse-height spectra acquired with a spectrometric LaBr 3 (Ce)(1.5″ × 1.5″) detector. There is a good agreement between results of the different H*(10) rate calculation methods using the spectra measured at the UPC secondary standard calibration laboratory in Barcelona. From the outdoor study at ESMERALDA station in Madrid, it can be concluded that the analysed methods provide results quite similar to those obtained with the reference RSS ionization chamber. In addition, the spectrometric detectors can also facilitate radionuclide identification. Copyright © 2018 Elsevier Ltd. All rights reserved.

Optimized multiple linear mappings for single image super-resolution

NASA Astrophysics Data System (ADS)

Zhang, Kaibing; Li, Jie; Xiong, Zenggang; Liu, Xiuping; Gao, Xinbo

2017-12-01

Learning piecewise linear regression has been recognized as an effective way for example learning-based single image super-resolution (SR) in literature. In this paper, we employ an expectation-maximization (EM) algorithm to further improve the SR performance of our previous multiple linear mappings (MLM) based SR method. In the training stage, the proposed method starts with a set of linear regressors obtained by the MLM-based method, and then jointly optimizes the clustering results and the low- and high-resolution subdictionary pairs for regression functions by using the metric of the reconstruction errors. In the test stage, we select the optimal regressor for SR reconstruction by accumulating the reconstruction errors of m-nearest neighbors in the training set. Thorough experimental results carried on six publicly available datasets demonstrate that the proposed SR method can yield high-quality images with finer details and sharper edges in terms of both quantitative and perceptual image quality assessments.

The EM Method in a Probabilistic Wavelet-Based MRI Denoising

PubMed Central

2015-01-01

Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the noise, when no signal is present, is Rayleigh distributed and its wavelet coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled by a Laplacian distribution in the wavelet domain. This paper proposes a new magnetic resonance image denoising method to solve this fact. This method performs shrinkage of wavelet coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization (EM) method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak's, Donoho-Johnstone's, Awate-Whitaker's, and nonlocal means filters, in different 2D and 3D images. PMID:26089959

The EM Method in a Probabilistic Wavelet-Based MRI Denoising.

PubMed

Martin-Fernandez, Marcos; Villullas, Sergio

2015-01-01

Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the noise, when no signal is present, is Rayleigh distributed and its wavelet coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled by a Laplacian distribution in the wavelet domain. This paper proposes a new magnetic resonance image denoising method to solve this fact. This method performs shrinkage of wavelet coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization (EM) method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak's, Donoho-Johnstone's, Awate-Whitaker's, and nonlocal means filters, in different 2D and 3D images.

Experimental design and statistical methods for improved hit detection in high-throughput screening.

PubMed

Malo, Nathalie; Hanley, James A; Carlile, Graeme; Liu, Jing; Pelletier, Jerry; Thomas, David; Nadon, Robert

2010-09-01

Identification of active compounds in high-throughput screening (HTS) contexts can be substantially improved by applying classical experimental design and statistical inference principles to all phases of HTS studies. The authors present both experimental and simulated data to illustrate how true-positive rates can be maximized without increasing false-positive rates by the following analytical process. First, the use of robust data preprocessing methods reduces unwanted variation by removing row, column, and plate biases. Second, replicate measurements allow estimation of the magnitude of the remaining random error and the use of formal statistical models to benchmark putative hits relative to what is expected by chance. Receiver Operating Characteristic (ROC) analyses revealed superior power for data preprocessed by a trimmed-mean polish method combined with the RVM t-test, particularly for small- to moderate-sized biological hits.

Network approach for decision making under risk—How do we choose among probabilistic options with the same expected value?

PubMed Central

Chen, Yi-Shin

2018-01-01

Conventional decision theory suggests that under risk, people choose option(s) by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing ‘goal’ and ‘time’ factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight. PMID:29702665

Network approach for decision making under risk-How do we choose among probabilistic options with the same expected value?

PubMed

Pan, Wei; Chen, Yi-Shin

2018-01-01

Conventional decision theory suggests that under risk, people choose option(s) by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing 'goal' and 'time' factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight.

Can Monkeys Make Investments Based on Maximized Pay-off?

PubMed Central

Steelandt, Sophie; Dufour, Valérie; Broihanne, Marie-Hélène; Thierry, Bernard

2011-01-01

Animals can maximize benefits but it is not known if they adjust their investment according to expected pay-offs. We investigated whether monkeys can use different investment strategies in an exchange task. We tested eight capuchin monkeys (Cebus apella) and thirteen macaques (Macaca fascicularis, Macaca tonkeana) in an experiment where they could adapt their investment to the food amounts proposed by two different experimenters. One, the doubling partner, returned a reward that was twice the amount given by the subject, whereas the other, the fixed partner, always returned a constant amount regardless of the amount given. To maximize pay-offs, subjects should invest a maximal amount with the first partner and a minimal amount with the second. When tested with the fixed partner only, one third of monkeys learned to remove a maximal amount of food for immediate consumption before investing a minimal one. With both partners, most subjects failed to maximize pay-offs by using different decision rules with each partner' quality. A single Tonkean macaque succeeded in investing a maximal amount to one experimenter and a minimal amount to the other. The fact that only one of over 21 subjects learned to maximize benefits in adapting investment according to experimenters' quality indicates that such a task is difficult for monkeys, albeit not impossible. PMID:21423777

Flexible mini gamma camera reconstructions of extended sources using step and shoot and list mode.

PubMed

Gardiazabal, José; Matthies, Philipp; Vogel, Jakob; Frisch, Benjamin; Navab, Nassir; Ziegler, Sibylle; Lasser, Tobias

2016-12-01

Hand- and robot-guided mini gamma cameras have been introduced for the acquisition of single-photon emission computed tomography (SPECT) images. Less cumbersome than whole-body scanners, they allow for a fast acquisition of the radioactivity distribution, for example, to differentiate cancerous from hormonally hyperactive lesions inside the thyroid. This work compares acquisition protocols and reconstruction algorithms in an attempt to identify the most suitable approach for fast acquisition and efficient image reconstruction, suitable for localization of extended sources, such as lesions inside the thyroid. Our setup consists of a mini gamma camera with precise tracking information provided by a robotic arm, which also provides reproducible positioning for our experiments. Based on a realistic phantom of the thyroid including hot and cold nodules as well as background radioactivity, the authors compare "step and shoot" (SAS) and continuous data (CD) acquisition protocols in combination with two different statistical reconstruction methods: maximum-likelihood expectation-maximization (ML-EM) for time-integrated count values and list-mode expectation-maximization (LM-EM) for individually detected gamma rays. In addition, the authors simulate lower uptake values by statistically subsampling the experimental data in order to study the behavior of their approach without changing other aspects of the acquired data. All compared methods yield suitable results, resolving the hot nodules and the cold nodule from the background. However, the CD acquisition is twice as fast as the SAS acquisition, while yielding better coverage of the thyroid phantom, resulting in qualitatively more accurate reconstructions of the isthmus between the lobes. For CD acquisitions, the LM-EM reconstruction method is preferable, as it yields comparable image quality to ML-EM at significantly higher speeds, on average by an order of magnitude. This work identifies CD acquisition protocols combined with LM-EM reconstruction as a prime candidate for the wider introduction of SPECT imaging with flexible mini gamma cameras in the clinical practice.

Evidence for surprise minimization over value maximization in choice behavior

PubMed Central

Schwartenbeck, Philipp; FitzGerald, Thomas H. B.; Mathys, Christoph; Dolan, Ray; Kronbichler, Martin; Friston, Karl

2015-01-01

Classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents’ to model their environment and minimize surprise about the states they frequent. We propose that choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone. Minimizing surprise makes a prediction at variance with expected utility models; namely, that in addition to attaining valuable states, agents attempt to maximize the entropy over outcomes and thus ‘keep their options open’. We tested this prediction using a simple binary choice paradigm and show that human decision-making is better explained by surprise minimization compared to utility maximization. Furthermore, we replicated this entropy-seeking behavior in a control task with no explicit utilities. These findings highlight a limitation of purely economic motivations in explaining choice behavior and instead emphasize the importance of belief-based motivations. PMID:26564686

Risk management for optimal land use planning integrating ecosystem services values: A case study in Changsha, Middle China.

PubMed

Liang, Jie; Zhong, Minzhou; Zeng, Guangming; Chen, Gaojie; Hua, Shanshan; Li, Xiaodong; Yuan, Yujie; Wu, Haipeng; Gao, Xiang

2017-02-01

Land-use change has direct impact on ecosystem services and alters ecosystem services values (ESVs). Ecosystem services analysis is beneficial for land management and decisions. However, the application of ESVs for decision-making in land use decisions is scarce. In this paper, a method, integrating ESVs to balance future ecosystem-service benefit and risk, is developed to optimize investment in land for ecological conservation in land use planning. Using ecological conservation in land use planning in Changsha as an example, ESVs is regarded as the expected ecosystem-service benefit. And uncertainty of land use change is regarded as risk. This method can optimize allocation of investment in land to improve ecological benefit. The result shows that investment should be partial to Liuyang City to get higher benefit. The investment should also be shifted from Liuyang City to other regions to reduce risk. In practice, lower limit and upper limit for weight distribution, which affects optimal outcome and selection of investment allocation, should be set in investment. This method can reveal the optimal spatial allocation of investment to maximize the expected ecosystem-service benefit at a given level of risk or minimize risk at a given level of expected ecosystem-service benefit. Our results of optimal analyses highlight tradeoffs between future ecosystem-service benefit and uncertainty of land use change in land use decisions. Copyright © 2016 Elsevier B.V. All rights reserved.

Natural parameter values for generalized gene adjacency.

PubMed

Yang, Zhenyu; Sankoff, David

2010-09-01

Given the gene orders in two modern genomes, it may be difficult to decide if some genes are close enough in both genomes to infer some ancestral proximity or some functional relationship. Current methods all depend on arbitrary parameters. We explore a class of gene proximity criteria and find two kinds of natural values for their parameters. One kind has to do with the parameter value where the expected information contained in two genomes about each other is maximized. The other kind of natural value has to do with parameter values beyond which all genes are clustered. We analyze these using combinatorial and probabilistic arguments as well as simulations.

Object-based change detection method using refined Markov random field

NASA Astrophysics Data System (ADS)

Peng, Daifeng; Zhang, Yongjun

2017-01-01

In order to fully consider the local spatial constraints between neighboring objects in object-based change detection (OBCD), an OBCD approach is presented by introducing a refined Markov random field (MRF). First, two periods of images are stacked and segmented to produce image objects. Second, object spectral and textual histogram features are extracted and G-statistic is implemented to measure the distance among different histogram distributions. Meanwhile, object heterogeneity is calculated by combining spectral and textual histogram distance using adaptive weight. Third, an expectation-maximization algorithm is applied for determining the change category of each object and the initial change map is then generated. Finally, a refined change map is produced by employing the proposed refined object-based MRF method. Three experiments were conducted and compared with some state-of-the-art unsupervised OBCD methods to evaluate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method obtains the highest accuracy among the methods used in this paper, which confirms its validness and effectiveness in OBCD.

C-arm technique using distance driven method for nephrolithiasis and kidney stones detection

NASA Astrophysics Data System (ADS)

Malalla, Nuhad; Sun, Pengfei; Chen, Ying; Lipkin, Michael E.; Preminger, Glenn M.; Qin, Jun

2016-04-01

Distance driven represents a state of art method that used for reconstruction for x-ray techniques. C-arm tomography is an x-ray imaging technique that provides three dimensional information of the object by moving the C-shaped gantry around the patient. With limited view angle, C-arm system was investigated to generate volumetric data of the object with low radiation dosage and examination time. This paper is a new simulation study with two reconstruction methods based on distance driven including: simultaneous algebraic reconstruction technique (SART) and Maximum Likelihood expectation maximization (MLEM). Distance driven is an efficient method that has low computation cost and free artifacts compared with other methods such as ray driven and pixel driven methods. Projection images of spherical objects were simulated with a virtual C-arm system with a total view angle of 40 degrees. Results show the ability of limited angle C-arm technique to generate three dimensional images with distance driven reconstruction.

Estimation for general birth-death processes

PubMed Central

Crawford, Forrest W.; Minin, Vladimir N.; Suchard, Marc A.

2013-01-01

Birth-death processes (BDPs) are continuous-time Markov chains that track the number of “particles” in a system over time. While widely used in population biology, genetics and ecology, statistical inference of the instantaneous particle birth and death rates remains largely limited to restrictive linear BDPs in which per-particle birth and death rates are constant. Researchers often observe the number of particles at discrete times, necessitating data augmentation procedures such as expectation-maximization (EM) to find maximum likelihood estimates. For BDPs on finite state-spaces, there are powerful matrix methods for computing the conditional expectations needed for the E-step of the EM algorithm. For BDPs on infinite state-spaces, closed-form solutions for the E-step are available for some linear models, but most previous work has resorted to time-consuming simulation. Remarkably, we show that the E-step conditional expectations can be expressed as convolutions of computable transition probabilities for any general BDP with arbitrary rates. This important observation, along with a convenient continued fraction representation of the Laplace transforms of the transition probabilities, allows for novel and efficient computation of the conditional expectations for all BDPs, eliminating the need for truncation of the state-space or costly simulation. We use this insight to derive EM algorithms that yield maximum likelihood estimation for general BDPs characterized by various rate models, including generalized linear models. We show that our Laplace convolution technique outperforms competing methods when they are available and demonstrate a technique to accelerate EM algorithm convergence. We validate our approach using synthetic data and then apply our methods to cancer cell growth and estimation of mutation parameters in microsatellite evolution. PMID:25328261

Estimation for general birth-death processes.

PubMed

Crawford, Forrest W; Minin, Vladimir N; Suchard, Marc A

2014-04-01

Birth-death processes (BDPs) are continuous-time Markov chains that track the number of "particles" in a system over time. While widely used in population biology, genetics and ecology, statistical inference of the instantaneous particle birth and death rates remains largely limited to restrictive linear BDPs in which per-particle birth and death rates are constant. Researchers often observe the number of particles at discrete times, necessitating data augmentation procedures such as expectation-maximization (EM) to find maximum likelihood estimates. For BDPs on finite state-spaces, there are powerful matrix methods for computing the conditional expectations needed for the E-step of the EM algorithm. For BDPs on infinite state-spaces, closed-form solutions for the E-step are available for some linear models, but most previous work has resorted to time-consuming simulation. Remarkably, we show that the E-step conditional expectations can be expressed as convolutions of computable transition probabilities for any general BDP with arbitrary rates. This important observation, along with a convenient continued fraction representation of the Laplace transforms of the transition probabilities, allows for novel and efficient computation of the conditional expectations for all BDPs, eliminating the need for truncation of the state-space or costly simulation. We use this insight to derive EM algorithms that yield maximum likelihood estimation for general BDPs characterized by various rate models, including generalized linear models. We show that our Laplace convolution technique outperforms competing methods when they are available and demonstrate a technique to accelerate EM algorithm convergence. We validate our approach using synthetic data and then apply our methods to cancer cell growth and estimation of mutation parameters in microsatellite evolution.

Maximal sfermion flavour violation in super-GUTs

DOE PAGES

Ellis, John; Olive, Keith A.; Velasco-Sevilla, Liliana

2016-10-20

We consider supersymmetric grand unified theories with soft supersymmetry-breaking scalar masses m 0 specified above the GUT scale (super-GUTs) and patterns of Yukawa couplings motivated by upper limits on flavour-changing interactions beyond the Standard Model. If the scalar masses are smaller than the gaugino masses m 1/2, as is expected in no-scale models, the dominant effects of renormalisation between the input scale and the GUT scale are generally expected to be those due to the gauge couplings, which are proportional to m 1/2 and generation independent. In this case, the input scalar masses m 0 may violate flavour maximally, amore » scenario we call MaxSFV, and there is no supersymmetric flavour problem. As a result, we illustrate this possibility within various specific super-GUT scenarios that are deformations of no-scale gravity« less

Time Series Modeling of Nano-Gold Immunochromatographic Assay via Expectation Maximization Algorithm.

PubMed

Zeng, Nianyin; Wang, Zidong; Li, Yurong; Du, Min; Cao, Jie; Liu, Xiaohui

2013-12-01

In this paper, the expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines. The model for the nano-GICA is developed as the stochastic dynamic model that consists of a first-order autoregressive stochastic dynamic process and a noisy measurement. By using the EM algorithm, the model parameters, the actual signal intensities of the test and control lines, as well as the noise intensity can be identified simultaneously. Three different time series data sets concerning the target concentrations are employed to demonstrate the effectiveness of the introduced algorithm. Several indices are also proposed to evaluate the inferred models. It is shown that the model fits the data very well.

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology.

PubMed

Jara-Ettinger, Julian; Gweon, Hyowon; Schulz, Laura E; Tenenbaum, Joshua B

2016-08-01

We propose that human social cognition is structured around a basic understanding of ourselves and others as intuitive utility maximizers: from a young age, humans implicitly assume that agents choose goals and actions to maximize the rewards they expect to obtain relative to the costs they expect to incur. This 'naïve utility calculus' allows both children and adults observe the behavior of others and infer their beliefs and desires, their longer-term knowledge and preferences, and even their character: who is knowledgeable or competent, who is praiseworthy or blameworthy, who is friendly, indifferent, or an enemy. We review studies providing support for the naïve utility calculus, and we show how it captures much of the rich social reasoning humans engage in from infancy. Copyright © 2016 Elsevier Ltd. All rights reserved.

Speeded Reaching Movements around Invisible Obstacles

PubMed Central

Hudson, Todd E.; Wolfe, Uta; Maloney, Laurence T.

2012-01-01

We analyze the problem of obstacle avoidance from a Bayesian decision-theoretic perspective using an experimental task in which reaches around a virtual obstacle were made toward targets on an upright monitor. Subjects received monetary rewards for touching the target and incurred losses for accidentally touching the intervening obstacle. The locations of target-obstacle pairs within the workspace were varied from trial to trial. We compared human performance to that of a Bayesian ideal movement planner (who chooses motor strategies maximizing expected gain) using the Dominance Test employed in Hudson et al. (2007). The ideal movement planner suffers from the same sources of noise as the human, but selects movement plans that maximize expected gain in the presence of that noise. We find good agreement between the predictions of the model and actual performance in most but not all experimental conditions. PMID:23028276

Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation-Maximization (SAEM) Algorithm.

PubMed

Chow, Sy-Miin; Lu, Zhaohua; Sherwood, Andrew; Zhu, Hongtu

2016-03-01

The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed.

Multimodal Event Detection in Twitter Hashtag Networks

DOE PAGES

Yilmaz, Yasin; Hero, Alfred O.

2016-07-01

In this study, event detection in a multimodal Twitter dataset is considered. We treat the hashtags in the dataset as instances with two modes: text and geolocation features. The text feature consists of a bag-of-words representation. The geolocation feature consists of geotags (i.e., geographical coordinates) of the tweets. Fusing the multimodal data we aim to detect, in terms of topic and geolocation, the interesting events and the associated hashtags. To this end, a generative latent variable model is assumed, and a generalized expectation-maximization (EM) algorithm is derived to learn the model parameters. The proposed method is computationally efficient, and lendsmore » itself to big datasets. Lastly, experimental results on a Twitter dataset from August 2014 show the efficacy of the proposed method.« less

Sampling-based ensemble segmentation against inter-operator variability

NASA Astrophysics Data System (ADS)

Huo, Jing; Okada, Kazunori; Pope, Whitney; Brown, Matthew

2011-03-01

Inconsistency and a lack of reproducibility are commonly associated with semi-automated segmentation methods. In this study, we developed an ensemble approach to improve reproducibility and applied it to glioblastoma multiforme (GBM) brain tumor segmentation on T1-weigted contrast enhanced MR volumes. The proposed approach combines samplingbased simulations and ensemble segmentation into a single framework; it generates a set of segmentations by perturbing user initialization and user-specified internal parameters, then fuses the set of segmentations into a single consensus result. Three combination algorithms were applied: majority voting, averaging and expectation-maximization (EM). The reproducibility of the proposed framework was evaluated by a controlled experiment on 16 tumor cases from a multicenter drug trial. The ensemble framework had significantly better reproducibility than the individual base Otsu thresholding method (p<.001).

Characterization of computer network events through simultaneous feature selection and clustering of intrusion alerts

NASA Astrophysics Data System (ADS)

Chen, Siyue; Leung, Henry; Dondo, Maxwell

2014-05-01

As computer network security threats increase, many organizations implement multiple Network Intrusion Detection Systems (NIDS) to maximize the likelihood of intrusion detection and provide a comprehensive understanding of intrusion activities. However, NIDS trigger a massive number of alerts on a daily basis. This can be overwhelming for computer network security analysts since it is a slow and tedious process to manually analyse each alert produced. Thus, automated and intelligent clustering of alerts is important to reveal the structural correlation of events by grouping alerts with common features. As the nature of computer network attacks, and therefore alerts, is not known in advance, unsupervised alert clustering is a promising approach to achieve this goal. We propose a joint optimization technique for feature selection and clustering to aggregate similar alerts and to reduce the number of alerts that analysts have to handle individually. More precisely, each identified feature is assigned a binary value, which reflects the feature's saliency. This value is treated as a hidden variable and incorporated into a likelihood function for clustering. Since computing the optimal solution of the likelihood function directly is analytically intractable, we use the Expectation-Maximisation (EM) algorithm to iteratively update the hidden variable and use it to maximize the expected likelihood. Our empirical results, using a labelled Defense Advanced Research Projects Agency (DARPA) 2000 reference dataset, show that the proposed method gives better results than the EM clustering without feature selection in terms of the clustering accuracy.

Novel hybrid GPU-CPU implementation of parallelized Monte Carlo parametric expectation maximization estimation method for population pharmacokinetic data analysis.

PubMed

Ng, C M

2013-10-01

The development of a population PK/PD model, an essential component for model-based drug development, is both time- and labor-intensive. A graphical-processing unit (GPU) computing technology has been proposed and used to accelerate many scientific computations. The objective of this study was to develop a hybrid GPU-CPU implementation of parallelized Monte Carlo parametric expectation maximization (MCPEM) estimation algorithm for population PK data analysis. A hybrid GPU-CPU implementation of the MCPEM algorithm (MCPEMGPU) and identical algorithm that is designed for the single CPU (MCPEMCPU) were developed using MATLAB in a single computer equipped with dual Xeon 6-Core E5690 CPU and a NVIDIA Tesla C2070 GPU parallel computing card that contained 448 stream processors. Two different PK models with rich/sparse sampling design schemes were used to simulate population data in assessing the performance of MCPEMCPU and MCPEMGPU. Results were analyzed by comparing the parameter estimation and model computation times. Speedup factor was used to assess the relative benefit of parallelized MCPEMGPU over MCPEMCPU in shortening model computation time. The MCPEMGPU consistently achieved shorter computation time than the MCPEMCPU and can offer more than 48-fold speedup using a single GPU card. The novel hybrid GPU-CPU implementation of parallelized MCPEM algorithm developed in this study holds a great promise in serving as the core for the next-generation of modeling software for population PK/PD analysis.

A new statistical approach to climate change detection and attribution

NASA Astrophysics Data System (ADS)

Ribes, Aurélien; Zwiers, Francis W.; Azaïs, Jean-Marc; Naveau, Philippe

2017-01-01

We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the " models are statistically indistinguishable from the truth" paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951-2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 ± 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings (-0.01± 0.02 K).

Algorithm for parametric community detection in networks.

PubMed

Bettinelli, Andrea; Hansen, Pierre; Liberti, Leo

2012-07-01

Modularity maximization is extensively used to detect communities in complex networks. It has been shown, however, that this method suffers from a resolution limit: Small communities may be undetectable in the presence of larger ones even if they are very dense. To alleviate this defect, various modifications of the modularity function have been proposed as well as multiresolution methods. In this paper we systematically study a simple model (proposed by Pons and Latapy [Theor. Comput. Sci. 412, 892 (2011)] and similar to the parametric model of Reichardt and Bornholdt [Phys. Rev. E 74, 016110 (2006)]) with a single parameter α that balances the fraction of within community edges and the expected fraction of edges according to the configuration model. An exact algorithm is proposed to find optimal solutions for all values of α as well as the corresponding successive intervals of α values for which they are optimal. This algorithm relies upon a routine for exact modularity maximization and is limited to moderate size instances. An agglomerative hierarchical heuristic is therefore proposed to address parametric modularity detection in large networks. At each iteration the smallest value of α for which it is worthwhile to merge two communities of the current partition is found. Then merging is performed and the data are updated accordingly. An implementation is proposed with the same time and space complexity as the well-known Clauset-Newman-Moore (CNM) heuristic [Phys. Rev. E 70, 066111 (2004)]. Experimental results on artificial and real world problems show that (i) communities are detected by both exact and heuristic methods for all values of the parameter α; (ii) the dendrogram summarizing the results of the heuristic method provides a useful tool for substantive analysis, as illustrated particularly on a Les Misérables data set; (iii) the difference between the parametric modularity values given by the exact method and those given by the heuristic is moderate; (iv) the heuristic version of the proposed parametric method, viewed as a modularity maximization tool, gives better results than the CNM heuristic for large instances.

The value of value of information: best informing research design and prioritization using current methods.

PubMed

Eckermann, Simon; Karnon, Jon; Willan, Andrew R

2010-01-01

Value of information (VOI) methods have been proposed as a systematic approach to inform optimal research design and prioritization. Four related questions arise that VOI methods could address. (i) Is further research for a health technology assessment (HTA) potentially worthwhile? (ii) Is the cost of a given research design less than its expected value? (iii) What is the optimal research design for an HTA? (iv) How can research funding be best prioritized across alternative HTAs? Following Occam's razor, we consider the usefulness of VOI methods in informing questions 1-4 relative to their simplicity of use. Expected value of perfect information (EVPI) with current information, while simple to calculate, is shown to provide neither a necessary nor a sufficient condition to address question 1, given that what EVPI needs to exceed varies with the cost of research design, which can vary from very large down to negligible. Hence, for any given HTA, EVPI does not discriminate, as it can be large and further research not worthwhile or small and further research worthwhile. In contrast, each of questions 1-4 are shown to be fully addressed (necessary and sufficient) where VOI methods are applied to maximize expected value of sample information (EVSI) minus expected costs across designs. In comparing complexity in use of VOI methods, applying the central limit theorem (CLT) simplifies analysis to enable easy estimation of EVSI and optimal overall research design, and has been shown to outperform bootstrapping, particularly with small samples. Consequently, VOI methods applying the CLT to inform optimal overall research design satisfy Occam's razor in both improving decision making and reducing complexity. Furthermore, they enable consideration of relevant decision contexts, including option value and opportunity cost of delay, time, imperfect implementation and optimal design across jurisdictions. More complex VOI methods such as bootstrapping of the expected value of partial EVPI may have potential value in refining overall research design. However, Occam's razor must be seriously considered in application of these VOI methods, given their increased complexity and current limitations in informing decision making, with restriction to EVPI rather than EVSI and not allowing for important decision-making contexts. Initial use of CLT methods to focus these more complex partial VOI methods towards where they may be useful in refining optimal overall trial design is suggested. Integrating CLT methods with such partial VOI methods to allow estimation of partial EVSI is suggested in future research to add value to the current VOI toolkit.

Optimization of Multiple Related Negotiation through Multi-Negotiation Network

NASA Astrophysics Data System (ADS)

Ren, Fenghui; Zhang, Minjie; Miao, Chunyan; Shen, Zhiqi

In this paper, a Multi-Negotiation Network (MNN) and a Multi- Negotiation Influence Diagram (MNID) are proposed to optimally handle Multiple Related Negotiations (MRN) in a multi-agent system. Most popular, state-of-the-art approaches perform MRN sequentially. However, a sequential procedure may not optimally execute MRN in terms of maximizing the global outcome, and may even lead to unnecessary losses in some situations. The motivation of this research is to use a MNN to handle MRN concurrently so as to maximize the expected utility of MRN. Firstly, both the joint success rate and the joint utility by considering all related negotiations are dynamically calculated based on a MNN. Secondly, by employing a MNID, an agent's possible decision on each related negotiation is reflected by the value of expected utility. Lastly, through comparing expected utilities between all possible policies to conduct MRN, an optimal policy is generated to optimize the global outcome of MRN. The experimental results indicate that the proposed approach can improve the global outcome of MRN in a successful end scenario, and avoid unnecessary losses in an unsuccessful end scenario.

Uncertainty, robustness, and the value of information in managing an expanding Arctic goose population

USGS Publications Warehouse

Johnson, Fred A.; Jensen, Gitte H.; Madsen, Jesper; Williams, Byron K.

2014-01-01

We explored the application of dynamic-optimization methods to the problem of pink-footed goose (Anser brachyrhynchus) management in western Europe. We were especially concerned with the extent to which uncertainty in population dynamics influenced an optimal management strategy, the gain in management performance that could be expected if uncertainty could be eliminated or reduced, and whether an adaptive or robust management strategy might be most appropriate in the face of uncertainty. We combined three alternative survival models with three alternative reproductive models to form a set of nine annual-cycle models for pink-footed geese. These models represent a wide range of possibilities concerning the extent to which demographic rates are density dependent or independent, and the extent to which they are influenced by spring temperatures. We calculated state-dependent harvest strategies for these models using stochastic dynamic programming and an objective function that maximized sustainable harvest, subject to a constraint on desired population size. As expected, attaining the largest mean objective value (i.e., the relative measure of management performance) depended on the ability to match a model-dependent optimal strategy with its generating model of population dynamics. The nine models suggested widely varying objective values regardless of the harvest strategy, with the density-independent models generally producing higher objective values than models with density-dependent survival. In the face of uncertainty as to which of the nine models is most appropriate, the optimal strategy assuming that both survival and reproduction were a function of goose abundance and spring temperatures maximized the expected minimum objective value (i.e., maxi–min). In contrast, the optimal strategy assuming equal model weights minimized the expected maximum loss in objective value. The expected value of eliminating model uncertainty was an increase in objective value of only 3.0%. This value represents the difference between the best that could be expected if the most appropriate model were known and the best that could be expected in the face of model uncertainty. The value of eliminating uncertainty about the survival process was substantially higher than that associated with the reproductive process, which is consistent with evidence that variation in survival is more important than variation in reproduction in relatively long-lived avian species. Comparing the expected objective value if the most appropriate model were known with that of the maxi–min robust strategy, we found the value of eliminating uncertainty to be an expected increase of 6.2% in objective value. This result underscores the conservatism of the maxi–min rule and suggests that risk-neutral managers would prefer the optimal strategy that maximizes expected value, which is also the strategy that is expected to minimize the maximum loss (i.e., a strategy based on equal model weights). The low value of information calculated for pink-footed geese suggests that a robust strategy (i.e., one in which no learning is anticipated) could be as nearly effective as an adaptive one (i.e., a strategy in which the relative credibility of models is assessed through time). Of course, an alternative explanation for the low value of information is that the set of population models we considered was too narrow to represent key uncertainties in population dynamics. Yet we know that questions about the presence of density dependence must be central to the development of a sustainable harvest strategy. And while there are potentially many environmental covariates that could help explain variation in survival or reproduction, our admission of models in which vital rates are drawn randomly from reasonable distributions represents a worst-case scenario for management. We suspect that much of the value of the various harvest strategies we calculated is derived from the fact that they are state dependent, such that appropriate harvest rates depend on population abundance and weather conditions, as well as our focus on an infinite time horizon for sustainability.

Optimal Resource Allocation in Library Systems

ERIC Educational Resources Information Center

Rouse, William B.

1975-01-01

Queueing theory is used to model processes as either waiting or balking processes. The optimal allocation of resources to these processes is defined as that which maximizes the expected value of the decision-maker's utility function. (Author)

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pichara, Karim; Protopapas, Pavlos

We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks and a probabilistic graphical model that allows us to perform inference to predict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incomplete data, we use an iterative algorithm that utilizes sampling methods and expectation maximization to estimate the distributions and probabilistic dependencies of variables from data with missing values. To test our model, we use three catalogs with missing data (SAGE, Two Micron All Sky Survey, and UBVI) and one complete catalog (MACHO). We examine howmore » classification accuracy changes when information from missing data catalogs is included, how our method compares to traditional missing data approaches, and at what computational cost. Integrating these catalogs with missing data, we find that classification of variable objects improves by a few percent and by 15% for quasar detection while keeping the computational cost the same.« less

Sparse Bayesian learning for DOA estimation with mutual coupling.

PubMed

Dai, Jisheng; Hu, Nan; Xu, Weichao; Chang, Chunqi

2015-10-16

Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.

The Dynamics of Crime and Punishment

NASA Astrophysics Data System (ADS)

Hausken, Kjell; Moxnes, John F.

This article analyzes crime development which is one of the largest threats in today's world, frequently referred to as the war on crime. The criminal commits crimes in his free time (when not in jail) according to a non-stationary Poisson process which accounts for fluctuations. Expected values and variances for crime development are determined. The deterrent effect of imprisonment follows from the amount of time in imprisonment. Each criminal maximizes expected utility defined as expected benefit (from crime) minus expected cost (imprisonment). A first-order differential equation of the criminal's utility-maximizing response to the given punishment policy is then developed. The analysis shows that if imprisonment is absent, criminal activity grows substantially. All else being equal, any equilibrium is unstable (labile), implying growth of criminal activity, unless imprisonment increases sufficiently as a function of criminal activity. This dynamic approach or perspective is quite interesting and has to our knowledge not been presented earlier. The empirical data material for crime intensity and imprisonment for Norway, England and Wales, and the US supports the model. Future crime development is shown to depend strongly on the societally chosen imprisonment policy. The model is intended as a valuable tool for policy makers who can envision arbitrarily sophisticated imprisonment functions and foresee the impact they have on crime development.

Acceptable regret in medical decision making.

PubMed

Djulbegovic, B; Hozo, I; Schwartz, A; McMasters, K M

1999-09-01

When faced with medical decisions involving uncertain outcomes, the principles of decision theory hold that we should select the option with the highest expected utility to maximize health over time. Whether a decision proves right or wrong can be learned only in retrospect, when it may become apparent that another course of action would have been preferable. This realization may bring a sense of loss, or regret. When anticipated regret is compelling, a decision maker may choose to violate expected utility theory to avoid regret. We formulate a concept of acceptable regret in medical decision making that explicitly introduces the patient's attitude toward loss of health due to a mistaken decision into decision making. In most cases, minimizing expected regret results in the same decision as maximizing expected utility. However, when acceptable regret is taken into consideration, the threshold probability below which we can comfortably withhold treatment is a function only of the net benefit of the treatment, and the threshold probability above which we can comfortably administer the treatment depends only on the magnitude of the risks associated with the therapy. By considering acceptable regret, we develop new conceptual relations that can help decide whether treatment should be withheld or administered, especially when the diagnosis is uncertain. This may be particularly beneficial in deciding what constitutes futile medical care.

Body composition of university students by hydrostatic weighing and skinfold measurement.

PubMed

Jürimäe, T; Jagomägi, G; Lepp, T

1992-12-01

The body composition of 124 male and 70 female Tartu University students was measured by three different methods: hydrostatic weighing by maximal expiration, hydrostatic weighing by maximal inspiration and subcutaneous fat thickness measurements. Our results show that the proposed body density measuring method by maximal expiration is simple, reliable and applicable not only in indoor swimming pools but in field conditions as well. The second new hydrostatic weighing apparatus is more comfortable for the subjects where the body density is measured at maximal inspiration. The mean body density of males was somewhat higher when measured by the maximal inspiration (1.066 +/- 0.012 g.ml-1) than when measured by the maximal expiration (1.063 +/- 0.009 g.ml-1, p < 0.05). For females, on the contrary, the maximal expiration method (1.044 +/- 0.010 g.ml-1) yielded a higher body density value than when measured by the maximal inspiration (1.040 +/- 0.011 g.ml-1, p > 0.05). The body fat percentage measured by skinfold thickness correlated significantly with the body fat percentage calculated by body density by maximal expiration (males r = 0.420, females r = 0.531) and inspiration (males r = 0.507, females r = 0.663). We must conclude that the presented two methods of measuring body density offer new possibilities for densitometric analysis without the need for expensive laboratory equipment.

Maximization, learning, and economic behavior

PubMed Central

Erev, Ido; Roth, Alvin E.

2014-01-01

The rationality assumption that underlies mainstream economic theory has proved to be a useful approximation, despite the fact that systematic violations to its predictions can be found. That is, the assumption of rational behavior is useful in understanding the ways in which many successful economic institutions function, although it is also true that actual human behavior falls systematically short of perfect rationality. We consider a possible explanation of this apparent inconsistency, suggesting that mechanisms that rest on the rationality assumption are likely to be successful when they create an environment in which the behavior they try to facilitate leads to the best payoff for all agents on average, and most of the time. Review of basic learning research suggests that, under these conditions, people quickly learn to maximize expected return. This review also shows that there are many situations in which experience does not increase maximization. In many cases, experience leads people to underweight rare events. In addition, the current paper suggests that it is convenient to distinguish between two behavioral approaches to improve economic analyses. The first, and more conventional approach among behavioral economists and psychologists interested in judgment and decision making, highlights violations of the rational model and proposes descriptive models that capture these violations. The second approach studies human learning to clarify the conditions under which people quickly learn to maximize expected return. The current review highlights one set of conditions of this type and shows how the understanding of these conditions can facilitate market design. PMID:25024182

Maximization, learning, and economic behavior.

PubMed

Erev, Ido; Roth, Alvin E

2014-07-22

The rationality assumption that underlies mainstream economic theory has proved to be a useful approximation, despite the fact that systematic violations to its predictions can be found. That is, the assumption of rational behavior is useful in understanding the ways in which many successful economic institutions function, although it is also true that actual human behavior falls systematically short of perfect rationality. We consider a possible explanation of this apparent inconsistency, suggesting that mechanisms that rest on the rationality assumption are likely to be successful when they create an environment in which the behavior they try to facilitate leads to the best payoff for all agents on average, and most of the time. Review of basic learning research suggests that, under these conditions, people quickly learn to maximize expected return. This review also shows that there are many situations in which experience does not increase maximization. In many cases, experience leads people to underweight rare events. In addition, the current paper suggests that it is convenient to distinguish between two behavioral approaches to improve economic analyses. The first, and more conventional approach among behavioral economists and psychologists interested in judgment and decision making, highlights violations of the rational model and proposes descriptive models that capture these violations. The second approach studies human learning to clarify the conditions under which people quickly learn to maximize expected return. The current review highlights one set of conditions of this type and shows how the understanding of these conditions can facilitate market design.

Partial volume correction of PET-imaged tumor heterogeneity using expectation maximization with a spatially varying point spread function

PubMed Central

Barbee, David L; Flynn, Ryan T; Holden, James E; Nickles, Robert J; Jeraj, Robert

2010-01-01

Tumor heterogeneities observed in positron emission tomography (PET) imaging are frequently compromised of partial volume effects which may affect treatment prognosis, assessment, or future implementations such as biologically optimized treatment planning (dose painting). This paper presents a method for partial volume correction of PET-imaged heterogeneous tumors. A point source was scanned on a GE Discover LS at positions of increasing radii from the scanner’s center to obtain the spatially varying point spread function (PSF). PSF images were fit in three dimensions to Gaussian distributions using least squares optimization. Continuous expressions were devised for each Gaussian width as a function of radial distance, allowing for generation of the system PSF at any position in space. A spatially varying partial volume correction (SV-PVC) technique was developed using expectation maximization (EM) and a stopping criterion based on the method’s correction matrix generated for each iteration. The SV-PVC was validated using a standard tumor phantom and a tumor heterogeneity phantom, and was applied to a heterogeneous patient tumor. SV-PVC results were compared to results obtained from spatially invariant partial volume correction (SINV-PVC), which used directionally uniform three dimensional kernels. SV-PVC of the standard tumor phantom increased the maximum observed sphere activity by 55 and 40% for 10 and 13 mm diameter spheres, respectively. Tumor heterogeneity phantom results demonstrated that as net changes in the EM correction matrix decreased below 35%, further iterations improved overall quantitative accuracy by less than 1%. SV-PVC of clinically observed tumors frequently exhibited changes of ±30% in regions of heterogeneity. The SV-PVC method implemented spatially varying kernel widths and automatically determined the number of iterations for optimal restoration, parameters which are arbitrarily chosen in SINV-PVC. Comparing SV-PVC to SINV-PVC demonstrated that similar results could be reached using both methods, but large differences result for the arbitrary selection of SINV-PVC parameters. The presented SV-PVC method was performed without user intervention, requiring only a tumor mask as input. Research involving PET-imaged tumor heterogeneity should include correcting for partial volume effects to improve the quantitative accuracy of results. PMID:20009194

Development of robust and multi-mode control of tearing in DIII-D

DOE PAGES

Welander, A. S.; La Haye, R.J.; Humphreys, D. A.; ...

2016-06-02

Neoclassical tearing modes (NTMs) are instabilities that can produce undesirable magnetic islands in tokamak plasmas. They can be stabilized by applying electron cyclotron current drive (ECCD) at the island. The NTM control system on DIII-D can now control multiple modes. Each of 6 mirrors that reflect ECCD beams into the plasma can be assigned to different surfaces in the plasma where NTMs are unstable. The control system then steers the mirrors to keep the beams aimed at the surfaces. The system routinely stabilizes one NTM preemptively and has now also been used to control two modes in the same discharge.more » With the “catch-and-subdue” function, ECCD-generating gyrotrons can be turned on when NTMs appear and off after suppression. Newly triggered NTMs can be promptly suppressed if mode onset is detected early and ECCD immediately applied. Early mode detection is achieved in this paper by spectral analysis of Mirnov probes with a band-pass filter for the expected mode frequency. Targeted surfaces are tracked by equilibrium reconstructions (that include measurements of the motional Stark effect). The ECCD position is tracked by ray-tracing using the TORBEAM code. Several techniques are being explored for fine-tuning alignment when NTMs occur. One method adjusts ECCD alignment in steps until the island decays fast enough. A second method sweeps the alignment to find the optimum. A third method pulses gyrotrons and uses electron cyclotron emission to compare where the resulting temperature pulses are relative to temperature fluctuations from a rotating NTM. NTM control in ITER is expected to use active profile regulation to maximize controllability, followed by repeated catch-and-subdue actions if modes are retriggered, in order to maintain island size below the disruptive threshold while maximizing confinement and fusion gain. Between events, real-time tracking will be performed to maintain alignment and readiness for subsequent catch-andsubdue actions. Methods for active probing of stability boundaries will be studied as possible diagnostics for the profile regulation. Finally, selected elements of this ITER NTM control vision will be discussed and assessed.« less

Phenomenology of maximal and near-maximal lepton mixing

NASA Astrophysics Data System (ADS)

Gonzalez-Garcia, M. C.; Peña-Garay, Carlos; Nir, Yosef; Smirnov, Alexei Yu.

2001-01-01

The possible existence of maximal or near-maximal lepton mixing constitutes an intriguing challenge for fundamental theories of flavor. We study the phenomenological consequences of maximal and near-maximal mixing of the electron neutrino with other (x=tau and/or muon) neutrinos. We describe the deviations from maximal mixing in terms of a parameter ɛ≡1-2 sin2 θex and quantify the present experimental status for \\|ɛ\\|<0.3. We show that both probabilities and observables depend on ɛ quadratically when effects are due to vacuum oscillations and they depend on ɛ linearly if matter effects dominate. The most important information on νe mixing comes from solar neutrino experiments. We find that the global analysis of solar neutrino data allows maximal mixing with confidence level better than 99% for 10-8 eV2<~Δm2<~2×10-7 eV2. In the mass ranges Δm2>~1.5×10-5 eV2 and 4×10-10 eV2<~Δm2<~2×10-7 eV2 the full interval \\|ɛ\\|<0.3 is allowed within ~4σ (99.995% CL) We suggest ways to measure ɛ in future experiments. The observable that is most sensitive to ɛ is the rate [NC]/[CC] in combination with the day-night asymmetry in the SNO detector. With theoretical and statistical uncertainties, the expected accuracy after 5 years is Δɛ~0.07. We also discuss the effects of maximal and near-maximal νe mixing in atmospheric neutrinos, supernova neutrinos, and neutrinoless double beta decay.

Assessing park-and-ride impacts.

DOT National Transportation Integrated Search

2010-06-01

Efficient transportation systems are vital to quality-of-life and mobility issues, and an effective park-and-ride (P&R) : network can help maximize system performance. Properly placed P&R facilities are expected to result in fewer calls : to increase...

Three faces of node importance in network epidemiology: Exact results for small graphs

NASA Astrophysics Data System (ADS)

Holme, Petter

2017-12-01

We investigate three aspects of the importance of nodes with respect to susceptible-infectious-removed (SIR) disease dynamics: influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size), and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that (i) node separation is more important than centrality for more than one active node, (ii) vaccination and influence maximization are the most different aspects of importance, and (iii) the three aspects are more similar when the infection rate is low.

Global biomass production potentials exceed expected future demand without the need for cropland expansion

PubMed Central

Mauser, Wolfram; Klepper, Gernot; Zabel, Florian; Delzeit, Ruth; Hank, Tobias; Putzenlechner, Birgitta; Calzadilla, Alvaro

2015-01-01

Global biomass demand is expected to roughly double between 2005 and 2050. Current studies suggest that agricultural intensification through optimally managed crops on today's cropland alone is insufficient to satisfy future demand. In practice though, improving crop growth management through better technology and knowledge almost inevitably goes along with (1) improving farm management with increased cropping intensity and more annual harvests where feasible and (2) an economically more efficient spatial allocation of crops which maximizes farmers' profit. By explicitly considering these two factors we show that, without expansion of cropland, today's global biomass potentials substantially exceed previous estimates and even 2050s' demands. We attribute 39% increase in estimated global production potentials to increasing cropping intensities and 30% to the spatial reallocation of crops to their profit-maximizing locations. The additional potentials would make cropland expansion redundant. Their geographic distribution points at possible hotspots for future intensification. PMID:26558436

Global biomass production potentials exceed expected future demand without the need for cropland expansion.

PubMed

Mauser, Wolfram; Klepper, Gernot; Zabel, Florian; Delzeit, Ruth; Hank, Tobias; Putzenlechner, Birgitta; Calzadilla, Alvaro

2015-11-12

Global biomass demand is expected to roughly double between 2005 and 2050. Current studies suggest that agricultural intensification through optimally managed crops on today's cropland alone is insufficient to satisfy future demand. In practice though, improving crop growth management through better technology and knowledge almost inevitably goes along with (1) improving farm management with increased cropping intensity and more annual harvests where feasible and (2) an economically more efficient spatial allocation of crops which maximizes farmers' profit. By explicitly considering these two factors we show that, without expansion of cropland, today's global biomass potentials substantially exceed previous estimates and even 2050s' demands. We attribute 39% increase in estimated global production potentials to increasing cropping intensities and 30% to the spatial reallocation of crops to their profit-maximizing locations. The additional potentials would make cropland expansion redundant. Their geographic distribution points at possible hotspots for future intensification.

Expected Power-Utility Maximization Under Incomplete Information and with Cox-Process Observations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fujimoto, Kazufumi, E-mail: m_fuji@kvj.biglobe.ne.jp; Nagai, Hideo, E-mail: nagai@sigmath.es.osaka-u.ac.jp; Runggaldier, Wolfgang J., E-mail: runggal@math.unipd.it

2013-02-15

We consider the problem of maximization of expected terminal power utility (risk sensitive criterion). The underlying market model is a regime-switching diffusion model where the regime is determined by an unobservable factor process forming a finite state Markov process. The main novelty is due to the fact that prices are observed and the portfolio is rebalanced only at random times corresponding to a Cox process where the intensity is driven by the unobserved Markovian factor process as well. This leads to a more realistic modeling for many practical situations, like in markets with liquidity restrictions; on the other hand itmore » considerably complicates the problem to the point that traditional methodologies cannot be directly applied. The approach presented here is specific to the power-utility. For log-utilities a different approach is presented in Fujimoto et al. (Preprint, 2012).« less

Royal Darwinian Demons: Enforced Changes in Reproductive Efforts Do Not Affect the Life Expectancy of Ant Queens.

PubMed

Schrempf, Alexandra; Giehr, Julia; Röhrl, Ramona; Steigleder, Sarah; Heinze, Jürgen

2017-04-01

One of the central tenets of life-history theory is that organisms cannot simultaneously maximize all fitness components. This results in the fundamental trade-off between reproduction and life span known from numerous animals, including humans. Social insects are a well-known exception to this rule: reproductive queens outlive nonreproductive workers. Here, we take a step forward and show that under identical social and environmental conditions the fecundity-longevity trade-off is absent also within the queen caste. A change in reproduction did not alter life expectancy, and even a strong enforced increase in reproductive efforts did not reduce residual life span. Generally, egg-laying rate and life span were positively correlated. Queens of perennial social insects thus seem to maximize at the same time two fitness parameters that are normally negatively correlated. Even though they are not immortal, they best approach a hypothetical "Darwinian demon" in the animal kingdom.

WFIRST: Exoplanet Target Selection and Scheduling with Greedy Optimization

NASA Astrophysics Data System (ADS)

Keithly, Dean; Garrett, Daniel; Delacroix, Christian; Savransky, Dmitry

2018-01-01

We present target selection and scheduling algorithms for missions with direct imaging of exoplanets, and the Wide Field Infrared Survey Telescope (WFIRST) in particular, which will be equipped with a coronagraphic instrument (CGI). Optimal scheduling of CGI targets can maximize the expected value of directly imaged exoplanets (completeness). Using target completeness as a reward metric and integration time plus overhead time as a cost metric, we can maximize the sum completeness for a mission with a fixed duration. We optimize over these metrics to create a list of target stars using a greedy optimization algorithm based off altruistic yield optimization (AYO) under ideal conditions. We simulate full missions using EXOSIMS by observing targets in this list for their predetermined integration times. In this poster, we report the theoretical maximum sum completeness, mean number of detected exoplanets from Monte Carlo simulations, and the ideal expected value of the simulated missions.

An Enumerative Combinatorics Model for Fragmentation Patterns in RNA Sequencing Provides Insights into Nonuniformity of the Expected Fragment Starting-Point and Coverage Profile.

PubMed

Prakash, Celine; Haeseler, Arndt Von

2017-03-01

RNA sequencing (RNA-seq) has emerged as the method of choice for measuring the expression of RNAs in a given cell population. In most RNA-seq technologies, sequencing the full length of RNA molecules requires fragmentation into smaller pieces. Unfortunately, the issue of nonuniform sequencing coverage across a genomic feature has been a concern in RNA-seq and is attributed to biases for certain fragments in RNA-seq library preparation and sequencing. To investigate the expected coverage obtained from fragmentation, we develop a simple fragmentation model that is independent of bias from the experimental method and is not specific to the transcript sequence. Essentially, we enumerate all configurations for maximal placement of a given fragment length, F, on transcript length, T, to represent every possible fragmentation pattern, from which we compute the expected coverage profile across a transcript. We extend this model to incorporate general empirical attributes such as read length, fragment length distribution, and number of molecules of the transcript. We further introduce the fragment starting-point, fragment coverage, and read coverage profiles. We find that the expected profiles are not uniform and that factors such as fragment length to transcript length ratio, read length to fragment length ratio, fragment length distribution, and number of molecules influence the variability of coverage across a transcript. Finally, we explore a potential application of the model where, with simulations, we show that it is possible to correctly estimate the transcript copy number for any transcript in the RNA-seq experiment.

An Enumerative Combinatorics Model for Fragmentation Patterns in RNA Sequencing Provides Insights into Nonuniformity of the Expected Fragment Starting-Point and Coverage Profile

PubMed Central

Haeseler, Arndt Von

2017-01-01

Abstract RNA sequencing (RNA-seq) has emerged as the method of choice for measuring the expression of RNAs in a given cell population. In most RNA-seq technologies, sequencing the full length of RNA molecules requires fragmentation into smaller pieces. Unfortunately, the issue of nonuniform sequencing coverage across a genomic feature has been a concern in RNA-seq and is attributed to biases for certain fragments in RNA-seq library preparation and sequencing. To investigate the expected coverage obtained from fragmentation, we develop a simple fragmentation model that is independent of bias from the experimental method and is not specific to the transcript sequence. Essentially, we enumerate all configurations for maximal placement of a given fragment length, F, on transcript length, T, to represent every possible fragmentation pattern, from which we compute the expected coverage profile across a transcript. We extend this model to incorporate general empirical attributes such as read length, fragment length distribution, and number of molecules of the transcript. We further introduce the fragment starting-point, fragment coverage, and read coverage profiles. We find that the expected profiles are not uniform and that factors such as fragment length to transcript length ratio, read length to fragment length ratio, fragment length distribution, and number of molecules influence the variability of coverage across a transcript. Finally, we explore a potential application of the model where, with simulations, we show that it is possible to correctly estimate the transcript copy number for any transcript in the RNA-seq experiment. PMID:27661099

A multi-objective decision-making approach to the journal submission problem.

PubMed

Wong, Tony E; Srikrishnan, Vivek; Hadka, David; Keller, Klaus

2017-01-01

When researchers complete a manuscript, they need to choose a journal to which they will submit the study. This decision requires to navigate trade-offs between multiple objectives. One objective is to share the new knowledge as widely as possible. Citation counts can serve as a proxy to quantify this objective. A second objective is to minimize the time commitment put into sharing the research, which may be estimated by the total time from initial submission to final decision. A third objective is to minimize the number of rejections and resubmissions. Thus, researchers often consider the trade-offs between the objectives of (i) maximizing citations, (ii) minimizing time-to-decision, and (iii) minimizing the number of resubmissions. To complicate matters further, this is a decision with multiple, potentially conflicting, decision-maker rationalities. Co-authors might have different preferences, for example about publishing fast versus maximizing citations. These diverging preferences can lead to conflicting trade-offs between objectives. Here, we apply a multi-objective decision analytical framework to identify the Pareto-front between these objectives and determine the set of journal submission pathways that balance these objectives for three stages of a researcher's career. We find multiple strategies that researchers might pursue, depending on how they value minimizing risk and effort relative to maximizing citations. The sequences that maximize expected citations within each strategy are generally similar, regardless of time horizon. We find that the "conditional impact factor"-impact factor times acceptance rate-is a suitable heuristic method for ranking journals, to strike a balance between minimizing effort objectives and maximizing citation count. Finally, we examine potential co-author tension resulting from differing rationalities by mapping out each researcher's preferred Pareto front and identifying compromise submission strategies. The explicit representation of trade-offs, especially when multiple decision-makers (co-authors) have different preferences, facilitates negotiations and can support the decision process.

A multi-objective decision-making approach to the journal submission problem

PubMed Central

Hadka, David; Keller, Klaus

2017-01-01

When researchers complete a manuscript, they need to choose a journal to which they will submit the study. This decision requires to navigate trade-offs between multiple objectives. One objective is to share the new knowledge as widely as possible. Citation counts can serve as a proxy to quantify this objective. A second objective is to minimize the time commitment put into sharing the research, which may be estimated by the total time from initial submission to final decision. A third objective is to minimize the number of rejections and resubmissions. Thus, researchers often consider the trade-offs between the objectives of (i) maximizing citations, (ii) minimizing time-to-decision, and (iii) minimizing the number of resubmissions. To complicate matters further, this is a decision with multiple, potentially conflicting, decision-maker rationalities. Co-authors might have different preferences, for example about publishing fast versus maximizing citations. These diverging preferences can lead to conflicting trade-offs between objectives. Here, we apply a multi-objective decision analytical framework to identify the Pareto-front between these objectives and determine the set of journal submission pathways that balance these objectives for three stages of a researcher’s career. We find multiple strategies that researchers might pursue, depending on how they value minimizing risk and effort relative to maximizing citations. The sequences that maximize expected citations within each strategy are generally similar, regardless of time horizon. We find that the “conditional impact factor”—impact factor times acceptance rate—is a suitable heuristic method for ranking journals, to strike a balance between minimizing effort objectives and maximizing citation count. Finally, we examine potential co-author tension resulting from differing rationalities by mapping out each researcher’s preferred Pareto front and identifying compromise submission strategies. The explicit representation of trade-offs, especially when multiple decision-makers (co-authors) have different preferences, facilitates negotiations and can support the decision process. PMID:28582430

Frequency assignments for HFDF receivers in a search and rescue network

NASA Astrophysics Data System (ADS)

Johnson, Krista E.

1990-03-01

This thesis applies a multiobjective linear programming approach to the problem of assigning frequencies to high frequency direction finding (HFDF) receivers in a search-and-rescue network in order to maximize the expected number of geolocations of vessels in distress. The problem is formulated as a multiobjective integer linear programming problem. The integrality of the solutions is guaranteed by the totally unimodularity of the A-matrix. Two approaches are taken to solve the multiobjective linear programming problem: (1) the multiobjective simplex method as implemented in ADBASE; and (2) an iterative approach. In this approach, the individual objective functions are weighted and combined in a single additive objective function. The resulting single objective problem is expressed as a network programming problem and solved using SAS NETFLOW. The process is then repeated with different weightings for the objective functions. The solutions obtained from the multiobjective linear programs are evaluated using a FORTRAN program to determine which solution provides the greatest expected number of geolocations. This solution is then compared to the sample mean and standard deviation for the expected number of geolocations resulting from 10,000 random frequency assignments for the network.

Statistical reconstruction for cosmic ray muon tomography.

PubMed

Schultz, Larry J; Blanpied, Gary S; Borozdin, Konstantin N; Fraser, Andrew M; Hengartner, Nicolas W; Klimenko, Alexei V; Morris, Christopher L; Orum, Chris; Sossong, Michael J

2007-08-01

Highly penetrating cosmic ray muons constantly shower the earth at a rate of about 1 muon per cm2 per minute. We have developed a technique which exploits the multiple Coulomb scattering of these particles to perform nondestructive inspection without the use of artificial radiation. In prior work [1]-[3], we have described heuristic methods for processing muon data to create reconstructed images. In this paper, we present a maximum likelihood/expectation maximization tomographic reconstruction algorithm designed for the technique. This algorithm borrows much from techniques used in medical imaging, particularly emission tomography, but the statistics of muon scattering dictates differences. We describe the statistical model for multiple scattering, derive the reconstruction algorithm, and present simulated examples. We also propose methods to improve the robustness of the algorithm to experimental errors and events departing from the statistical model.

Maximizing investments in work zone safety in Oregon : final report.

DOT National Transportation Integrated Search

2011-05-01

Due to the federal stimulus program and the 2009 Jobs and Transportation Act, the Oregon Department of Transportation (ODOT) anticipates that a large increase in highway construction will occur. There is the expectation that, since transportation saf...

Leadership Strategies.

ERIC Educational Resources Information Center

Lashway, Larry

1997-01-01

Principals today are expected to maximize their schools' performances with limited resources while also adopting educational innovations. This synopsis reviews five recent publications that offer some important insights about the nature of principals' leadership strategies: (1) "Leadership Styles and Strategies" (Larry Lashway); (2) "Facilitative…

Aging management guideline for commercial nuclear power plants - heat exchangers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Booker, S.; Lehnert, D.; Daavettila, N.

1994-06-01

This Aging Management Guideline (AMG) describes recommended methods for effective detection and mitigation of age-related degradation mechanisms in commercial nuclear power plant heat exchangers important to license renewal. The intent of this AMG is to assist plant maintenance and operations personnel in maximizing the safe, useful life of these components. It also supports the documentation of effective aging management programs required under the License Renewal Rule 10 CFR 54. This AMG is presented in a manner that allows personnel responsible for performance analysis and maintenance to compare their plant-specific aging mechanisms (expected or already experienced) and aging management program activitiesmore » to the more generic results and recommendations presented herein.« less

A quasi-likelihood approach to non-negative matrix factorization

PubMed Central

Devarajan, Karthik; Cheung, Vincent C.K.

2017-01-01

A unified approach to non-negative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framework and provides a unified view of such factorizations from the perspective of quasi-likelihood. Using this framework, a family of algorithms for handling signal-dependent noise is developed and its convergence proven using the Expectation-Maximization algorithm. In addition, a measure to evaluate the goodness-of-fit of the resulting factorization is described. The proposed methods allow modeling of non-linear effects via appropriate link functions and are illustrated using an application in biomedical signal processing. PMID:27348511

Analysis of Modified SMI Method for Adaptive Array Weight Control. M.S. Thesis

NASA Technical Reports Server (NTRS)

Dilsavor, Ronald Louis

1989-01-01

An adaptive array is used to receive a desired signal in the presence of weak interference signals which need to be suppressed. A modified sample matrix inversion (SMI) algorithm controls the array weights. The modification leads to increased interference suppression by subtracting a fraction of the noise power from the diagonal elements of the covariance matrix. The modified algorithm maximizes an intuitive power ratio criterion. The expected values and variances of the array weights, output powers, and power ratios as functions of the fraction and the number of snapshots are found and compared to computer simulation and real experimental array performance. Reduced-rank covariance approximations and errors in the estimated covariance are also described.

Multiresolution 3-D reconstruction from side-scan sonar images.

PubMed

Coiras, Enrique; Petillot, Yvan; Lane, David M

2007-02-01

In this paper, a new method for the estimation of seabed elevation maps from side-scan sonar images is presented. The side-scan image formation process is represented by a Lambertian diffuse model, which is then inverted by a multiresolution optimization procedure inspired by expectation-maximization to account for the characteristics of the imaged seafloor region. On convergence of the model, approximations for seabed reflectivity, side-scan beam pattern, and seabed altitude are obtained. The performance of the system is evaluated against a real structure of known dimensions. Reconstruction results for images acquired by different sonar sensors are presented. Applications to augmented reality for the simulation of targets in sonar imagery are also discussed.

Three-dimensional ordered-subset expectation maximization iterative protocol for evaluation of left ventricular volumes and function by quantitative gated SPECT: a dynamic phantom study.

PubMed

Ceriani, Luca; Ruberto, Teresa; Delaloye, Angelika Bischof; Prior, John O; Giovanella, Luca

2010-03-01

The purposes of this study were to characterize the performance of a 3-dimensional (3D) ordered-subset expectation maximization (OSEM) algorithm in the quantification of left ventricular (LV) function with (99m)Tc-labeled agent gated SPECT (G-SPECT), the QGS program, and a beating-heart phantom and to optimize the reconstruction parameters for clinical applications. A G-SPECT image of a dynamic heart phantom simulating the beating left ventricle was acquired. The exact volumes of the phantom were known and were as follows: end-diastolic volume (EDV) of 112 mL, end-systolic volume (ESV) of 37 mL, and stroke volume (SV) of 75 mL; these volumes produced an LV ejection fraction (LVEF) of 67%. Tomographic reconstructions were obtained after 10-20 iterations (I) with 4, 8, and 16 subsets (S) at full width at half maximum (FWHM) gaussian postprocessing filter cutoff values of 8-15 mm. The QGS program was used for quantitative measurements. Measured values ranged from 72 to 92 mL for EDV, from 18 to 32 mL for ESV, and from 54 to 63 mL for SV, and the calculated LVEF ranged from 65% to 76%. Overall, the combination of 10 I, 8 S, and a cutoff filter value of 10 mm produced the most accurate results. The plot of the measures with respect to the expectation maximization-equivalent iterations (I x S product) revealed a bell-shaped curve for the LV volumes and a reverse distribution for the LVEF, with the best results in the intermediate range. In particular, FWHM cutoff values exceeding 10 mm affected the estimation of the LV volumes. The QGS program is able to correctly calculate the LVEF when used in association with an optimized 3D OSEM algorithm (8 S, 10 I, and FWHM of 10 mm) but underestimates the LV volumes. However, various combinations of technical parameters, including a limited range of I and S (80-160 expectation maximization-equivalent iterations) and low cutoff values (< or =10 mm) for the gaussian postprocessing filter, produced results with similar accuracies and without clinically relevant differences in the LV volumes and the estimated LVEF.

Automated Identification of the Heart Wall Throughout the Entire Cardiac Cycle Using Optimal Cardiac Phase for Extracted Features

NASA Astrophysics Data System (ADS)

Takahashi, Hiroki; Hasegawa, Hideyuki; Kanai, Hiroshi

2011-07-01

In most methods for evaluation of cardiac function based on echocardiography, the heart wall is currently identified manually by an operator. However, this task is very time-consuming and suffers from inter- and intraobserver variability. The present paper proposes a method that uses multiple features of ultrasonic echo signals for automated identification of the heart wall region throughout an entire cardiac cycle. In addition, the optimal cardiac phase to select a frame of interest, i.e., the frame for the initiation of tracking, was determined. The heart wall region at the frame of interest in this cardiac phase was identified by the expectation-maximization (EM) algorithm, and heart wall regions in the following frames were identified by tracking each point classified in the initial frame as the heart wall region using the phased tracking method. The results for two subjects indicate the feasibility of the proposed method in the longitudinal axis view of the heart.

Half-blind remote sensing image restoration with partly unknown degradation

NASA Astrophysics Data System (ADS)

Xie, Meihua; Yan, Fengxia

2017-01-01

The problem of image restoration has been extensively studied for its practical importance and theoretical interest. This paper mainly discusses the problem of image restoration with partly unknown kernel. In this model, the degraded kernel function is known but its parameters are unknown. With this model, we should estimate the parameters in Gaussian kernel and the real image simultaneity. For this new problem, a total variation restoration model is put out and an intersect direction iteration algorithm is designed. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) are used to measure the performance of the method. Numerical results show that we can estimate the parameters in kernel accurately, and the new method has both much higher PSNR and much higher SSIM than the expectation maximization (EM) method in many cases. In addition, the accuracy of estimation is not sensitive to noise. Furthermore, even though the support of the kernel is unknown, we can also use this method to get accurate estimation.

A systematic desaturation method for images from the Atmospheric Imaging Assembly in the Solar Dynamics Observatory.

NASA Astrophysics Data System (ADS)

Torre, Gabriele; Schwartz, Richard; Piana, Michele; Massone, Anna Maria; Benvenuto, Federico

2016-05-01

The fine spatial resolution of the SDO AIA CCD's is often destroyed by the charge in saturated pixels overflowing into a swath of neighboring cells during fast rising solar flares. Automated exposure control can only mitigate this issue to a degree and it has other deleterious effects. Our method addresses the desaturation problem for AIA images as an image reconstruction problem in which the information content of the diffraction fringes, generated by the interaction between the incoming radiation and the hardware of the spacecraft, is exploited to recover the true image intensities within the primary saturated core of the image. This methodology takes advantage of some well defined techniques like cross-correlation and the Expectation Maximization method to invert the direct relation between the diffraction fringes intensities and the true flux intensities. During this talk a complete overview on the structure of the method will be provided, besides some reliability tests obtained by its application against synthetic and real data.

On the Achievable Throughput Over TVWS Sensor Networks

PubMed Central

Caleffi, Marcello; Cacciapuoti, Angela Sara

2016-01-01

In this letter, we study the throughput achievable by an unlicensed sensor network operating over TV white space spectrum in presence of coexistence interference. Through the letter, we first analytically derive the achievable throughput as a function of the channel ordering. Then, we show that the problem of deriving the maximum expected throughput through exhaustive search is computationally unfeasible. Finally, we derive a computational-efficient algorithm characterized by polynomial-time complexity to compute the channel set maximizing the expected throughput and, stemming from this, we derive a closed-form expression of the maximum expected throughput. Numerical simulations validate the theoretical analysis. PMID:27043565

A new numerical method for calculating extrema of received power for polarimetric SAR

USGS Publications Warehouse

Zhang, Y.; Zhang, Jiahua; Lu, Z.; Gong, W.

2009-01-01

A numerical method called cross-step iteration is proposed to calculate the maximal/minimal received power for polarized imagery based on a target's Kennaugh matrix. This method is much more efficient than the systematic method, which searches for the extrema of received power by varying the polarization ellipse angles of receiving and transmitting polarizations. It is also more advantageous than the Schuler method, which has been adopted by the PolSARPro package, because the cross-step iteration method requires less computation time and can derive both the maximal and minimal received powers, whereas the Schuler method is designed to work out only the maximal received power. The analytical model of received-power optimization indicates that the first eigenvalue of the Kennaugh matrix is the supremum of the maximal received power. The difference between these two parameters reflects the depolarization effect of the target's backscattering, which might be useful for target discrimination. ?? 2009 IEEE.

Estimation of snow in extratropical cyclones from multiple frequency airborne radar observations. An Expectation-Maximization approach

NASA Astrophysics Data System (ADS)

Grecu, M.; Tian, L.; Heymsfield, G. M.

2017-12-01

A major challenge in deriving accurate estimates of physical properties of falling snow particles from single frequency space- or airborne radar observations is that snow particles exhibit a large variety of shapes and their electromagnetic scattering characteristics are highly dependent on these shapes. Triple frequency (Ku-Ka-W) radar observations are expected to facilitate the derivation of more accurate snow estimates because specific snow particle shapes tend to have specific signatures in the associated two-dimensional dual-reflectivity-ratio (DFR) space. However, the derivation of accurate snow estimates from triple frequency radar observations is by no means a trivial task. This is because the radar observations can be subject to non-negligible attenuation (especially at W-band when super-cooled water is present), which may significantly impact the interpretation of the information in the DFR space. Moreover, the electromagnetic scattering properties of snow particles are computationally expensive to derive, which makes the derivation of reliable parameterizations usable in estimation methodologies challenging. In this study, we formulate an two-step Expectation Maximization (EM) methodology to derive accurate snow estimates in Extratropical Cyclones (ECTs) from triple frequency airborne radar observations. The Expectation (E) step consists of a least-squares triple frequency estimation procedure applied with given assumptions regarding the relationships between the density of snow particles and their sizes, while the Maximization (M) step consists of the optimization of the assumptions used in step E. The electromagnetic scattering properties of snow particles are derived using the Rayleigh-Gans approximation. The methodology is applied to triple frequency radar observations collected during the Olympic Mountains Experiment (OLYMPEX). Results show that snowfall estimates above the freezing level in ETCs consistent with the triple frequency radar observations as well as with independent rainfall estimates below the freezing level may be derived using the EM methodology formulated in the study.

Expecting ankle tilts and wearing an ankle brace influence joint control in an imitated ankle sprain mechanism during walking.

PubMed

Gehring, Dominic; Wissler, Sabrina; Lohrer, Heinz; Nauck, Tanja; Gollhofer, Albert

2014-03-01

A thorough understanding of the functional aspects of ankle joint control is essential to developing effective injury prevention. It is of special interest to understand how neuromuscular control mechanisms and mechanical constraints stabilize the ankle joint. Therefore, the aim of the present study was to determine how expecting ankle tilts and the application of an ankle brace influence ankle joint control when imitating the ankle sprain mechanism during walking. Ankle kinematics and muscle activity were assessed in 17 healthy men. During gait rapid perturbations were applied using a trapdoor (tilting with 24° inversion and 15° plantarflexion). The subjects either knew that a perturbation would definitely occur (expected tilts) or there was only the possibility that a perturbation would occur (potential tilts). Both conditions were conducted with and without a semi-rigid ankle brace. Expecting perturbations led to an increased ankle eversion at foot contact, which was mediated by an altered muscle preactivation pattern. Moreover, the maximal inversion angle (-7%) and velocity (-4%), as well as the reactive muscle response were significantly reduced when the perturbation was expected. While wearing an ankle brace did not influence muscle preactivation nor the ankle kinematics before ground contact, it significantly reduced the maximal ankle inversion angle (-14%) and velocity (-11%) as well as reactive neuromuscular responses. The present findings reveal that expecting ankle inversion modifies neuromuscular joint control prior to landing. Although such motor control strategies are weaker in their magnitude compared with braces, they seem to assist ankle joint stabilization in a close-to-injury situation. Copyright © 2013 Elsevier B.V. All rights reserved.

Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation-maximization reconstruction

PubMed Central

Karakatsanis, Nicolas A.; Casey, Michael E.; Lodge, Martin A.; Rahmim, Arman; Zaidi, Habib

2016-01-01

Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the tracer influx rate Ki as a complimentary metric to the semi-quantitative standardized uptake value (SUV). The resulting Ki images may suffer from high noise due to the need for short acquisition frames. Meanwhile, a generalized Patlak (gPatlak) WB post-reconstruction method had been suggested to limit Ki bias of sPatlak analysis at regions with non-negligible 18F-FDG uptake reversibility; however, gPatlak analysis is non-linear and thus can further amplify noise. In the present study, we implemented, within the open-source Software for Tomographic Image Reconstruction (STIR) platform, a clinically adoptable 4D WB reconstruction framework enabling efficient estimation of sPatlak and gPatlak images directly from dynamic multi-bed PET raw data with substantial noise reduction. Furthermore, we employed the optimization transfer methodology to accelerate 4D expectation-maximization (EM) convergence by nesting the fast image-based estimation of Patlak parameters within each iteration cycle of the slower projection-based estimation of dynamic PET images. The novel gPatlak 4D method was initialized from an optimized set of sPatlak ML-EM iterations to facilitate EM convergence. Initially, realistic simulations were conducted utilizing published 18F-FDG kinetic parameters coupled with the XCAT phantom. Quantitative analyses illustrated enhanced Ki target-to-background ratio (TBR) and especially contrast-to-noise ratio (CNR) performance for the 4D vs. the indirect methods and static SUV. Furthermore, considerable convergence acceleration was observed for the nested algorithms involving 10–20 sub-iterations. Moreover, systematic reduction in Ki % bias and improved TBR were observed for gPatlak vs. sPatlak. Finally, validation on clinical WB dynamic data demonstrated the clinical feasibility and superior Ki CNR performance for the proposed 4D framework compared to indirect Patlak and SUV imaging. PMID:27383991

Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation-maximization reconstruction

NASA Astrophysics Data System (ADS)

Karakatsanis, Nicolas A.; Casey, Michael E.; Lodge, Martin A.; Rahmim, Arman; Zaidi, Habib

2016-08-01

Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the tracer influx rate K i as a complimentary metric to the semi-quantitative standardized uptake value (SUV). The resulting K i images may suffer from high noise due to the need for short acquisition frames. Meanwhile, a generalized Patlak (gPatlak) WB post-reconstruction method had been suggested to limit K i bias of sPatlak analysis at regions with non-negligible 18F-FDG uptake reversibility; however, gPatlak analysis is non-linear and thus can further amplify noise. In the present study, we implemented, within the open-source software for tomographic image reconstruction platform, a clinically adoptable 4D WB reconstruction framework enabling efficient estimation of sPatlak and gPatlak images directly from dynamic multi-bed PET raw data with substantial noise reduction. Furthermore, we employed the optimization transfer methodology to accelerate 4D expectation-maximization (EM) convergence by nesting the fast image-based estimation of Patlak parameters within each iteration cycle of the slower projection-based estimation of dynamic PET images. The novel gPatlak 4D method was initialized from an optimized set of sPatlak ML-EM iterations to facilitate EM convergence. Initially, realistic simulations were conducted utilizing published 18F-FDG kinetic parameters coupled with the XCAT phantom. Quantitative analyses illustrated enhanced K i target-to-background ratio (TBR) and especially contrast-to-noise ratio (CNR) performance for the 4D versus the indirect methods and static SUV. Furthermore, considerable convergence acceleration was observed for the nested algorithms involving 10-20 sub-iterations. Moreover, systematic reduction in K i % bias and improved TBR were observed for gPatlak versus sPatlak. Finally, validation on clinical WB dynamic data demonstrated the clinical feasibility and superior K i CNR performance for the proposed 4D framework compared to indirect Patlak and SUV imaging.

Simultaneously learning DNA motif along with its position and sequence rank preferences through expectation maximization algorithm.

PubMed

Zhang, ZhiZhuo; Chang, Cheng Wei; Hugo, Willy; Cheung, Edwin; Sung, Wing-Kin

2013-03-01

Although de novo motifs can be discovered through mining over-represented sequence patterns, this approach misses some real motifs and generates many false positives. To improve accuracy, one solution is to consider some additional binding features (i.e., position preference and sequence rank preference). This information is usually required from the user. This article presents a de novo motif discovery algorithm called SEME (sampling with expectation maximization for motif elicitation), which uses pure probabilistic mixture model to model the motif's binding features and uses expectation maximization (EM) algorithms to simultaneously learn the sequence motif, position, and sequence rank preferences without asking for any prior knowledge from the user. SEME is both efficient and accurate thanks to two important techniques: the variable motif length extension and importance sampling. Using 75 large-scale synthetic datasets, 32 metazoan compendium benchmark datasets, and 164 chromatin immunoprecipitation sequencing (ChIP-Seq) libraries, we demonstrated the superior performance of SEME over existing programs in finding transcription factor (TF) binding sites. SEME is further applied to a more difficult problem of finding the co-regulated TF (coTF) motifs in 15 ChIP-Seq libraries. It identified significantly more correct coTF motifs and, at the same time, predicted coTF motifs with better matching to the known motifs. Finally, we show that the learned position and sequence rank preferences of each coTF reveals potential interaction mechanisms between the primary TF and the coTF within these sites. Some of these findings were further validated by the ChIP-Seq experiments of the coTFs. The application is available online.

Aging and loss decision making: increased risk aversion and decreased use of maximizing information, with correlated rationality and value maximization.

PubMed

Kurnianingsih, Yoanna A; Sim, Sam K Y; Chee, Michael W L; Mullette-Gillman, O'Dhaniel A

2015-01-01

We investigated how adult aging specifically alters economic decision-making, focusing on examining alterations in uncertainty preferences (willingness to gamble) and choice strategies (what gamble information influences choices) within both the gains and losses domains. Within each domain, participants chose between certain monetary outcomes and gambles with uncertain outcomes. We examined preferences by quantifying how uncertainty modulates choice behavior as if altering the subjective valuation of gambles. We explored age-related preferences for two types of uncertainty, risk, and ambiguity. Additionally, we explored how aging may alter what information participants utilize to make their choices by comparing the relative utilization of maximizing and satisficing information types through a choice strategy metric. Maximizing information was the ratio of the expected value of the two options, while satisficing information was the probability of winning. We found age-related alterations of economic preferences within the losses domain, but no alterations within the gains domain. Older adults (OA; 61-80 years old) were significantly more uncertainty averse for both risky and ambiguous choices. OA also exhibited choice strategies with decreased use of maximizing information. Within OA, we found a significant correlation between risk preferences and choice strategy. This linkage between preferences and strategy appears to derive from a convergence to risk neutrality driven by greater use of the effortful maximizing strategy. As utility maximization and value maximization intersect at risk neutrality, this result suggests that OA are exhibiting a relationship between enhanced rationality and enhanced value maximization. While there was variability in economic decision-making measures within OA, these individual differences were unrelated to variability within examined measures of cognitive ability. Our results demonstrate that aging alters economic decision-making for losses through changes in both individual preferences and the strategies individuals employ.

Hybrid simulated annealing and its application to optimization of hidden Markov models for visual speech recognition.

PubMed

Lee, Jong-Seok; Park, Cheol Hoon

2010-08-01

We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.

Ionization imaging—A new method to search for 0- ν ββ decay

NASA Astrophysics Data System (ADS)

Chinowski, W.; Goldschmidt, A.; Nygren, D.; Bernstein, A.; Heffner, M.; Millaud, J.

2007-10-01

We present a new method to search for 0- ν ββ decay in 136Xe, the Ionization Imaging Chamber. This concept is based on 3-D track reconstruction by detection of ionization, without avalanche gain, in a novel time projection chamber (TPC) geometry. The rejection efficiency of external charged particle backgrounds is optimized by the realization of a maximal, fully active, closed, and ex post facto variable fiducial surface. Event localization within the fiducial volume and detailed event reconstruction mitigate external neutral particle backgrounds; larger detectors offer higher rejection efficiencies. Energy resolution at the Q-value of 2.5 MeV is expected to be better than 1% FWHM, reducing the potential impact of allowed 2- ν ββ decays. Scaling from ˜25 kg prototype to 1000+ kg target mass is graceful. A new possible methodology for the identification of the daughter barium nucleus is also described.

Generalized Wishart Mixtures for Unsupervised Classification of PolSAR Data

NASA Astrophysics Data System (ADS)

Li, Lan; Chen, Erxue; Li, Zengyuan

2013-01-01

This paper presents an unsupervised clustering algorithm based upon the expectation maximization (EM) algorithm for finite mixture modelling, using the complex wishart probability density function (PDF) for the probabilities. The mixture model enables to consider heterogeneous thematic classes which could not be better fitted by the unimodal wishart distribution. In order to make it fast and robust to calculate, we use the recently proposed generalized gamma distribution (GΓD) for the single polarization intensity data to make the initial partition. Then we use the wishart probability density function for the corresponding sample covariance matrix to calculate the posterior class probabilities for each pixel. The posterior class probabilities are used for the prior probability estimates of each class and weights for all class parameter updates. The proposed method is evaluated and compared with the wishart H-Alpha-A classification. Preliminary results show that the proposed method has better performance.

NOTE: Acceleration of Monte Carlo-based scatter compensation for cardiac SPECT

NASA Astrophysics Data System (ADS)

Sohlberg, A.; Watabe, H.; Iida, H.

2008-07-01

Single proton emission computed tomography (SPECT) images are degraded by photon scatter making scatter compensation essential for accurate reconstruction. Reconstruction-based scatter compensation with Monte Carlo (MC) modelling of scatter shows promise for accurate scatter correction, but it is normally hampered by long computation times. The aim of this work was to accelerate the MC-based scatter compensation using coarse grid and intermittent scatter modelling. The acceleration methods were compared to un-accelerated implementation using MC-simulated projection data of the mathematical cardiac torso (MCAT) phantom modelling 99mTc uptake and clinical myocardial perfusion studies. The results showed that when combined the acceleration methods reduced the reconstruction time for 10 ordered subset expectation maximization (OS-EM) iterations from 56 to 11 min without a significant reduction in image quality indicating that the coarse grid and intermittent scatter modelling are suitable for MC-based scatter compensation in cardiac SPECT.

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

PubMed

Ness, Robert O; Sachs, Karen; Mallick, Parag; Vitek, Olga

2018-06-21

Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

A bi-objective model for robust yard allocation scheduling for outbound containers

NASA Astrophysics Data System (ADS)

Liu, Changchun; Zhang, Canrong; Zheng, Li

2017-01-01

This article examines the yard allocation problem for outbound containers, with consideration of uncertainty factors, mainly including the arrival and operation time of calling vessels. Based on the time buffer inserting method, a bi-objective model is constructed to minimize the total operational cost and to maximize the robustness of fighting against the uncertainty. Due to the NP-hardness of the constructed model, a two-stage heuristic is developed to solve the problem. In the first stage, initial solutions are obtained by a greedy algorithm that looks n-steps ahead with the uncertainty factors set as their respective expected values; in the second stage, based on the solutions obtained in the first stage and with consideration of uncertainty factors, a neighbourhood search heuristic is employed to generate robust solutions that can fight better against the fluctuation of uncertainty factors. Finally, extensive numerical experiments are conducted to test the performance of the proposed method.

Novel edge treatment method for improving the transmission reconstruction quality in Tomographic Gamma Scanning.

PubMed

Han, Miaomiao; Guo, Zhirong; Liu, Haifeng; Li, Qinghua

2018-05-01

Tomographic Gamma Scanning (TGS) is a method used for the nondestructive assay of radioactive wastes. In TGS, the actual irregular edge voxels are regarded as regular cubic voxels in the traditional treatment method. In this study, in order to improve the performance of TGS, a novel edge treatment method is proposed that considers the actual shapes of these voxels. The two different edge voxel treatment methods were compared by computing the pixel-level relative errors and normalized mean square errors (NMSEs) between the reconstructed transmission images and the ideal images. Both methods were coupled with two different interative algorithms comprising Algebraic Reconstruction Technique (ART) with a non-negativity constraint and Maximum Likelihood Expectation Maximization (MLEM). The results demonstrated that the traditional method for edge voxel treatment can introduce significant error and that the real irregular edge voxel treatment method can improve the performance of TGS by obtaining better transmission reconstruction images. With the real irregular edge voxel treatment method, MLEM algorithm and ART algorithm can be comparable when assaying homogenous matrices, but MLEM algorithm is superior to ART algorithm when assaying heterogeneous matrices. Copyright © 2018 Elsevier Ltd. All rights reserved.

Varying-energy CT imaging method based on EM-TV

NASA Astrophysics Data System (ADS)

Chen, Ping; Han, Yan

2016-11-01

For complicated structural components with wide x-ray attenuation ranges, conventional fixed-energy computed tomography (CT) imaging cannot obtain all the structural information. This limitation results in a shortage of CT information because the effective thickness of the components along the direction of x-ray penetration exceeds the limit of the dynamic range of the x-ray imaging system. To address this problem, a varying-energy x-ray CT imaging method is proposed. In this new method, the tube voltage is adjusted several times with the fixed lesser interval. Next, the fusion of grey consistency and logarithm demodulation are applied to obtain full and lower noise projection with a high dynamic range (HDR). In addition, for the noise suppression problem of the analytical method, EM-TV (expectation maximization-total Jvariation) iteration reconstruction is used. In the process of iteration, the reconstruction result obtained at one x-ray energy is used as the initial condition of the next iteration. An accompanying experiment demonstrates that this EM-TV reconstruction can also extend the dynamic range of x-ray imaging systems and provide a higher reconstruction quality relative to the fusion reconstruction method.

Locally adaptive parallel temperature accelerated dynamics method

NASA Astrophysics Data System (ADS)

Shim, Yunsic; Amar, Jacques G.

2010-03-01

The recently-developed temperature-accelerated dynamics (TAD) method [M. Sørensen and A.F. Voter, J. Chem. Phys. 112, 9599 (2000)] along with the more recently developed parallel TAD (parTAD) method [Y. Shim et al, Phys. Rev. B 76, 205439 (2007)] allow one to carry out non-equilibrium simulations over extended time and length scales. The basic idea behind TAD is to speed up transitions by carrying out a high-temperature MD simulation and then use the resulting information to obtain event times at the desired low temperature. In a typical implementation, a fixed high temperature Thigh is used. However, in general one expects that for each configuration there exists an optimal value of Thigh which depends on the particular transition pathways and activation energies for that configuration. Here we present a locally adaptive high-temperature TAD method in which instead of using a fixed Thigh the high temperature is dynamically adjusted in order to maximize simulation efficiency. Preliminary results of the performance obtained from parTAD simulations of Cu/Cu(100) growth using the locally adaptive Thigh method will also be presented.

A multiple scales approach to maximal superintegrability

NASA Astrophysics Data System (ADS)

Gubbiotti, G.; Latini, D.

2018-07-01

In this paper we present a simple, algorithmic test to establish if a Hamiltonian system is maximally superintegrable or not. This test is based on a very simple corollary of a theorem due to Nekhoroshev and on a perturbative technique called the multiple scales method. If the outcome is positive, this test can be used to suggest maximal superintegrability, whereas when the outcome is negative it can be used to disprove it. This method can be regarded as a finite dimensional analog of the multiple scales method as a way to produce soliton equations. We use this technique to show that the real counterpart of a mechanical system found by Jules Drach in 1935 is, in general, not maximally superintegrable. We give some hints on how this approach could be applied to classify maximally superintegrable systems by presenting a direct proof of the well-known Bertrand’s theorem.

Engaging Older Adult Volunteers in National Service

ERIC Educational Resources Information Center

McBride, Amanda Moore; Greenfield, Jennifer C.; Morrow-Howell, Nancy; Lee, Yung Soo; McCrary, Stacey

2012-01-01

Volunteer-based programs are increasingly designed as interventions to affect the volunteers and the beneficiaries of the volunteers' activities. To achieve the intended impacts for both, programs need to leverage the volunteers' engagement by meeting their expectations, retaining them, and maximizing their perceptions of benefits. Programmatic…

Merton's problem for an investor with a benchmark in a Barndorff-Nielsen and Shephard market.

PubMed

Lennartsson, Jan; Lindberg, Carl

2015-01-01

To try to outperform an externally given benchmark with known weights is the most common equity mandate in the financial industry. For quantitative investors, this task is predominantly approached by optimizing their portfolios consecutively over short time horizons with one-period models. We seek in this paper to provide a theoretical justification to this practice when the underlying market is of Barndorff-Nielsen and Shephard type. This is done by verifying that an investor who seeks to maximize her expected terminal exponential utility of wealth in excess of her benchmark will in fact use an optimal portfolio equivalent to the one-period Markowitz mean-variance problem in continuum under the corresponding Black-Scholes market. Further, we can represent the solution to the optimization problem as in Feynman-Kac form. Hence, the problem, and its solution, is analogous to Merton's classical portfolio problem, with the main difference that Merton maximizes expected utility of terminal wealth, not wealth in excess of a benchmark.

Bilevel formulation of a policy design problem considering multiple objectives and incomplete preferences

NASA Astrophysics Data System (ADS)

Hawthorne, Bryant; Panchal, Jitesh H.

2014-07-01

A bilevel optimization formulation of policy design problems considering multiple objectives and incomplete preferences of the stakeholders is presented. The formulation is presented for Feed-in-Tariff (FIT) policy design for decentralized energy infrastructure. The upper-level problem is the policy designer's problem and the lower-level problem is a Nash equilibrium problem resulting from market interactions. The policy designer has two objectives: maximizing the quantity of energy generated and minimizing policy cost. The stakeholders decide on quantities while maximizing net present value and minimizing capital investment. The Nash equilibrium problem in the presence of incomplete preferences is formulated as a stochastic linear complementarity problem and solved using expected value formulation, expected residual minimization formulation, and the Monte Carlo technique. The primary contributions in this article are the mathematical formulation of the FIT policy, the extension of computational policy design problems to multiple objectives, and the consideration of incomplete preferences of stakeholders for policy design problems.

Choosing Fitness-Enhancing Innovations Can Be Detrimental under Fluctuating Environments

PubMed Central

Xue, Julian Z.; Costopoulos, Andre; Guichard, Frederic

2011-01-01

The ability to predict the consequences of one's behavior in a particular environment is a mechanism for adaptation. In the absence of any cost to this activity, we might expect agents to choose behaviors that maximize their fitness, an example of directed innovation. This is in contrast to blind mutation, where the probability of becoming a new genotype is independent of the fitness of the new genotypes. Here, we show that under environments punctuated by rapid reversals, a system with both genetic and cultural inheritance should not always maximize fitness through directed innovation. This is because populations highly accurate at selecting the fittest innovations tend to over-fit the environment during its stable phase, to the point that a rapid environmental reversal can cause extinction. A less accurate population, on the other hand, can track long term trends in environmental change, keeping closer to the time-average of the environment. We use both analytical and agent-based models to explore when this mechanism is expected to occur. PMID:22125601

A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI

PubMed Central

Castillo-Barnes, Diego; Peis, Ignacio; Martínez-Murcia, Francisco J.; Segovia, Fermín; Illán, Ignacio A.; Górriz, Juan M.; Ramírez, Javier; Salas-Gonzalez, Diego

2017-01-01

A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, intensity histograms of White Matter and Gray Matter are not symmetric and they exhibit heavy tails. In this work, we present a hidden Markov random field model with expectation maximization (EM-HMRF) modeling the components using the α-stable distribution. The proposed model is a generalization of the widely used EM-HMRF algorithm with Gaussian distributions. We test the α-stable EM-HMRF model in synthetic data and brain MRI data. The proposed methodology presents two main advantages: Firstly, it is more robust to outliers. Secondly, we obtain similar results than using Gaussian when the Gaussian assumption holds. This approach is able to model the spatial dependence between neighboring voxels in tomographic brain MRI. PMID:29209194

Using return on investment to maximize conservation effectiveness in Argentine grasslands.

PubMed

Murdoch, William; Ranganathan, Jai; Polasky, Stephen; Regetz, James

2010-12-07

The rapid global loss of natural habitats and biodiversity, and limited resources, place a premium on maximizing the expected benefits of conservation actions. The scarcity of information on the fine-grained distribution of species of conservation concern, on risks of loss, and on costs of conservation actions, especially in developing countries, makes efficient conservation difficult. The distribution of ecosystem types (unique ecological communities) is typically better known than species and arguably better represents the entirety of biodiversity than do well-known taxa, so we use conserving the diversity of ecosystem types as our conservation goal. We define conservation benefit to include risk of conversion, spatial effects that reward clumping of habitat, and diminishing returns to investment in any one ecosystem type. Using Argentine grasslands as an example, we compare three strategies: protecting the cheapest land ("minimize cost"), maximizing conservation benefit regardless of cost ("maximize benefit"), and maximizing conservation benefit per dollar ("return on investment"). We first show that the widely endorsed goal of saving some percentage (typically 10%) of a country or habitat type, although it may inspire conservation, is a poor operational goal. It either leads to the accumulation of areas with low conservation benefit or requires infeasibly large sums of money, and it distracts from the real problem: maximizing conservation benefit given limited resources. Second, given realistic budgets, return on investment is superior to the other conservation strategies. Surprisingly, however, over a wide range of budgets, minimizing cost provides more conservation benefit than does the maximize-benefit strategy.

Artificial odor discrimination system using electronic nose and neural networks for the identification of urinary tract infection.

PubMed

Kodogiannis, Vassilis S; Lygouras, John N; Tarczynski, Andrzej; Chowdrey, Hardial S

2008-11-01

Current clinical diagnostics are based on biochemical, immunological, or microbiological methods. However, these methods are operator dependent, time-consuming, expensive, and require special skills, and are therefore, not suitable for point-of-care testing. Recent developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. An electronic nose has been used to detect urinary tract infection from 45 suspected cases that were sent for analysis in a U.K. Public Health Registry. These samples were analyzed by incubation in a volatile generation test tube system for 4-5 h. Two issues are being addressed, including the implementation of an advanced neural network, based on a modified expectation maximization scheme that incorporates a dynamic structure methodology and the concept of a fusion of multiple classifiers dedicated to specific feature parameters. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology.

Bounded influence function based inference in joint modelling of ordinal partial linear model and accelerated failure time model.

PubMed

Chakraborty, Arindom

2016-12-01

A common objective in longitudinal studies is to characterize the relationship between a longitudinal response process and a time-to-event data. Ordinal nature of the response and possible missing information on covariates add complications to the joint model. In such circumstances, some influential observations often present in the data may upset the analysis. In this paper, a joint model based on ordinal partial mixed model and an accelerated failure time model is used, to account for the repeated ordered response and time-to-event data, respectively. Here, we propose an influence function-based robust estimation method. Monte Carlo expectation maximization method-based algorithm is used for parameter estimation. A detailed simulation study has been done to evaluate the performance of the proposed method. As an application, a data on muscular dystrophy among children is used. Robust estimates are then compared with classical maximum likelihood estimates. © The Author(s) 2014.

Bayesian inversion analysis of nonlinear dynamics in surface heterogeneous reactions.

PubMed

Omori, Toshiaki; Kuwatani, Tatsu; Okamoto, Atsushi; Hukushima, Koji

2016-09-01

It is essential to extract nonlinear dynamics from time-series data as an inverse problem in natural sciences. We propose a Bayesian statistical framework for extracting nonlinear dynamics of surface heterogeneous reactions from sparse and noisy observable data. Surface heterogeneous reactions are chemical reactions with conjugation of multiple phases, and they have the intrinsic nonlinearity of their dynamics caused by the effect of surface-area between different phases. We adapt a belief propagation method and an expectation-maximization (EM) algorithm to partial observation problem, in order to simultaneously estimate the time course of hidden variables and the kinetic parameters underlying dynamics. The proposed belief propagation method is performed by using sequential Monte Carlo algorithm in order to estimate nonlinear dynamical system. Using our proposed method, we show that the rate constants of dissolution and precipitation reactions, which are typical examples of surface heterogeneous reactions, as well as the temporal changes of solid reactants and products, were successfully estimated only from the observable temporal changes in the concentration of the dissolved intermediate product.

A maximum likelihood algorithm for genome mapping of cytogenetic loci from meiotic configuration data.

PubMed Central

Reyes-Valdés, M H; Stelly, D M

1995-01-01

Frequencies of meiotic configurations in cytogenetic stocks are dependent on chiasma frequencies in segments defined by centromeres, breakpoints, and telomeres. The expectation maximization algorithm is proposed as a general method to perform maximum likelihood estimations of the chiasma frequencies in the intervals between such locations. The estimates can be translated via mapping functions into genetic maps of cytogenetic landmarks. One set of observational data was analyzed to exemplify application of these methods, results of which were largely concordant with other comparable data. The method was also tested by Monte Carlo simulation of frequencies of meiotic configurations from a monotelodisomic translocation heterozygote, assuming six different sample sizes. The estimate averages were always close to the values given initially to the parameters. The maximum likelihood estimation procedures can be extended readily to other kinds of cytogenetic stocks and allow the pooling of diverse cytogenetic data to collectively estimate lengths of segments, arms, and chromosomes. Images Fig. 1 PMID:7568226

Design Considerations and Experimental Verification of a Rail Brake Armature Based on Linear Induction Motor Technology

NASA Astrophysics Data System (ADS)

Sakamoto, Yasuaki; Kashiwagi, Takayuki; Hasegawa, Hitoshi; Sasakawa, Takashi; Fujii, Nobuo

This paper describes the design considerations and experimental verification of an LIM rail brake armature. In order to generate power and maximize the braking force density despite the limited area between the armature and the rail and the limited space available for installation, we studied a design method that is suitable for designing an LIM rail brake armature; we considered adoption of a ring winding structure. To examine the validity of the proposed design method, we developed a prototype ring winding armature for the rail brakes and examined its electromagnetic characteristics in a dynamic test system with roller rigs. By repeating various tests, we confirmed that unnecessary magnetic field components, which were expected to be present under high speed running condition or when a ring winding armature was used, were not present. Further, the necessary magnetic field component and braking force attained the desired values. These studies have helped us to develop a basic design method that is suitable for designing the LIM rail brake armatures.

Variable selection in semiparametric cure models based on penalized likelihood, with application to breast cancer clinical trials.

PubMed

Liu, Xiang; Peng, Yingwei; Tu, Dongsheng; Liang, Hua

2012-10-30

Survival data with a sizable cure fraction are commonly encountered in cancer research. The semiparametric proportional hazards cure model has been recently used to analyze such data. As seen in the analysis of data from a breast cancer study, a variable selection approach is needed to identify important factors in predicting the cure status and risk of breast cancer recurrence. However, no specific variable selection method for the cure model is available. In this paper, we present a variable selection approach with penalized likelihood for the cure model. The estimation can be implemented easily by combining the computational methods for penalized logistic regression and the penalized Cox proportional hazards models with the expectation-maximization algorithm. We illustrate the proposed approach on data from a breast cancer study. We conducted Monte Carlo simulations to evaluate the performance of the proposed method. We used and compared different penalty functions in the simulation studies. Copyright © 2012 John Wiley & Sons, Ltd.

Light Microscopy at Maximal Precision

NASA Astrophysics Data System (ADS)

Bierbaum, Matthew; Leahy, Brian D.; Alemi, Alexander A.; Cohen, Itai; Sethna, James P.

2017-10-01

Microscopy is the workhorse of the physical and life sciences, producing crisp images of everything from atoms to cells well beyond the capabilities of the human eye. However, the analysis of these images is frequently little more accurate than manual marking. Here, we revolutionize the analysis of microscopy images, extracting all the useful information theoretically contained in a complex microscope image. Using a generic, methodological approach, we extract the information by fitting experimental images with a detailed optical model of the microscope, a method we call parameter extraction from reconstructing images (PERI). As a proof of principle, we demonstrate this approach with a confocal image of colloidal spheres, improving measurements of particle positions and radii by 10-100 times over current methods and attaining the maximum possible accuracy. With this unprecedented accuracy, we measure nanometer-scale colloidal interactions in dense suspensions solely with light microscopy, a previously impossible feat. Our approach is generic and applicable to imaging methods from brightfield to electron microscopy, where we expect accuracies of 1 nm and 0.1 pm, respectively.

Probabilistic segmentation and intensity estimation for microarray images.

PubMed

Gottardo, Raphael; Besag, Julian; Stephens, Matthew; Murua, Alejandro

2006-01-01

We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for complementary DNA microarray experiments. The approach overcomes several limitations of existing methods. In particular, it (a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods, including relatively common 'doughnut-shaped' spots; (b) models the image directly as background plus hybridization intensity, and estimates the two quantities simultaneously, avoiding the common logical error that estimates of foreground may be less than those of the corresponding background if the two are estimated separately; and (c) uses a probabilistic modeling approach to simultaneously perform segmentation and intensity estimation, and to compute spot quality measures. We describe two approaches to parameter estimation: a fast algorithm, based on the expectation-maximization and the iterated conditional modes algorithms, and a fully Bayesian framework. These approaches produce comparable results, and both appear to offer some advantages over other methods. We use an HIV experiment to compare our approach to two commercial software products: Spot and Arrayvision.

Retail food environments in Canada: Maximizing the impact of research, policy and practice.

PubMed

Minaker, Leia M

2016-06-09

Retail food environments are gaining national and international attention as important determinants of population dietary intake. Communities across Canada are beginning to discuss and implement programs and policies to create supportive retail food environments. Three considerations should drive the selection of food environment assessment methods: relevance (What is the problem, and how is it related to dietary outcomes?); resources (What human, time and financial resources are required to undertake an assessment?); and response (How will policy-makers find meaning out of and act on the information gained through the food environment assessment?). Ultimately, food environment assessments should be conducted in the context of stakeholder buy-in and multi-sectoral partnerships, since food environment solutions require multi-sectoral action. Partnerships between public health actors and the food and beverage industry can be challenging, especially when mandates are not aligned. Clarifying the motivations, expectations and roles of all stakeholders takes time but is important if the impact of food environment research, policy and practice is to be maximized. The articles contained in this special supplementary issue describe ongoing food environments research across Canada and fill some of the important gaps in the current body of Canadian food environments literature.

Counting malaria parasites with a two-stage EM based algorithm using crowsourced data.

PubMed

Cabrera-Bean, Margarita; Pages-Zamora, Alba; Diaz-Vilor, Carles; Postigo-Camps, Maria; Cuadrado-Sanchez, Daniel; Luengo-Oroz, Miguel Angel

2017-07-01

Malaria eradication of the worldwide is currently one of the main WHO's global goals. In this work, we focus on the use of human-machine interaction strategies for low-cost fast reliable malaria diagnostic based on a crowdsourced approach. The addressed technical problem consists in detecting spots in images even under very harsh conditions when positive objects are very similar to some artifacts. The clicks or tags delivered by several annotators labeling an image are modeled as a robust finite mixture, and techniques based on the Expectation-Maximization (EM) algorithm are proposed for accurately counting malaria parasites on thick blood smears obtained by microscopic Giemsa-stained techniques. This approach outperforms other traditional methods as it is shown through experimentation with real data.

Determination of maximal amount of minor gases adsorbed in a shale sample by headspace gas chromatography.

PubMed

Zhang, Chun-Yun; Hu, Hui-Chao; Chai, Xin-Sheng; Pan, Lei; Xiao, Xian-Ming

2014-02-07

In this paper, we present a novel method for determining the maximal amount of ethane, a minor gas species, adsorbed in a shale sample. The method is based on the time-dependent release of ethane from shale samples measured by headspace gas chromatography (HS-GC). The study includes a mathematical model for fitting the experimental data, calculating the maximal amount gas adsorbed, and predicting results at other temperatures. The method is a more efficient alternative to the isothermal adsorption method that is in widespread use today. Copyright © 2013 Elsevier B.V. All rights reserved.

Signal-to-noise ratio comparison of encoding methods for hyperpolarized noble gas MRI

NASA Technical Reports Server (NTRS)

Zhao, L.; Venkatesh, A. K.; Albert, M. S.; Panych, L. P.

2001-01-01

Some non-Fourier encoding methods such as wavelet and direct encoding use spatially localized bases. The spatial localization feature of these methods enables optimized encoding for improved spatial and temporal resolution during dynamically adaptive MR imaging. These spatially localized bases, however, have inherently reduced image signal-to-noise ratio compared with Fourier or Hadamad encoding for proton imaging. Hyperpolarized noble gases, on the other hand, have quite different MR properties compared to proton, primarily the nonrenewability of the signal. It could be expected, therefore, that the characteristics of image SNR with respect to encoding method will also be very different from hyperpolarized noble gas MRI compared to proton MRI. In this article, hyperpolarized noble gas image SNRs of different encoding methods are compared theoretically using a matrix description of the encoding process. It is shown that image SNR for hyperpolarized noble gas imaging is maximized for any orthonormal encoding method. Methods are then proposed for designing RF pulses to achieve normalized encoding profiles using Fourier, Hadamard, wavelet, and direct encoding methods for hyperpolarized noble gases. Theoretical results are confirmed with hyperpolarized noble gas MRI experiments. Copyright 2001 Academic Press.

On the Teaching of Portfolio Theory.

ERIC Educational Resources Information Center

Biederman, Daniel K.

1992-01-01

Demonstrates how a simple portfolio problem expressed explicitly as an expected utility maximization problem can be used to instruct students in portfolio theory. Discusses risk aversion, decision making under uncertainty, and the limitations of the traditional mean variance approach. Suggests students may develop a greater appreciation of general…

TIME SHARING WITH AN EXPLICIT PRIORITY QUEUING DISCIPLINE.

DTIC Science & Technology

exponentially distributed service times and an ordered priority queue. Each new arrival buys a position in this queue by offering a non-negative bribe to the...parameters is investigated through numerical examples. Finally, to maximize the expected revenue per unit time accruing from bribes , an optimization

Program Monitoring: Problems and Cases.

ERIC Educational Resources Information Center

Lundin, Edward; Welty, Gordon

Designed as the major component of a comprehensive model of educational management, a behavioral model of decision making is presented that approximates the synoptic model of neoclassical economic theory. The synoptic model defines all possible alternatives and provides a basis for choosing that alternative which maximizes expected utility. The…

A Bayesian Approach to Interactive Retrieval

ERIC Educational Resources Information Center

Tague, Jean M.

1973-01-01

A probabilistic model for interactive retrieval is presented. Bayesian statistical decision theory principles are applied: use of prior and sample information about the relationship of document descriptions to query relevance; maximization of expected value of a utility function, to the problem of optimally restructuring search strategies in an…

Creating an Agent Based Framework to Maximize Information Utility

DTIC Science & Technology

2008-03-01

information utility may be a qualitative description of the information, where one would expect the adjectives low value, fair value , high value. For...operations. Information in this category may have a fair value rating. Finally, many seemingly unrelated events, such as reports of snipers in buildings

Can differences in breast cancer utilities explain disparities in breast cancer care?

PubMed

Schleinitz, Mark D; DePalo, Dina; Blume, Jeffrey; Stein, Michael

2006-12-01

Black, older, and less affluent women are less likely to receive adjuvant breast cancer therapy than their counterparts. Whereas preference contributes to disparities in other health care scenarios, it is unclear if preference explains differential rates of breast cancer care. To ascertain utilities from women of diverse backgrounds for the different stages of, and treatments for, breast cancer and to determine whether a treatment decision modeled from utilities is associated with socio-demographic characteristics. A stratified sample (by age and race) of 156 English-speaking women over 25 years old not currently undergoing breast cancer treatment. We assessed utilities using standard gamble for 5 breast cancer stages, and time-tradeoff for 3 therapeutic modalities. We incorporated each subject's utilities into a Markov model to determine whether her quality-adjusted life expectancy would be maximized with chemotherapy for a hypothetical, current diagnosis of stage II breast cancer. We used logistic regression to determine whether socio-demographic variables were associated with this optimal strategy. Median utilities for the 8 health states were: stage I disease, 0.91 (interquartile range 0.50 to 1.00); stage II, 0.75 (0.26 to 0.99); stage III, 0.51 (0.25 to 0.94); stage IV (estrogen receptor positive), 0.36 (0 to 0.75); stage IV (estrogen receptor negative), 0.40 (0 to 0.79); chemotherapy 0.50 (0 to 0.92); hormonal therapy 0.58 (0 to 1); and radiation therapy 0.83 (0.10 to 1). Utilities for early stage disease and treatment modalities, but not metastatic disease, varied with socio-demographic characteristics. One hundred and twenty-two of 156 subjects had utilities that maximized quality-adjusted life expectancy given stage II breast cancer with chemotherapy. Age over 50, black race, and low household income were associated with at least 5-fold lower odds of maximizing quality-adjusted life expectancy with chemotherapy, whereas women who were married or had a significant other were 4-fold more likely to maximize quality-adjusted life expectancy with chemotherapy. Differences in utility for breast cancer health states may partially explain the lower rate of adjuvant therapy for black, older, and less affluent women. Further work must clarify whether these differences result from health preference alone or reflect women's perceptions of sources of disparity, such as access to care, poor communication with providers, limitations in health knowledge or in obtaining social and workplace support during therapy.

Optimal threshold estimator of a prognostic marker by maximizing a time-dependent expected utility function for a patient-centered stratified medicine.

PubMed

Dantan, Etienne; Foucher, Yohann; Lorent, Marine; Giral, Magali; Tessier, Philippe

2018-06-01

Defining thresholds of prognostic markers is essential for stratified medicine. Such thresholds are mostly estimated from purely statistical measures regardless of patient preferences potentially leading to unacceptable medical decisions. Quality-Adjusted Life-Years are a widely used preferences-based measure of health outcomes. We develop a time-dependent Quality-Adjusted Life-Years-based expected utility function for censored data that should be maximized to estimate an optimal threshold. We performed a simulation study to compare estimated thresholds when using the proposed expected utility approach and purely statistical estimators. Two applications illustrate the usefulness of the proposed methodology which was implemented in the R package ROCt ( www.divat.fr ). First, by reanalysing data of a randomized clinical trial comparing the efficacy of prednisone vs. placebo in patients with chronic liver cirrhosis, we demonstrate the utility of treating patients with a prothrombin level higher than 89%. Second, we reanalyze the data of an observational cohort of kidney transplant recipients: we conclude to the uselessness of the Kidney Transplant Failure Score to adapt the frequency of clinical visits. Applying such a patient-centered methodology may improve future transfer of novel prognostic scoring systems or markers in clinical practice.

Mother doesn't always know best: Maternal wormlion choice of oviposition habitat does not match larval habitat choice.

PubMed

Adar, Shay; Dor, Roi

2018-02-01

Habitat choice is an important decision that influences animals' fitness. Insect larvae are less mobile than the adults. Consequently, the contribution of the maternal choice of habitat to the survival and development of the offspring is considered to be crucial. According to the "preference-performance hypothesis", ovipositing females are expected to choose habitats that will maximize the performance of their offspring. We tested this hypothesis in wormlions (Diptera: Vermileonidae), which are small sand-dwelling insects that dig pit-traps in sandy patches and ambush small arthropods. Larvae prefer relatively deep and obstacle-free sand, and here we tested the habitat preference of the ovipositing female. In contrast to our expectation, ovipositing females showed no clear preference for either a deep sand or obstacle-free habitat, in contrast to the larval choice. This suboptimal female choice led to smaller pits being constructed later by the larvae, which may reduce prey capture success of the larvae. We offer several explanations for this apparently suboptimal female behavior, related either to maximizing maternal rather than offspring fitness, or to constraints on the female's behavior. Female's ovipositing habitat choice may have weaker negative consequences than expected for the offspring, as larvae can partially correct suboptimal maternal choice. Copyright © 2017 Elsevier B.V. All rights reserved.

Surgical versus expectant management in women with an incomplete evacuation of the uterus after treatment with misoprostol for miscarriage: the MisoREST trial

PubMed Central

2013-01-01

Background Medical treatment with misoprostol is a non-invasive and inexpensive treatment option in first trimester miscarriage. However, about 30% of women treated with misoprostol have incomplete evacuation of the uterus. Despite being relatively asymptomatic in most cases, this finding often leads to additional surgical treatment (curettage). A comparison of effectiveness and cost-effectiveness of surgical management versus expectant management is lacking in women with incomplete miscarriage after misoprostol. Methods/Design The proposed study is a multicentre randomized controlled trial that assesses the costs and effects of curettage versus expectant management in women with incomplete evacuation of the uterus after misoprostol treatment for first trimester miscarriage. Eligible women will be randomized, after informed consent, within 24 hours after identification of incomplete evacuation of the uterus by ultrasound scanning. Women are randomly allocated to surgical or expectant management. Curettage is performed within three days after randomization. Primary outcome is the sonographic finding of an empty uterus (maximal diameter of any contents of the uterine cavity < 10 millimeters) six weeks after study entry. Secondary outcomes are patients’ quality of life, surgical outcome parameters, the type and number of re-interventions during the first three months and pregnancy rates and outcome 12 months after study entry. Discussion This trial will provide evidence for the (cost) effectiveness of surgical versus expectant management in women with incomplete evacuation of the uterus after misoprostol treatment for first trimester miscarriage. Trial registration Dutch Trial Register: NTR3110 PMID:23638956

Multiple imputation of rainfall missing data in the Iberian Mediterranean context

NASA Astrophysics Data System (ADS)

Miró, Juan Javier; Caselles, Vicente; Estrela, María José

2017-11-01

Given the increasing need for complete rainfall data networks, in recent years have been proposed diverse methods for filling gaps in observed precipitation series, progressively more advanced that traditional approaches to overcome the problem. The present study has consisted in validate 10 methods (6 linear, 2 non-linear and 2 hybrid) that allow multiple imputation, i.e., fill at the same time missing data of multiple incomplete series in a dense network of neighboring stations. These were applied for daily and monthly rainfall in two sectors in the Júcar River Basin Authority (east Iberian Peninsula), which is characterized by a high spatial irregularity and difficulty of rainfall estimation. A classification of precipitation according to their genetic origin was applied as pre-processing, and a quantile-mapping adjusting as post-processing technique. The results showed in general a better performance for the non-linear and hybrid methods, highlighting that the non-linear PCA (NLPCA) method outperforms considerably the Self Organizing Maps (SOM) method within non-linear approaches. On linear methods, the Regularized Expectation Maximization method (RegEM) was the best, but far from NLPCA. Applying EOF filtering as post-processing of NLPCA (hybrid approach) yielded the best results.

Maximizing the Spread of Influence via Generalized Degree Discount.

PubMed

Wang, Xiaojie; Zhang, Xue; Zhao, Chengli; Yi, Dongyun

2016-01-01

It is a crucial and fundamental issue to identify a small subset of influential spreaders that can control the spreading process in networks. In previous studies, a degree-based heuristic called DegreeDiscount has been shown to effectively identify multiple influential spreaders and has severed as a benchmark method. However, the basic assumption of DegreeDiscount is not adequate, because it treats all the nodes equally without any differences. To consider a general situation in real world networks, a novel heuristic method named GeneralizedDegreeDiscount is proposed in this paper as an effective extension of original method. In our method, the status of a node is defined as a probability of not being influenced by any of its neighbors, and an index generalized discounted degree of one node is presented to measure the expected number of nodes it can influence. Then the spreaders are selected sequentially upon its generalized discounted degree in current network. Empirical experiments are conducted on four real networks, and the results show that the spreaders identified by our approach are more influential than several benchmark methods. Finally, we analyze the relationship between our method and three common degree-based methods.

Maximizing the Spread of Influence via Generalized Degree Discount

PubMed Central

Wang, Xiaojie; Zhang, Xue; Zhao, Chengli; Yi, Dongyun

2016-01-01

It is a crucial and fundamental issue to identify a small subset of influential spreaders that can control the spreading process in networks. In previous studies, a degree-based heuristic called DegreeDiscount has been shown to effectively identify multiple influential spreaders and has severed as a benchmark method. However, the basic assumption of DegreeDiscount is not adequate, because it treats all the nodes equally without any differences. To consider a general situation in real world networks, a novel heuristic method named GeneralizedDegreeDiscount is proposed in this paper as an effective extension of original method. In our method, the status of a node is defined as a probability of not being influenced by any of its neighbors, and an index generalized discounted degree of one node is presented to measure the expected number of nodes it can influence. Then the spreaders are selected sequentially upon its generalized discounted degree in current network. Empirical experiments are conducted on four real networks, and the results show that the spreaders identified by our approach are more influential than several benchmark methods. Finally, we analyze the relationship between our method and three common degree-based methods. PMID:27732681

Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.

PubMed

Yousefi, Siamak; Balasubramanian, Madhusudhanan; Goldbaum, Michael H; Medeiros, Felipe A; Zangwill, Linda M; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Bowd, Christopher

2016-05-01

To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM-progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods. GEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of mean deviation (MD), and visual field index (VFI). Sensitivity and specificity for detecting glaucomatous VFs were 89.9% and 93.8%, respectively, for GEM and 93.0% and 97.0%, respectively, for VIM. Receiver operating characteristic (ROC) curve areas for classifying progressed eyes were 0.82 for VIM-POP, 0.86 for GEM-POP, 0.81 for PoPLR, 0.69 for LR of MD, and 0.76 for LR of VFI. GEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information. Detection of glaucomatous progression can be improved by assessing longitudinal changes in localized patterns of glaucomatous defect identified by unsupervised machine learning.

Correlations between prefrontal neurons form a small-world network that optimizes the generation of multineuron sequences of activity

PubMed Central

Luongo, Francisco J.; Zimmerman, Chris A.; Horn, Meryl E.

2016-01-01

Sequential patterns of prefrontal activity are believed to mediate important behaviors, e.g., working memory, but it remains unclear exactly how they are generated. In accordance with previous studies of cortical circuits, we found that prefrontal microcircuits in young adult mice spontaneously generate many more stereotyped sequences of activity than expected by chance. However, the key question of whether these sequences depend on a specific functional organization within the cortical microcircuit, or emerge simply as a by-product of random interactions between neurons, remains unanswered. We observed that correlations between prefrontal neurons do follow a specific functional organization—they have a small-world topology. However, until now it has not been possible to directly link small-world topologies to specific circuit functions, e.g., sequence generation. Therefore, we developed a novel analysis to address this issue. Specifically, we constructed surrogate data sets that have identical levels of network activity at every point in time but nevertheless represent various network topologies. We call this method shuffling activity to rearrange correlations (SHARC). We found that only surrogate data sets based on the actual small-world functional organization of prefrontal microcircuits were able to reproduce the levels of sequences observed in actual data. As expected, small-world data sets contained many more sequences than surrogate data sets with randomly arranged correlations. Surprisingly, small-world data sets also outperformed data sets in which correlations were maximally clustered. Thus the small-world functional organization of cortical microcircuits, which effectively balances the random and maximally clustered regimes, is optimal for producing stereotyped sequential patterns of activity. PMID:26888108

Probabilistic co-adaptive brain-computer interfacing

NASA Astrophysics Data System (ADS)

Bryan, Matthew J.; Martin, Stefan A.; Cheung, Willy; Rao, Rajesh P. N.

2013-12-01

Objective. Brain-computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. Approach. We introduce a new approach to brain-computer interfacing based on partially observable Markov decision processes (POMDPs). POMDPs provide a principled approach to handling uncertainty and achieving co-adaptation in the following manner: (1) Bayesian inference is used to compute posterior probability distributions (‘beliefs’) over brain and environment state, and (2) actions are selected based on entire belief distributions in order to maximize total expected reward; by employing methods from reinforcement learning, the POMDP’s reward function can be updated over time to allow for co-adaptive behaviour. Main results. We illustrate our approach using a simple non-invasive BCI which optimizes the speed-accuracy trade-off for individual subjects based on the signal-to-noise characteristics of their brain signals. We additionally demonstrate that the POMDP BCI can automatically detect changes in the user’s control strategy and can co-adaptively switch control strategies on-the-fly to maximize expected reward. Significance. Our results suggest that the framework of POMDPs offers a promising approach for designing BCIs that can handle uncertainty in neural signals and co-adapt with the user on an ongoing basis. The fact that the POMDP BCI maintains a probability distribution over the user’s brain state allows a much more powerful form of decision making than traditional BCI approaches, which have typically been based on the output of classifiers or regression techniques. Furthermore, the co-adaptation of the system allows the BCI to make online improvements to its behaviour, adjusting itself automatically to the user’s changing circumstances.

Cognitive Rehabilitation for Schizophrenia and the Putative Role of Motivation and Expectancies

PubMed Central

Velligan, Dawn I.; Kern, Robert S.; Gold, James M.

2006-01-01

Cognitive rehabilitation (CR) approaches seek to enhance cognitive processes or to circumvent cognitive impairments in schizophrenia in an effort to improve functional outcome. In this review we examine the research findings on the 8 evidence-based approaches to cognitive remediation listed in the 2005 Training Grid Outlining Best Practices for Recovery and Improved Outcomes for People With Serious Mental Illness, developed by the American Psychological Association Committee for the Advancement of Professional Practice. Though the approaches vary widely in theoretical orientation and methods of intervention, the results are, for the most part, encouraging. Improvements in attention, memory, and executive functioning have been reported. However, many persons with schizophrenia are more impaired in real-world functioning than one would expect given the magnitude of their cognitive deficits. We may need to look beyond cognition to other targets such as motivation to identify the reasons that many persons with schizophrenia demonstrate such marked levels of disability. Although a number of current CR approaches address motivation to varying degrees, treating motivation as a primary target may be needed to maximize CR outcomes. PMID:16641424

Cognitive rehabilitation for schizophrenia and the putative role of motivation and expectancies.

PubMed

Velligan, Dawn I; Kern, Robert S; Gold, James M

2006-07-01

Cognitive rehabilitation (CR) approaches seek to enhance cognitive processes or to circumvent cognitive impairments in schizophrenia in an effort to improve functional outcome. In this review we examine the research findings on the 8 evidence-based approaches to cognitive remediation listed in the 2005 Training Grid Outlining Best Practices for Recovery and Improved Outcomes for People With Serious Mental Illness, developed by the American Psychological Association Committee for the Advancement of Professional Practice. Though the approaches vary widely in theoretical orientation and methods of intervention, the results are, for the most part, encouraging. Improvements in attention, memory, and executive functioning have been reported. However, many persons with schizophrenia are more impaired in real-world functioning than one would expect given the magnitude of their cognitive deficits. We may need to look beyond cognition to other targets such as motivation to identify the reasons that many persons with schizophrenia demonstrate such marked levels of disability. Although a number of current CR approaches address motivation to varying degrees, treating motivation as a primary target may be needed to maximize CR outcomes.

Allocating dissipation across a molecular machine cycle to maximize flux

PubMed Central

Brown, Aidan I.; Sivak, David A.

2017-01-01

Biomolecular machines consume free energy to break symmetry and make directed progress. Nonequilibrium ATP concentrations are the typical free energy source, with one cycle of a molecular machine consuming a certain number of ATP, providing a fixed free energy budget. Since evolution is expected to favor rapid-turnover machines that operate efficiently, we investigate how this free energy budget can be allocated to maximize flux. Unconstrained optimization eliminates intermediate metastable states, indicating that flux is enhanced in molecular machines with fewer states. When maintaining a set number of states, we show that—in contrast to previous findings—the flux-maximizing allocation of dissipation is not even. This result is consistent with the coexistence of both “irreversible” and reversible transitions in molecular machine models that successfully describe experimental data, which suggests that, in evolved machines, different transitions differ significantly in their dissipation. PMID:29073016

Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction.

PubMed

Ning, Jing; Chen, Yong; Piao, Jin

2017-07-01

Publication bias occurs when the published research results are systematically unrepresentative of the population of studies that have been conducted, and is a potential threat to meaningful meta-analysis. The Copas selection model provides a flexible framework for correcting estimates and offers considerable insight into the publication bias. However, maximizing the observed likelihood under the Copas selection model is challenging because the observed data contain very little information on the latent variable. In this article, we study a Copas-like selection model and propose an expectation-maximization (EM) algorithm for estimation based on the full likelihood. Empirical simulation studies show that the EM algorithm and its associated inferential procedure performs well and avoids the non-convergence problem when maximizing the observed likelihood. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

An integrated prediction and optimization model of biogas production system at a wastewater treatment facility.

PubMed

Akbaş, Halil; Bilgen, Bilge; Turhan, Aykut Melih

2015-11-01

This study proposes an integrated prediction and optimization model by using multi-layer perceptron neural network and particle swarm optimization techniques. Three different objective functions are formulated. The first one is the maximization of methane percentage with single output. The second one is the maximization of biogas production with single output. The last one is the maximization of biogas quality and biogas production with two outputs. Methane percentage, carbon dioxide percentage, and other contents' percentage are used as the biogas quality criteria. Based on the formulated models and data from a wastewater treatment facility, optimal values of input variables and their corresponding maximum output values are found out for each model. It is expected that the application of the integrated prediction and optimization models increases the biogas production and biogas quality, and contributes to the quantity of electricity production at the wastewater treatment facility. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimisation of the mean boat velocity in rowing.

PubMed

Rauter, G; Baumgartner, L; Denoth, J; Riener, R; Wolf, P

2012-01-01

In rowing, motor learning may be facilitated by augmented feedback that displays the ratio between actual mean boat velocity and maximal achievable mean boat velocity. To provide this ratio, the aim of this work was to develop and evaluate an algorithm calculating an individual maximal mean boat velocity. The algorithm optimised the horizontal oar movement under constraints such as the individual range of the horizontal oar displacement, individual timing of catch and release and an individual power-angle relation. Immersion and turning of the oar were simplified, and the seat movement of a professional rower was implemented. The feasibility of the algorithm, and of the associated ratio between actual boat velocity and optimised boat velocity, was confirmed by a study on four subjects: as expected, advanced rowing skills resulted in higher ratios, and the maximal mean boat velocity depended on the range of the horizontal oar displacement.

[Comparison of different methods in dealing with HIV viral load data with diversified missing value mechanism on HIV positive MSM].

PubMed

Jiang, Z; Dou, Z; Song, W L; Xu, J; Wu, Z Y

2017-11-10

Objective: To compare results of different methods: in organizing HIV viral load (VL) data with missing values mechanism. Methods We used software SPSS 17.0 to simulate complete and missing data with different missing value mechanism from HIV viral loading data collected from MSM in 16 cities in China in 2013. Maximum Likelihood Methods Using the Expectation and Maximization Algorithm (EM), regressive method, mean imputation, delete method, and Markov Chain Monte Carlo (MCMC) were used to supplement missing data respectively. The results: of different methods were compared according to distribution characteristics, accuracy and precision. Results HIV VL data could not be transferred into a normal distribution. All the methods showed good results in iterating data which is Missing Completely at Random Mechanism (MCAR). For the other types of missing data, regressive and MCMC methods were used to keep the main characteristic of the original data. The means of iterating database with different methods were all close to the original one. The EM, regressive method, mean imputation, and delete method under-estimate VL while MCMC overestimates it. Conclusion: MCMC can be used as the main imputation method for HIV virus loading missing data. The iterated data can be used as a reference for mean HIV VL estimation among the investigated population.

Effect of Repeated Whole Blood Donations on Aerobic Capacity and Hemoglobin Mass in Moderately Trained Male Subjects: A Randomized Controlled Trial.

PubMed

Meurrens, Julie; Steiner, Thomas; Ponette, Jonathan; Janssen, Hans Antonius; Ramaekers, Monique; Wehrlin, Jon Peter; Vandekerckhove, Philippe; Deldicque, Louise

2016-12-01

The aims of the present study were to investigate the impact of three whole blood donations on endurance capacity and hematological parameters and to determine the duration to fully recover initial endurance capacity and hematological parameters after each donation. Twenty-four moderately trained subjects were randomly divided in a donation (n = 16) and a placebo (n = 8) group. Each of the three donations was interspersed by 3 months, and the recovery of endurance capacity and hematological parameters was monitored up to 1 month after donation. Maximal power output, peak oxygen consumption, and hemoglobin mass decreased (p < 0.001) up to 4 weeks after a single blood donation with a maximal decrease of 4, 10, and 7%, respectively. Hematocrit, hemoglobin concentration, ferritin, and red blood cell count (RBC), all key hematological parameters for oxygen transport, were lowered by a single donation (p < 0.001) and cumulatively further affected by the repetition of the donations (p < 0.001). The maximal decrease after a blood donation was 11% for hematocrit, 10% for hemoglobin concentration, 50% for ferritin, and 12% for RBC (p < 0.001). Maximal power output cumulatively increased in the placebo group as the maximal exercise tests were repeated (p < 0.001), which indicates positive training adaptations. This increase in maximal power output over the whole duration of the study was not observed in the donation group. Maximal, but not submaximal, endurance capacity was altered after blood donation in moderately trained people and the expected increase in capacity after multiple maximal exercise tests was not present when repeating whole blood donations.

Maximum Likelihood and Minimum Distance Applied to Univariate Mixture Distributions.

ERIC Educational Resources Information Center

Wang, Yuh-Yin Wu; Schafer, William D.

This Monte-Carlo study compared modified Newton (NW), expectation-maximization algorithm (EM), and minimum Cramer-von Mises distance (MD), used to estimate parameters of univariate mixtures of two components. Data sets were fixed at size 160 and manipulated by mean separation, variance ratio, component proportion, and non-normality. Results…

Aging Education: A Worldwide Imperative

ERIC Educational Resources Information Center

McGuire, Sandra L.

2017-01-01

Life expectancy is increasing worldwide. Unfortunately, people are generally not prepared for this long life ahead and have ageist attitudes that inhibit maximizing the "longevity dividend" they have been given. Aging education can prepare people for life's later years and combat ageism. It can reimage aging as a time of continued…

IRT Item Parameter Recovery with Marginal Maximum Likelihood Estimation Using Loglinear Smoothing Models

ERIC Educational Resources Information Center

Casabianca, Jodi M.; Lewis, Charles

2015-01-01

Loglinear smoothing (LLS) estimates the latent trait distribution while making fewer assumptions about its form and maintaining parsimony, thus leading to more precise item response theory (IRT) item parameter estimates than standard marginal maximum likelihood (MML). This article provides the expectation-maximization algorithm for MML estimation…

The Probabilistic Nature of Preferential Choice

ERIC Educational Resources Information Center

Rieskamp, Jorg

2008-01-01

Previous research has developed a variety of theories explaining when and why people's decisions under risk deviate from the standard economic view of expected utility maximization. These theories are limited in their predictive accuracy in that they do not explain the probabilistic nature of preferential choice, that is, why an individual makes…

Relevance of a Managerial Decision-Model to Educational Administration.

ERIC Educational Resources Information Center

Lundin, Edward.; Welty, Gordon

The rational model of classical economic theory assumes that the decision maker has complete information on alternatives and consequences, and that he chooses the alternative that maximizes expected utility. This model does not allow for constraints placed on the decision maker resulting from lack of information, organizational pressures,…

India's growing participation in global clinical trials.

PubMed

Gupta, Yogendra K; Padhy, Biswa M

2011-06-01

Lower operational costs, recent regulatory reforms and several logistic advantages make India an attractive destination for conducting clinical trials. Efforts for maintaining stringent ethical standards and the launch of Pharmacovigilance Program of India are expected to maximize the potential of the country for clinical research. Copyright © 2011. Published by Elsevier Ltd.

Optimization Techniques for College Financial Aid Managers

ERIC Educational Resources Information Center

Bosshardt, Donald I.; Lichtenstein, Larry; Palumbo, George; Zaporowski, Mark P.

2010-01-01

In the context of a theoretical model of expected profit maximization, this paper shows how historic institutional data can be used to assist enrollment managers in determining the level of financial aid for students with varying demographic and quality characteristics. Optimal tuition pricing in conjunction with empirical estimation of…

An Investigation of the Nontechnical Skills Required to Maximize the Safety and Productivity of U.S. Navy Divers

DTIC Science & Technology

2005-04-01

experience. The critical incident interview uses recollection of a specific incident as its starting point and employs a semistructured interview format...context assessment, expectancies, and judgments. The four sweeps in the critical incident interview include: Sweep 1 - Prompting the interviewee to

An EM Algorithm for Maximum Likelihood Estimation of Process Factor Analysis Models

ERIC Educational Resources Information Center

Lee, Taehun

2010-01-01

In this dissertation, an Expectation-Maximization (EM) algorithm is developed and implemented to obtain maximum likelihood estimates of the parameters and the associated standard error estimates characterizing temporal flows for the latent variable time series following stationary vector ARMA processes, as well as the parameters defining the…

Common Core in the Real World

ERIC Educational Resources Information Center

Hess, Frederick M.; McShane, Michael Q.

2013-01-01

There are at least four key places where the Common Core intersects with current efforts to improve education in the United States--testing, professional development, expectations, and accountability. Understanding them can help educators, parents, and policymakers maximize the chance that the Common Core is helpful to these efforts and, perhaps…

Designing Contributing Student Pedagogies to Promote Students' Intrinsic Motivation to Learn

ERIC Educational Resources Information Center

Herman, Geoffrey L.

2012-01-01

In order to maximize the effectiveness of our pedagogies, we must understand how our pedagogies align with prevailing theories of cognition and motivation and design our pedagogies according to this understanding. When implementing Contributing Student Pedagogies (CSPs), students are expected to make meaningful contributions to the learning of…

Charter School Discipline: Examples of Policies and School Climate Efforts from the Field

ERIC Educational Resources Information Center

Kern, Nora; Kim, Suzie

2016-01-01

Students need a safe and supportive school environment to maximize their academic and social-emotional learning potential. A school's discipline policies and practices directly impact school climate and student achievement. Together, discipline policies and positive school climate efforts can reinforce behavioral expectations and ensure student…

In the queue for coronary artery bypass grafting: patients' perceptions of risk and 'maximal acceptable waiting time'.

PubMed

Llewellyn-Thomas, H; Thiel, E; Paterson, M; Naylor, D

1999-04-01

To elicit patients' maximal acceptable waiting times (MAWT) for non-urgent coronary artery bypass grafting (CABG), and to determine if MAWT is related to prior expectations of waiting times, symptom burden, expected relief, or perceived risks of myocardial infarction while waiting. Seventy-two patients on an elective CABG waiting list chose between two hypothetical but plausible options: a 1-month wait with 2% risk of surgical mortality, and a 6-month wait with 1% risk of surgical mortality. Waiting time in the 6-month option was varied up if respondents chose the 6-month/lower risk option, and down if they chose the 1-month/higher risk option, until the MAWT switch point was reached. Patients also reported their expected waiting time, perceived risks of myocardial infarction while waiting, current function, expected functional improvement and the value of that improvement. Only 17 (24%) patients chose the 6-month/1% risk option, while 55 (76%) chose the 1-month/2% risk option. The median MAWT was 2 months; scores ranged from 1 to 12 months (with two outliers). Many perceived high cumulative risks of myocardial infarction if waiting for 1 (upper quartile, > or = 1.45%) or 6 (upper quartile, > or = 10%) months. However, MAWT scores were related only to expected waiting time (r = 0.47; P < 0.0001). Most patients reject waiting 6 months for elective CABG, even if offered along with a halving in surgical mortality (from 2% to 1%). Intolerance for further delay seems to be determined primarily by patients' attachment to their scheduled surgical dates. Many also have severely inflated perceptions of their risk of myocardial infarction in the queue. These results suggest a need for interventions to modify patients' inaccurate risk perceptions, particularly if a scheduled surgical date must be deferred.

Detection of the power lines in UAV remote sensed images using spectral-spatial methods.

PubMed

Bhola, Rishav; Krishna, Nandigam Hari; Ramesh, K N; Senthilnath, J; Anand, Gautham

2018-01-15

In this paper, detection of the power lines on images acquired by Unmanned Aerial Vehicle (UAV) based remote sensing is carried out using spectral-spatial methods. Spectral clustering was performed using Kmeans and Expectation Maximization (EM) algorithm to classify the pixels into the power lines and non-power lines. The spectral clustering methods used in this study are parametric in nature, to automate the number of clusters Davies-Bouldin index (DBI) is used. The UAV remote sensed image is clustered into the number of clusters determined by DBI. The k clustered image is merged into 2 clusters (power lines and non-power lines). Further, spatial segmentation was performed using morphological and geometric operations, to eliminate the non-power line regions. In this study, UAV images acquired at different altitudes and angles were analyzed to validate the robustness of the proposed method. It was observed that the EM with spatial segmentation (EM-Seg) performed better than the Kmeans with spatial segmentation (Kmeans-Seg) on most of the UAV images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Performance Analysis of Continuous Black-Box Optimization Algorithms via Footprints in Instance Space.

PubMed

Muñoz, Mario A; Smith-Miles, Kate A

2017-01-01

This article presents a method for the objective assessment of an algorithm's strengths and weaknesses. Instead of examining the performance of only one or more algorithms on a benchmark set, or generating custom problems that maximize the performance difference between two algorithms, our method quantifies both the nature of the test instances and the algorithm performance. Our aim is to gather information about possible phase transitions in performance, that is, the points in which a small change in problem structure produces algorithm failure. The method is based on the accurate estimation and characterization of the algorithm footprints, that is, the regions of instance space in which good or exceptional performance is expected from an algorithm. A footprint can be estimated for each algorithm and for the overall portfolio. Therefore, we select a set of features to generate a common instance space, which we validate by constructing a sufficiently accurate prediction model. We characterize the footprints by their area and density. Our method identifies complementary performance between algorithms, quantifies the common features of hard problems, and locates regions where a phase transition may lie.

Impact of chronobiology on neuropathic pain treatment.

PubMed

Gilron, Ian

2016-01-01

Inflammatory pain exhibits circadian rhythmicity. Recently, a distinct diurnal pattern has been described for peripheral neuropathic conditions. This diurnal variation has several implications: advancing understanding of chronobiology may facilitate identification of new and improved treatments; developing pain-contingent strategies that maximize treatment at times of the day associated with highest pain intensity may provide optimal pain relief as well as minimize treatment-related adverse effects (e.g., daytime cognitive dysfunction); and consideration of the impact of chronobiology on pain measurement may lead to improvements in analgesic study design that will maximize assay sensitivity of clinical trials. Recent and ongoing chronobiology studies are thus expected to advance knowledge and treatment of neuropathic pain.

Operation of MRO's High Resolution Imaging Science Experiment (HiRISE): Maximizing Science Participation

NASA Technical Reports Server (NTRS)

Eliason, E.; Hansen, C. J.; McEwen, A.; Delamere, W. A.; Bridges, N.; Grant, J.; Gulich, V.; Herkenhoff, K.; Keszthelyi, L.; Kirk, R.

2003-01-01

Science return from the Mars Reconnaissance Orbiter (MRO) High Resolution Imaging Science Experiment (HiRISE) will be optimized by maximizing science participation in the experiment. MRO is expected to arrive at Mars in March 2006, and the primary science phase begins near the end of 2006 after aerobraking (6 months) and a transition phase. The primary science phase lasts for almost 2 Earth years, followed by a 2-year relay phase in which science observations by MRO are expected to continue. We expect to acquire approx. 10,000 images with HiRISE over the course of MRO's two earth-year mission. HiRISE can acquire images with a ground sampling dimension of as little as 30 cm (from a typical altitude of 300 km), in up to 3 colors, and many targets will be re-imaged for stereo. With such high spatial resolution, the percent coverage of Mars will be very limited in spite of the relatively high data rate of MRO (approx. 10x greater than MGS or Odyssey). We expect to cover approx. 1% of Mars at approx. 1m/pixel or better, approx. 0.1% at full resolution, and approx. 0.05% in color or in stereo. Therefore, the placement of each HiRISE image must be carefully considered in order to maximize the scientific return from MRO. We believe that every observation should be the result of a mini research project based on pre-existing datasets. During operations, we will need a large database of carefully researched 'suggested' observations to select from. The HiRISE team is dedicated to involving the broad Mars community in creating this database, to the fullest degree that is both practical and legal. The philosophy of the team and the design of the ground data system are geared to enabling community involvement. A key aspect of this is that image data will be made available to the planetary community for science analysis as quickly as possible to encourage feedback and new ideas for targets.

Interindividual variation in thermal sensitivity of maximal sprint speed, thermal behavior, and resting metabolic rate in a lizard.

PubMed

Artacho, Paulina; Jouanneau, Isabelle; Le Galliard, Jean-François

2013-01-01

Studies of the relationship of performance and behavioral traits with environmental factors have tended to neglect interindividual variation even though quantification of this variation is fundamental to understanding how phenotypic traits can evolve. In ectotherms, functional integration of locomotor performance, thermal behavior, and energy metabolism is of special interest because of the potential for coadaptation among these traits. For this reason, we analyzed interindividual variation, covariation, and repeatability of the thermal sensitivity of maximal sprint speed, preferred body temperature, thermal precision, and resting metabolic rate measured in ca. 200 common lizards (Zootoca vivipara) that varied by sex, age, and body size. We found significant interindividual variation in selected body temperatures and in the thermal performance curve of maximal sprint speed for both the intercept (expected trait value at the average temperature) and the slope (measure of thermal sensitivity). Interindividual differences in maximal sprint speed across temperatures, preferred body temperature, and thermal precision were significantly repeatable. A positive relationship existed between preferred body temperature and thermal precision, implying that individuals selecting higher temperatures were more precise. The resting metabolic rate was highly variable but was not related to thermal sensitivity of maximal sprint speed or thermal behavior. Thus, locomotor performance, thermal behavior, and energy metabolism were not directly functionally linked in the common lizard.

Using return on investment to maximize conservation effectiveness in Argentine grasslands

PubMed Central

Murdoch, William; Ranganathan, Jai; Polasky, Stephen; Regetz, James

2010-01-01

The rapid global loss of natural habitats and biodiversity, and limited resources, place a premium on maximizing the expected benefits of conservation actions. The scarcity of information on the fine-grained distribution of species of conservation concern, on risks of loss, and on costs of conservation actions, especially in developing countries, makes efficient conservation difficult. The distribution of ecosystem types (unique ecological communities) is typically better known than species and arguably better represents the entirety of biodiversity than do well-known taxa, so we use conserving the diversity of ecosystem types as our conservation goal. We define conservation benefit to include risk of conversion, spatial effects that reward clumping of habitat, and diminishing returns to investment in any one ecosystem type. Using Argentine grasslands as an example, we compare three strategies: protecting the cheapest land (“minimize cost”), maximizing conservation benefit regardless of cost (“maximize benefit”), and maximizing conservation benefit per dollar (“return on investment”). We first show that the widely endorsed goal of saving some percentage (typically 10%) of a country or habitat type, although it may inspire conservation, is a poor operational goal. It either leads to the accumulation of areas with low conservation benefit or requires infeasibly large sums of money, and it distracts from the real problem: maximizing conservation benefit given limited resources. Second, given realistic budgets, return on investment is superior to the other conservation strategies. Surprisingly, however, over a wide range of budgets, minimizing cost provides more conservation benefit than does the maximize-benefit strategy. PMID:21098281

Optimization of detectors for the ILC

NASA Astrophysics Data System (ADS)

Suehara, Taikan; ILD Group; SID Group

2016-04-01

International Linear Collider (ILC) is a next-generation e+e- linear collider to explore Higgs, Beyond-Standard-Models, top and electroweak particles with great precision. We are optimizing our two detectors, International Large Detector (ILD) and Silicon Detector (SiD) to maximize the physics reach expected in ILC with reasonable detector cost and good reliability. The optimization study on vertex detectors, main trackers and calorimeters is underway. We aim to conclude the optimization to establish final designs in a few years, to finish detector TDR and proposal in reply to expected ;green sign; of the ILC project.

Comparison of changes in the mobility of the pelvic floor muscle on during the abdominal drawing-in maneuver, maximal expiration, and pelvic floor muscle maximal contraction

PubMed Central

Jung, Halim; Jung, Sangwoo; Joo, Sunghee; Song, Changho

2016-01-01

[Purpose] The purpose of this study was to compare changes in the mobility of the pelvic floor muscle during the abdominal drawing-in maneuver, maximal expiration, and pelvic floor muscle maximal contraction. [Subjects] Thirty healthy adults participated in this study (15 men and 15 women). [Methods] All participants performed a bridge exercise and abdominal curl-up during the abdominal drawing-in maneuver, maximal expiration, and pelvic floor muscle maximal contraction. Pelvic floor mobility was evaluated as the distance from the bladder base using ultrasound. [Results] According to exercise method, bridge exercise and abdominal curl-ups led to significantly different pelvic floor mobility. The pelvic floor muscle was elevated during the abdominal drawing-in maneuver and descended during maximal expiration. Finally, pelvic floor muscle mobility was greater during abdominal curl-up than during the bridge exercise. [Conclusion] According to these results, the abdominal drawing-in maneuver induced pelvic floor muscle contraction, and pelvic floor muscle contraction was greater during the abdominal curl-up than during the bridge exercise. PMID:27065532

Comparison of changes in the mobility of the pelvic floor muscle on during the abdominal drawing-in maneuver, maximal expiration, and pelvic floor muscle maximal contraction.

PubMed

Jung, Halim; Jung, Sangwoo; Joo, Sunghee; Song, Changho

2016-01-01

[Purpose] The purpose of this study was to compare changes in the mobility of the pelvic floor muscle during the abdominal drawing-in maneuver, maximal expiration, and pelvic floor muscle maximal contraction. [Subjects] Thirty healthy adults participated in this study (15 men and 15 women). [Methods] All participants performed a bridge exercise and abdominal curl-up during the abdominal drawing-in maneuver, maximal expiration, and pelvic floor muscle maximal contraction. Pelvic floor mobility was evaluated as the distance from the bladder base using ultrasound. [Results] According to exercise method, bridge exercise and abdominal curl-ups led to significantly different pelvic floor mobility. The pelvic floor muscle was elevated during the abdominal drawing-in maneuver and descended during maximal expiration. Finally, pelvic floor muscle mobility was greater during abdominal curl-up than during the bridge exercise. [Conclusion] According to these results, the abdominal drawing-in maneuver induced pelvic floor muscle contraction, and pelvic floor muscle contraction was greater during the abdominal curl-up than during the bridge exercise.

Aging Management Guideline for commercial nuclear power plants: Motor control centers; Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Toman, G.; Gazdzinski, R.; O`Hearn, E.

1994-02-01

This Aging Management Guideline (AMG) provides recommended methods for effective detection and mitigation of age-related degradation mechanisms in Boiling Water Reactor (BWR) and Pressurized Water Reactor (PWR) commercial nuclear power plant motor control centers important to license renewal. The intent of this AMG is to assist plant maintenance and operations personnel in maximizing the safe, useful life of these components. It also supports the documentation of effective aging management programs required under the License Renewal Rule 10 CFR Part 54. This AMG is presented in a manner that allows personnel responsible for performance analysis and maintenance to compare their plant-specificmore » aging mechanisms (expected or already experienced) and aging management program activities to the more generic results and recommendations presented herein.« less

The impact of depuration on mussel hepatopancreas bacteriome composition and predicted metagenome.

PubMed

Rubiolo, J A; Lozano-Leon, A; Rodriguez-Souto, R; Fol Rodríguez, N; Vieytes, M R; Botana, L M

2018-07-01

Due to the rapid elimination of bacteria through normal behaviour of filter feeding and excretion, the decontamination of hazardous contaminating bacteria from shellfish is performed by depuration. This process, under conditions that maximize shellfish filtering activity, is a useful method to eliminate microorganisms from bivalves. The microbiota composition in bivalves reflects that of the environment of harvesting waters, so quite different bacteriomes would be expected in shellfish collected in different locations. Bacterial accumulation within molluscan shellfish occurs primarily in the hepatopancreas. In order to assess the effect of the depuration process on these different bacteriomes, in this work we used 16S RNA pyrosequencing and metagenome prediction to assess the impact of 15 h of depuration on the whole hepatopancreas bacteriome of mussels collected in three different locations.

Conservative restoration of a traumatically involved central incisor.

PubMed

Bassett, Joyce

2012-04-01

The use of a direct composite material known for excellent polishability, polish retention, and wear resistance is described in this case of a fractured central incisor restoration. The method used enabled the clinician to conserve tooth structure and maintain full control of the outcome while creating an esthetically imperceptible, reliable, and durable restoration for a young male patient. Emphasized in this case are the techniques of layering, contouring, and polishing of a nanocomposite used to maximize esthetics and meet patient expectations. To further ensure imperceptibility, the author recommends first facilitating color shade selection for both body and dentin-especially in two-shade or multiple-shade restorations-by placing the composite in its planned area of the restoration and curing it in its proper thickness to allow a preview and recipe map.

Optimization Of Mean-Semivariance-Skewness Portfolio Selection Model In Fuzzy Random Environment

NASA Astrophysics Data System (ADS)

Chatterjee, Amitava; Bhattacharyya, Rupak; Mukherjee, Supratim; Kar, Samarjit

2010-10-01

The purpose of the paper is to construct a mean-semivariance-skewness portfolio selection model in fuzzy random environment. The objective is to maximize the skewness with predefined maximum risk tolerance and minimum expected return. Here the security returns in the objectives and constraints are assumed to be fuzzy random variables in nature and then the vagueness of the fuzzy random variables in the objectives and constraints are transformed into fuzzy variables which are similar to trapezoidal numbers. The newly formed fuzzy model is then converted into a deterministic optimization model. The feasibility and effectiveness of the proposed method is verified by numerical example extracted from Bombay Stock Exchange (BSE). The exact parameters of fuzzy membership function and probability density function are obtained through fuzzy random simulating the past dates.

Aging and loss decision making: increased risk aversion and decreased use of maximizing information, with correlated rationality and value maximization

PubMed Central

Kurnianingsih, Yoanna A.; Sim, Sam K. Y.; Chee, Michael W. L.; Mullette-Gillman, O’Dhaniel A.

2015-01-01

We investigated how adult aging specifically alters economic decision-making, focusing on examining alterations in uncertainty preferences (willingness to gamble) and choice strategies (what gamble information influences choices) within both the gains and losses domains. Within each domain, participants chose between certain monetary outcomes and gambles with uncertain outcomes. We examined preferences by quantifying how uncertainty modulates choice behavior as if altering the subjective valuation of gambles. We explored age-related preferences for two types of uncertainty, risk, and ambiguity. Additionally, we explored how aging may alter what information participants utilize to make their choices by comparing the relative utilization of maximizing and satisficing information types through a choice strategy metric. Maximizing information was the ratio of the expected value of the two options, while satisficing information was the probability of winning. We found age-related alterations of economic preferences within the losses domain, but no alterations within the gains domain. Older adults (OA; 61–80 years old) were significantly more uncertainty averse for both risky and ambiguous choices. OA also exhibited choice strategies with decreased use of maximizing information. Within OA, we found a significant correlation between risk preferences and choice strategy. This linkage between preferences and strategy appears to derive from a convergence to risk neutrality driven by greater use of the effortful maximizing strategy. As utility maximization and value maximization intersect at risk neutrality, this result suggests that OA are exhibiting a relationship between enhanced rationality and enhanced value maximization. While there was variability in economic decision-making measures within OA, these individual differences were unrelated to variability within examined measures of cognitive ability. Our results demonstrate that aging alters economic decision-making for losses through changes in both individual preferences and the strategies individuals employ. PMID:26029092

Reliability and cost: A sensitivity analysis

NASA Technical Reports Server (NTRS)

Suich, Ronald C.; Patterson, Richard L.

1991-01-01

In the design phase of a system, how a design engineer or manager choose between a subsystem with .990 reliability and a more costly subsystem with .995 reliability is examined, along with the justification of the increased cost. High reliability is not necessarily an end in itself but may be desirable in order to reduce the expected cost due to subsystem failure. However, this may not be the wisest use of funds since the expected cost due to subsystem failure is not the only cost involved. The subsystem itself may be very costly. The cost of the subsystem nor the expected cost due to subsystem failure should not be considered separately but the total of the two costs should be maximized, i.e., the total of the cost of the subsystem plus the expected cost due to subsystem failure.

Maximizing Your Grant Development: A Guide for CEOs.

ERIC Educational Resources Information Center

Snyder, Thomas

1993-01-01

Since most private and public sources of external funding generally expect increased effort and accountability, Chief Executive Officers (CEOs) at two-year colleges must inform faculty and staff that if they do not expend extra effort their college will not receive significant grants. The CEO must also work with the college's professional…

A Prelude to Strategic Management of an Online Enterprise

ERIC Educational Resources Information Center

Pan, Cheng-Chang; Sivo, Stephen A.; Goldsmith, Clair

2016-01-01

Strategic management is expected to allow an organization to maximize given constraints and optimize limited resources in an effort to create a competitive advantage that leads to better results. For both for-profit and non-profit organizations, such strategic thinking helps the management make informed decisions and sustain long-term planning. To…

76 FR 79612 - Magnuson-Stevens Fishery Conservation and Management Act Provisions; Fisheries of the...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-22

... state-operated permit banks for the purpose of maximizing the fishing opportunities made available by... activity to regain their DAS for that trip, providing another opportunity to profit from the DAS that would... entities. Further, no reductions in profit are expected for any small entities, so the profitability...

Smooth Transitions: Helping Students with Autism Spectrum Disorder Navigate the School Day

ERIC Educational Resources Information Center

Hume, Kara; Sreckovic, Melissa; Snyder, Kate; Carnahan, Christina R.

2014-01-01

In school, students are expected to navigate different types of transitions every day, including those between instructors, subjects, and instructional formats, as well as classrooms. Despite the routines that many teachers develop to facilitate efficient transitions and maximize instructional time, many learners with ASD continue to struggle with…

3D image reconstruction algorithms for cryo-electron-microscopy images of virus particles

NASA Astrophysics Data System (ADS)

Doerschuk, Peter C.; Johnson, John E.

2000-11-01

A statistical model for the object and the complete image formation process in cryo electron microscopy of viruses is presented. Using this model, maximum likelihood reconstructions of the 3D structure of viruses are computed using the expectation maximization algorithm and an example based on Cowpea mosaic virus is provided.

A Benefit-Maximization Solution to Our Faculty Promotion and Tenure Process

ERIC Educational Resources Information Center

Barat, Somjit; Harvey, Hanafiah

2015-01-01

Tenure-track/tenured faculty at higher education institutions are expected to teach, conduct research and provide service as part of their promotion and tenure process, the relative importance of each component varying with the position and/or the university. However, based on the author's personal experience, feedback received from several…

"At Least One" Way to Add Value to Conferences

ERIC Educational Resources Information Center

Wilson, Warren J.

2005-01-01

In "EDUCAUSE Quarterly," Volume 25, Number 3, 2002, Joan Getman and Nikki Reynolds published an excellent article about getting the most from a conference. They listed 10 strategies that a conference attendee could use to maximize the conference's yield in information and motivation: (1) Plan ahead; (2) Set realistic expectations; (3) Use e-mail…

Team Members' Perceptions of Online Teamwork Learning Experiences and Building Teamwork Trust: A Qualitative Study

ERIC Educational Resources Information Center

Tseng, Hung Wei; Yeh, Hsin-Te

2013-01-01

Teamwork factors can facilitate team members, committing themselves to the purposes of maximizing their own and others' contributions and successes. It is important for online instructors to comprehend students' expectations on learning collaboratively. The aims of this study were to investigate online collaborative learning experiences and to…

78 FR 25862 - Fisheries of the Northeastern United States; Final 2013-2015 Spiny Dogfish Fishery Specifications

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-03

... the status quo. The action is expected to maximize the profitability for the spiny dogfish fishery... possible commercial quotas by not making a deduction from the ACL accounting for management uncertainty...) in 2015; however, not accounting for management uncertainty would have increased the risk of...

Global Convergence of the EM Algorithm for Unconstrained Latent Variable Models with Categorical Indicators

ERIC Educational Resources Information Center

Weissman, Alexander

2013-01-01

Convergence of the expectation-maximization (EM) algorithm to a global optimum of the marginal log likelihood function for unconstrained latent variable models with categorical indicators is presented. The sufficient conditions under which global convergence of the EM algorithm is attainable are provided in an information-theoretic context by…

Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

ERIC Educational Resources Information Center

Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu

2011-01-01

Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…

Steganalysis feature improvement using expectation maximization

NASA Astrophysics Data System (ADS)

Rodriguez, Benjamin M.; Peterson, Gilbert L.; Agaian, Sos S.

2007-04-01

Images and data files provide an excellent opportunity for concealing illegal or clandestine material. Currently, there are over 250 different tools which embed data into an image without causing noticeable changes to the image. From a forensics perspective, when a system is confiscated or an image of a system is generated the investigator needs a tool that can scan and accurately identify files suspected of containing malicious information. The identification process is termed the steganalysis problem which focuses on both blind identification, in which only normal images are available for training, and multi-class identification, in which both the clean and stego images at several embedding rates are available for training. In this paper an investigation of a clustering and classification technique (Expectation Maximization with mixture models) is used to determine if a digital image contains hidden information. The steganalysis problem is for both anomaly detection and multi-class detection. The various clusters represent clean images and stego images with between 1% and 10% embedding percentage. Based on the results it is concluded that the EM classification technique is highly suitable for both blind detection and the multi-class problem.

Search for maximal flavor violating scalars in same-charge lepton pairs in pp collisions at sqrt[s]=1.96 TeV.

PubMed

Aaltonen, T; Adelman, J; Akimoto, T; Albrow, M G; Alvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Aoki, M; Apollinari, G; Apresyan, A; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Azzi-Bacchetta, P; Azzurri, P; Bacchetta, N; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Baroiant, S; Bar-Shalom, S; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Bednar, P; Behari, S; Bellettini, G; Bellinger, J; Belloni, A; Benjamin, D; Beretvas, A; Beringer, J; Berry, T; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bolshov, A; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Bridgeman, A; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Compostella, G; Convery, M E; Conway, J; Cooper, B; Copic, K; Cordelli, M; Cortiana, G; Crescioli, F; Cuenca Almenar, C; Cuevas, J; Culbertson, R; Cully, J C; Dagenhart, D; Datta, M; Davies, T; de Barbaro, P; De Cecco, S; Deisher, A; De Lentdecker, G; De Lorenzo, G; Dell'orso, M; Demortier, L; Deng, J; Deninno, M; De Pedis, D; Derwent, P F; Di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Ferrazza, C; Field, R; Flanagan, G; Forrest, R; Forrester, S; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garberson, F; Garcia, J E; Garfinkel, A F; Genser, K; Gerberich, H; Gerdes, D; Giagu, S; Giakoumopolou, V; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C M; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Hamilton, A; Han, B-Y; Han, J Y; Handler, R; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hauser, J; Hays, C; Heck, M; Heijboer, A; Heinemann, B; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hewamanage, S; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; Iyutin, B; James, E; Jayatilaka, B; Jeans, D; Jeon, E J; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Kar, D; Karchin, P E; Kato, Y; Kephart, R; Kerzel, U; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Klute, M; Knuteson, B; Ko, B R; Koay, S A; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kraus, J; Kreps, M; Kroll, J; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhlmann, S E; Kuhr, T; Kulkarni, N P; Kusakabe, Y; Kwang, S; Laasanen, A T; Lai, S; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; Lecompte, T; Lee, J; Lee, J; Lee, Y J; Lee, S W; Lefèvre, R; Leonardo, N; Leone, S; Levy, S; Lewis, J D; Lin, C; Lin, C S; Linacre, J; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lovas, L; Lu, R-S; Lucchesi, D; Lueck, J; Luci, C; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Lytken, E; Mack, P; Macqueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis, A; Margaroli, F; Marino, C; Marino, C P; Martin, A; Martin, M; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Mattson, M E; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzemer, S; Menzione, A; Merkel, P; Mesropian, C; Messina, A; Miao, T; Miladinovic, N; Miles, J; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyake, H; Moed, S; Moggi, N; Moon, C S; Moore, R; Morello, M; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Neu, C; Neubauer, M S; Nielsen, J; Nodulman, L; Norman, M; Norniella, O; Nurse, E; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Oldeman, R; Orava, R; Osterberg, K; Pagan Griso, S; Pagliarone, C; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Piedra, J; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Portell, X; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Rajaraman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Reisert, B; Rekovic, V; Renton, P; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Saarikko, H; Safonov, A; Sakumoto, W K; Salamanna, G; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savoy-Navarro, A; Scheidle, T; Schlabach, P; Schmidt, E E; Schmidt, M A; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scott, A L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sfyria, A; Shalhout, S Z; Shapiro, M D; Shears, T; Shepard, P F; Sherman, D; Shimojima, M; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soderberg, M; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spinella, F; Spreitzer, T; Squillacioti, P; Stanitzki, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Stuart, D; Suh, J S; Sukhanov, A; Sun, H; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thompson, G A; Thomson, E; Tipton, P; Tiwari, V; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Tourneur, S; Trischuk, W; Tu, Y; Turini, N; Ukegawa, F; Uozumi, S; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Vellidis, C; Veszpremi, V; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vogel, M; Volobouev, I; Volpi, G; Würthwein, F; Wagner, P; Wagner, R G; Wagner, R L; Wagner-Kuhr, J; Wagner, W; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Wynne, S M; Yagil, A; Yamamoto, K; Yamaoka, J; Yamashita, T; Yang, C; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, F; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zaw, I; Zhang, X; Zheng, Y; Zucchelli, S

2009-01-30

Models of maximal flavor violation (MxFV) in elementary particle physics may contain at least one new scalar SU(2) doublet field Phi(FV)=(eta(0),eta(+)) that couples the first and third generation quarks (q_(1), q_(3)) via a Lagrangian term L(FV)=xi(13)Phi(FV)q(1)q(3). These models have a distinctive signature of same-charge top-quark pairs and evade flavor-changing limits from meson mixing measurements. Data corresponding to 2 fb(-1) collected by the Collider Dectector at Fermilab II detector in pp[over ] collisions at sqrt[s]=1.96 TeV are analyzed for evidence of the MxFV signature. For a neutral scalar eta(0) with m_(eta;(0))=200 GeV/c(2) and coupling xi(13)=1, approximately 11 signal events are expected over a background of 2.1+/-1.8 events. Three events are observed in the data, consistent with background expectations, and limits are set on the coupling xi(13) for m(eta(0)=180-300 GeV/c(2).

Nonadditive entropy maximization is inconsistent with Bayesian updating.

PubMed

Pressé, Steve

2014-11-01

The maximum entropy method-used to infer probabilistic models from data-is a special case of Bayes's model inference prescription which, in turn, is grounded in basic propositional logic. By contrast to the maximum entropy method, the compatibility of nonadditive entropy maximization with Bayes's model inference prescription has never been established. Here we demonstrate that nonadditive entropy maximization is incompatible with Bayesian updating and discuss the immediate implications of this finding. We focus our attention on special cases as illustrations.

A Unified Framework for Brain Segmentation in MR Images

PubMed Central

Yazdani, S.; Yusof, R.; Karimian, A.; Riazi, A. H.; Bennamoun, M.

2015-01-01

Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets. PMID:26089978

Innovation of floating time domain electromagnetic method in the case of environmental geophysics

NASA Astrophysics Data System (ADS)

Nurjanah, Siti; Widodo

2017-07-01

Geophysics has some methods that can be used to reveal the subsurface structure of the earth. The physical features obtained from the acquisition then analyzed and interpreted, so that it can be a great lead to interpret the physical contents, determine its position and its distribution. Geophysical methods also can be used to help the environment contamination survey which is referred to environmental geophysics. There are many sources of pollution that can harm the nature, for example, the source in the form of solid waste, liquid waste containing heavy metals, or radioactive, and etc. As time passes, these sources might settle in any sedimentary area and become sediments. Time Domain Electromagnetic (TDEM) is a trustworthy method to detect the presence of conductive anomaly due to sediment accumulation. Innovation of floating TDEM created to maximize the potential of the method, so that it can be used in aquatic environments. The configuration of TDEM modified using pipes and tires during the process of measurements. We conducted numerical simulation using Marquardt and Occam Algorithms towards synthetic model to ensure the capability of the proposed design. The development of this innovation is expected to be very useful to repair the natural conditions, especially in the water.

Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning

PubMed Central

Kim, Yong-Hyuk; Ha, Ji-Hun; Kim, Na-Young; Im, Hyo-Hyuc; Sim, Sangjin; Choi, Reno K. Y.

2016-01-01

A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones. The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km2, from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user's mobility, prior to the expectation-maximization (EM) clustering. Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR). The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering. For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP. It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network. PMID:27524999

Optimal flight initiation distance.

PubMed

Cooper, William E; Frederick, William G

2007-01-07

Decisions regarding flight initiation distance have received scant theoretical attention. A graphical model by Ydenberg and Dill (1986. The economics of fleeing from predators. Adv. Stud. Behav. 16, 229-249) that has guided research for the past 20 years specifies when escape begins. In the model, a prey detects a predator, monitors its approach until costs of escape and of remaining are equal, and then flees. The distance between predator and prey when escape is initiated (approach distance = flight initiation distance) occurs where decreasing cost of remaining and increasing cost of fleeing intersect. We argue that prey fleeing as predicted cannot maximize fitness because the best prey can do is break even during an encounter. We develop two optimality models, one applying when all expected future contribution to fitness (residual reproductive value) is lost if the prey dies, the other when any fitness gained (increase in expected RRV) during the encounter is retained after death. Both models predict optimal flight initiation distance from initial expected fitness, benefits obtainable during encounters, costs of escaping, and probability of being killed. Predictions match extensively verified predictions of Ydenberg and Dill's (1986) model. Our main conclusion is that optimality models are preferable to break-even models because they permit fitness maximization, offer many new testable predictions, and allow assessment of prey decisions in many naturally occurring situations through modification of benefit, escape cost, and risk functions.

Experimental evolution of bet hedging under manipulated environmental uncertainty in Neurospora crassa.

PubMed

Graham, Jeffrey K; Smith, Myron L; Simons, Andrew M

2014-07-22

All organisms are faced with environmental uncertainty. Bet-hedging theory expects unpredictable selection to result in the evolution of traits that maximize the geometric-mean fitness even though such traits appear to be detrimental over the shorter term. Despite the centrality of fitness measures to evolutionary analysis, no direct test of the geometric-mean fitness principle exists. Here, we directly distinguish between predictions of competing fitness maximization principles by testing Cohen's 1966 classic bet-hedging model using the fungus Neurospora crassa. The simple prediction is that propagule dormancy will evolve in proportion to the frequency of 'bad' years, whereas the prediction of the alternative arithmetic-mean principle is the evolution of zero dormancy as long as the expectation of a bad year is less than 0.5. Ascospore dormancy fraction in N. crassa was allowed to evolve under five experimental selection regimes that differed in the frequency of unpredictable 'bad years'. Results were consistent with bet-hedging theory: final dormancy fraction in 12 genetic lineages across 88 independently evolving samples was proportional to the frequency of bad years, and evolved both upwards and downwards as predicted from a range of starting dormancy fractions. These findings suggest that selection results in adaptation to variable rather than to expected environments. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Defender-Attacker Decision Tree Analysis to Combat Terrorism.

PubMed

Garcia, Ryan J B; von Winterfeldt, Detlof

2016-12-01

We propose a methodology, called defender-attacker decision tree analysis, to evaluate defensive actions against terrorist attacks in a dynamic and hostile environment. Like most game-theoretic formulations of this problem, we assume that the defenders act rationally by maximizing their expected utility or minimizing their expected costs. However, we do not assume that attackers maximize their expected utilities. Instead, we encode the defender's limited knowledge about the attacker's motivations and capabilities as a conditional probability distribution over the attacker's decisions. We apply this methodology to the problem of defending against possible terrorist attacks on commercial airplanes, using one of three weapons: infrared-guided MANPADS (man-portable air defense systems), laser-guided MANPADS, or visually targeted RPGs (rocket propelled grenades). We also evaluate three countermeasures against these weapons: DIRCMs (directional infrared countermeasures), perimeter control around the airport, and hardening airplanes. The model includes deterrence effects, the effectiveness of the countermeasures, and the substitution of weapons and targets once a specific countermeasure is selected. It also includes a second stage of defensive decisions after an attack occurs. Key findings are: (1) due to the high cost of the countermeasures, not implementing countermeasures is the preferred defensive alternative for a large range of parameters; (2) if the probability of an attack and the associated consequences are large, a combination of DIRCMs and ground perimeter control are preferred over any single countermeasure. © 2016 Society for Risk Analysis.

Optimal rotation sequences for active perception

NASA Astrophysics Data System (ADS)

Nakath, David; Rachuy, Carsten; Clemens, Joachim; Schill, Kerstin

2016-05-01

One major objective of autonomous systems navigating in dynamic environments is gathering information needed for self localization, decision making, and path planning. To account for this, such systems are usually equipped with multiple types of sensors. As these sensors often have a limited field of view and a fixed orientation, the task of active perception breaks down to the problem of calculating alignment sequences which maximize the information gain regarding expected measurements. Action sequences that rotate the system according to the calculated optimal patterns then have to be generated. In this paper we present an approach for calculating these sequences for an autonomous system equipped with multiple sensors. We use a particle filter for multi- sensor fusion and state estimation. The planning task is modeled as a Markov decision process (MDP), where the system decides in each step, what actions to perform next. The optimal control policy, which provides the best action depending on the current estimated state, maximizes the expected cumulative reward. The latter is computed from the expected information gain of all sensors over time using value iteration. The algorithm is applied to a manifold representation of the joint space of rotation and time. We show the performance of the approach in a spacecraft navigation scenario where the information gain is changing over time, caused by the dynamic environment and the continuous movement of the spacecraft

Price of anarchy is maximized at the percolation threshold.

PubMed

Skinner, Brian

2015-05-01

When many independent users try to route traffic through a network, the flow can easily become suboptimal as a consequence of congestion of the most efficient paths. The degree of this suboptimality is quantified by the so-called price of anarchy (POA), but so far there are no general rules for when to expect a large POA in a random network. Here I address this question by introducing a simple model of flow through a network with randomly placed congestible and incongestible links. I show that the POA is maximized precisely when the fraction of congestible links matches the percolation threshold of the lattice. Both the POA and the total cost demonstrate critical scaling near the percolation threshold.

Methodology using a portable X-ray fluorescence device for on-site and rapid evaluation of heavy-atom contamination in wounds: a model study for application to plutonium contamination.

PubMed

Yoshii, Hiroshi; Yanagihara, Kouta; Imaseki, Hitoshi; Hamano, Tsuyoshi; Yamanishi, Hirokuni; Inagaki, Masayo; Sakai, Yasuhiro; Sugiura, Nobuyuki; Kurihara, Osamu; Sakai, Kazuo

2014-01-01

Workers decommissioning the Fukushima-Daiichi nuclear power plant damaged from the Great East Japan Earthquake and resulting tsunami are at risk of injury with possible contamination from radioactive heavy atoms including actinides, such as plutonium. We propose a new methodology for on-site and rapid evaluation of heavy-atom contamination in wounds using a portable X-ray fluorescence (XRF) device. In the present study, stable lead was used as the model contaminant substitute for radioactive heavy atoms. First, the wound model was developed by placing a liquid blood phantom on an epoxy resin wound phantom contaminated with lead. Next, the correlation between the concentration of contaminant and the XRF peak intensity was formulated considering the thickness of blood exiting the wound. Methods to determine the minimum detection limit (MDL) of contaminants at any maximal equivalent dose to the wound by XRF measurement were also established. For example, in this system, at a maximal equivalent dose of 16.5 mSv to the wound and blood thickness of 0.5 mm, the MDL value for lead was 1.2 ppm (3.1 nmol). The radioactivity of 239Pu corresponding to 3.1 nmol is 1.7 kBq, which is lower than the radioactivity of 239Pu contaminating puncture wounds in previous severe accidents. In conclusion, the established methodology could be beneficial for future development of a method to evaluate plutonium contamination in wounds. Highlights: Methodology for evaluation of heavy-atom contamination in a wound was established. A portable X-ray fluorescence device enables on-site, rapid and direct evaluation. This method is expected to be used for evaluation of plutonium contamination in wounds.

Performance of Blind Source Separation Algorithms for FMRI Analysis using a Group ICA Method

PubMed Central

Correa, Nicolle; Adali, Tülay; Calhoun, Vince D.

2007-01-01

Independent component analysis (ICA) is a popular blind source separation (BSS) technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data. A number of ICA approaches have been used for fMRI data analysis, and even more ICA algorithms exist, however the impact of using different algorithms on the results is largely unexplored. In this paper, we study the performance of four major classes of algorithms for spatial ICA, namely information maximization, maximization of non-gaussianity, joint diagonalization of cross-cumulant matrices, and second-order correlation based methods when they are applied to fMRI data from subjects performing a visuo-motor task. We use a group ICA method to study the variability among different ICA algorithms and propose several analysis techniques to evaluate their performance. We compare how different ICA algorithms estimate activations in expected neuronal areas. The results demonstrate that the ICA algorithms using higher-order statistical information prove to be quite consistent for fMRI data analysis. Infomax, FastICA, and JADE all yield reliable results; each having their strengths in specific areas. EVD, an algorithm using second-order statistics, does not perform reliably for fMRI data. Additionally, for the iterative ICA algorithms, it is important to investigate the variability of the estimates from different runs. We test the consistency of the iterative algorithms, Infomax and FastICA, by running the algorithm a number of times with different initializations and note that they yield consistent results over these multiple runs. Our results greatly improve our confidence in the consistency of ICA for fMRI data analysis. PMID:17540281

Performance of blind source separation algorithms for fMRI analysis using a group ICA method.

PubMed

Correa, Nicolle; Adali, Tülay; Calhoun, Vince D

2007-06-01

Independent component analysis (ICA) is a popular blind source separation technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data. A number of ICA approaches have been used for fMRI data analysis, and even more ICA algorithms exist; however, the impact of using different algorithms on the results is largely unexplored. In this paper, we study the performance of four major classes of algorithms for spatial ICA, namely, information maximization, maximization of non-Gaussianity, joint diagonalization of cross-cumulant matrices and second-order correlation-based methods, when they are applied to fMRI data from subjects performing a visuo-motor task. We use a group ICA method to study variability among different ICA algorithms, and we propose several analysis techniques to evaluate their performance. We compare how different ICA algorithms estimate activations in expected neuronal areas. The results demonstrate that the ICA algorithms using higher-order statistical information prove to be quite consistent for fMRI data analysis. Infomax, FastICA and joint approximate diagonalization of eigenmatrices (JADE) all yield reliable results, with each having its strengths in specific areas. Eigenvalue decomposition (EVD), an algorithm using second-order statistics, does not perform reliably for fMRI data. Additionally, for iterative ICA algorithms, it is important to investigate the variability of estimates from different runs. We test the consistency of the iterative algorithms Infomax and FastICA by running the algorithm a number of times with different initializations, and we note that they yield consistent results over these multiple runs. Our results greatly improve our confidence in the consistency of ICA for fMRI data analysis.

Unifying cost and information in information-theoretic competitive learning.

PubMed

Kamimura, Ryotaro

2005-01-01

In this paper, we introduce costs into the framework of information maximization and try to maximize the ratio of information to its associated cost. We have shown that competitive learning is realized by maximizing mutual information between input patterns and competitive units. One shortcoming of the method is that maximizing information does not necessarily produce representations faithful to input patterns. Information maximizing primarily focuses on some parts of input patterns that are used to distinguish between patterns. Therefore, we introduce the cost, which represents average distance between input patterns and connection weights. By minimizing the cost, final connection weights reflect input patterns well. We applied the method to a political data analysis, a voting attitude problem and a Wisconsin cancer problem. Experimental results confirmed that, when the cost was introduced, representations faithful to input patterns were obtained. In addition, improved generalization performance was obtained within a relatively short learning time.

Maximally random discrete-spin systems with symmetric and asymmetric interactions and maximally degenerate ordering

NASA Astrophysics Data System (ADS)

Atalay, Bora; Berker, A. Nihat

2018-05-01

Discrete-spin systems with maximally random nearest-neighbor interactions that can be symmetric or asymmetric, ferromagnetic or antiferromagnetic, including off-diagonal disorder, are studied, for the number of states q =3 ,4 in d dimensions. We use renormalization-group theory that is exact for hierarchical lattices and approximate (Migdal-Kadanoff) for hypercubic lattices. For all d >1 and all noninfinite temperatures, the system eventually renormalizes to a random single state, thus signaling q ×q degenerate ordering. Note that this is the maximally degenerate ordering. For high-temperature initial conditions, the system crosses over to this highly degenerate ordering only after spending many renormalization-group iterations near the disordered (infinite-temperature) fixed point. Thus, a temperature range of short-range disorder in the presence of long-range order is identified, as previously seen in underfrustrated Ising spin-glass systems. The entropy is calculated for all temperatures, behaves similarly for ferromagnetic and antiferromagnetic interactions, and shows a derivative maximum at the short-range disordering temperature. With a sharp immediate contrast of infinitesimally higher dimension 1 +ɛ , the system is as expected disordered at all temperatures for d =1 .

TH-CD-206-01: Expectation-Maximization Algorithm-Based Tissue Mixture Quantification for Perfusion MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Han, H; Xing, L; Liang, Z

Purpose: To investigate the feasibility of estimating the tissue mixture perfusions and quantifying cerebral blood flow change in arterial spin labeled (ASL) perfusion MR images. Methods: The proposed perfusion MR image analysis framework consists of 5 steps: (1) Inhomogeneity correction was performed on the T1- and T2-weighted images, which are available for each studied perfusion MR dataset. (2) We used the publicly available FSL toolbox to strip off the non-brain structures from the T1- and T2-weighted MR images. (3) We applied a multi-spectral tissue-mixture segmentation algorithm on both T1- and T2-structural MR images to roughly estimate the fraction of eachmore » tissue type - white matter, grey matter and cerebral spinal fluid inside each image voxel. (4) The distributions of the three tissue types or tissue mixture across the structural image array are down-sampled and mapped onto the ASL voxel array via a co-registration operation. (5) The presented 4-dimensional expectation-maximization (4D-EM) algorithm takes the down-sampled three tissue type distributions on perfusion image data to generate the perfusion mean, variance and percentage images for each tissue type of interest. Results: Experimental results on three volunteer datasets demonstrated that the multi-spectral tissue-mixture segmentation algorithm was effective to initialize tissue mixtures from T1- and T2-weighted MR images. Compared with the conventional ASL image processing toolbox, the proposed 4D-EM algorithm not only generated comparable perfusion mean images, but also produced perfusion variance and percentage images, which the ASL toolbox cannot obtain. It is observed that the perfusion contribution percentages may not be the same as the corresponding tissue mixture volume fractions estimated in the structural images. Conclusion: A specific application to brain ASL images showed that the presented perfusion image analysis method is promising for detecting subtle changes in tissue perfusions, which is valuable for the early diagnosis of certain brain diseases, e.g. multiple sclerosis.« less

Antagonistic effect of helpers on breeding male and female survival in a cooperatively breeding bird

PubMed Central

Paquet, Matthieu; Doutrelant, Claire; Hatchwell, Ben J; Spottiswoode, Claire N; Covas, Rita

2015-01-01

1. Cooperatively breeding species are typically long lived and hence, according to theory, are expected to maximize their lifetime reproductive success through maximizing survival. Under these circumstances, the presence of helpers could be used to lighten the effort of current reproduction for parents to achieve higher survival. 2. In addition, individuals of different sexes and ages may follow different strategies, but whether male and female breeders and individuals of different ages benefit differently from the presence of helpers has often been overlooked. Moreover, only one study that investigated the relationship between parental survival and the presence of helpers used capture–mark–recapture analyses (CMR). These methods are important since they allow us to account for the non-detection of individuals that are alive in the population but not detected, and thus, the effects on survival and recapture probability to be disentangled. 3. Here, we used multi-event CMR methods to investigate whether the number of helpers was associated with an increase in survival probability for male and female breeders of different ages in the sociable weaver Philetairus socius. In this species, both sexes reduce their feeding rate in the presence of helpers. We therefore predicted that the presence of helpers should increase the breeders' survival in both sexes, especially early in life when individuals potentially have more future breeding opportunities. In addition, sociable weaver females reduce their investment in eggs in the presence of helpers, so we predicted a stronger effect of helpers on female than male survival. 4. As expected we found that females had a higher survival probability when breeding with more helpers. Unexpectedly, however, male survival probability decreased with increasing number of helpers. This antagonistic effect diminished as the breeders grew older. 5. These results illustrate the complexity of fitness costs and benefits underlying cooperative behaviours and how these may vary with the individuals' sex and age. They also highlight the need for further studies on the sex-specific effects of helpers on survival. PMID:25850564

The Scatter Search Based Algorithm to Revenue Management Problem in Broadcasting Companies

NASA Astrophysics Data System (ADS)

Pishdad, Arezoo; Sharifyazdi, Mehdi; Karimpour, Reza

2009-09-01

The problem under question in this paper which is faced by broadcasting companies is how to benefit from a limited advertising space. This problem is due to the stochastic behavior of customers (advertiser) in different fare classes. To address this issue we propose a mathematical constrained nonlinear multi period model which incorporates cancellation and overbooking. The objective function is to maximize the total expected revenue and our numerical method performs it by determining the sales limits for each class of customer to present the revenue management control policy. Scheduling the advertising spots in breaks is another area of concern and we consider it as a constraint in our model. In this paper an algorithm based on Scatter search is developed to acquire a good feasible solution. This method uses simulation over customer arrival and in a continuous finite time horizon [0, T]. Several sensitivity analyses are conducted in computational result for depicting the effectiveness of proposed method. It also provides insight into better results of considering revenue management (control policy) compared to "no sales limit" policy in which sooner demand will served first.

Fitting ordinary differential equations to short time course data.

PubMed

Brewer, Daniel; Barenco, Martino; Callard, Robin; Hubank, Michael; Stark, Jaroslav

2008-02-28

Ordinary differential equations (ODEs) are widely used to model many systems in physics, chemistry, engineering and biology. Often one wants to compare such equations with observed time course data, and use this to estimate parameters. Surprisingly, practical algorithms for doing this are relatively poorly developed, particularly in comparison with the sophistication of numerical methods for solving both initial and boundary value problems for differential equations, and for locating and analysing bifurcations. A lack of good numerical fitting methods is particularly problematic in the context of systems biology where only a handful of time points may be available. In this paper, we present a survey of existing algorithms and describe the main approaches. We also introduce and evaluate a new efficient technique for estimating ODEs linear in parameters particularly suited to situations where noise levels are high and the number of data points is low. It employs a spline-based collocation scheme and alternates linear least squares minimization steps with repeated estimates of the noise-free values of the variables. This is reminiscent of expectation-maximization methods widely used for problems with nuisance parameters or missing data.

A segmentation/clustering model for the analysis of array CGH data.

PubMed

Picard, F; Robin, S; Lebarbier, E; Daudin, J-J

2007-09-01

Microarray-CGH (comparative genomic hybridization) experiments are used to detect and map chromosomal imbalances. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose representative sequences share the same relative copy number on average. Segmentation methods constitute a natural framework for the analysis, but they do not provide a biological status for the detected segments. We propose a new model for this segmentation/clustering problem, combining a segmentation model with a mixture model. We present a new hybrid algorithm called dynamic programming-expectation maximization (DP-EM) to estimate the parameters of the model by maximum likelihood. This algorithm combines DP and the EM algorithm. We also propose a model selection heuristic to select the number of clusters and the number of segments. An example of our procedure is presented, based on publicly available data sets. We compare our method to segmentation methods and to hidden Markov models, and we show that the new segmentation/clustering model is a promising alternative that can be applied in the more general context of signal processing.

Direct 4D reconstruction of parametric images incorporating anato-functional joint entropy.

PubMed

Tang, Jing; Kuwabara, Hiroto; Wong, Dean F; Rahmim, Arman

2010-08-07

We developed an anatomy-guided 4D closed-form algorithm to directly reconstruct parametric images from projection data for (nearly) irreversible tracers. Conventional methods consist of individually reconstructing 2D/3D PET data, followed by graphical analysis on the sequence of reconstructed image frames. The proposed direct reconstruction approach maintains the simplicity and accuracy of the expectation-maximization (EM) algorithm by extending the system matrix to include the relation between the parametric images and the measured data. A closed-form solution was achieved using a different hidden complete-data formulation within the EM framework. Furthermore, the proposed method was extended to maximum a posterior reconstruction via incorporation of MR image information, taking the joint entropy between MR and parametric PET features as the prior. Using realistic simulated noisy [(11)C]-naltrindole PET and MR brain images/data, the quantitative performance of the proposed methods was investigated. Significant improvements in terms of noise versus bias performance were demonstrated when performing direct parametric reconstruction, and additionally upon extending the algorithm to its Bayesian counterpart using the MR-PET joint entropy measure.

Acceleration of the direct reconstruction of linear parametric images using nested algorithms.

PubMed

Wang, Guobao; Qi, Jinyi

2010-03-07

Parametric imaging using dynamic positron emission tomography (PET) provides important information for biological research and clinical diagnosis. Indirect and direct methods have been developed for reconstructing linear parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the image reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from raw PET data and are statistically more efficient. However, the convergence rate of direct algorithms can be slow due to the coupling between the reconstruction and kinetic modeling. Here we present two fast gradient-type algorithms for direct reconstruction of linear parametric images. The new algorithms decouple the reconstruction and linear parametric modeling at each iteration by employing the principle of optimization transfer. Convergence speed is accelerated by running more sub-iterations of linear parametric estimation because the computation cost of the linear parametric modeling is much less than that of the image reconstruction. Computer simulation studies demonstrated that the new algorithms converge much faster than the traditional expectation maximization (EM) and the preconditioned conjugate gradient algorithms for dynamic PET.

Mismatch removal via coherent spatial relations

NASA Astrophysics Data System (ADS)

Chen, Jun; Ma, Jiayi; Yang, Changcai; Tian, Jinwen

2014-07-01

We propose a method for removing mismatches from the given putative point correspondences in image pairs based on "coherent spatial relations." Under the Bayesian framework, we formulate our approach as a maximum likelihood problem and solve a coherent spatial relation between the putative point correspondences using an expectation-maximization (EM) algorithm. Our approach associates each point correspondence with a latent variable indicating it as being either an inlier or an outlier, and alternatively estimates the inlier set and recovers the coherent spatial relation. It can handle not only the case of image pairs with rigid motions but also the case of image pairs with nonrigid motions. To parameterize the coherent spatial relation, we choose two-view geometry and thin-plate spline as models for rigid and nonrigid cases, respectively. The mismatches could be successfully removed via the coherent spatial relations after the EM algorithm converges. The quantitative results on various experimental data demonstrate that our method outperforms many state-of-the-art methods, it is not affected by low initial correct match percentages, and is robust to most geometric transformations including a large viewing angle, image rotation, and affine transformation.

Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions

NASA Astrophysics Data System (ADS)

Galimzianova, Alfiia; Lesjak, Žiga; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

2015-03-01

Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov random field (MRF) models are known to give reliable and smooth NABT segmentation. However, the presence of pathology, MR intensity bias and natural tissue-dependent intensity variability altogether represent difficult challenges for a reliable estimation of NABT intensity model based on MR images. In this paper, we propose a novel method for segmentation of normal and pathological structures in brain MR images of multiple sclerosis (MS) patients that is based on locally-adaptive NABT model, a robust method for the estimation of model parameters and a MRF-based segmentation framework. Experiments on multi-sequence brain MR images of 27 MS patients show that, compared to whole-brain model and compared to the widely used Expectation-Maximization Segmentation (EMS) method, the locally-adaptive NABT model increases the accuracy of MS lesion segmentation.

Hypergraph-based anomaly detection of high-dimensional co-occurrences.

PubMed

Silva, Jorge; Willett, Rebecca

2009-03-01

This paper addresses the problem of detecting anomalous multivariate co-occurrences using a limited number of unlabeled training observations. A novel method based on using a hypergraph representation of the data is proposed to deal with this very high-dimensional problem. Hypergraphs constitute an important extension of graphs which allow edges to connect more than two vertices simultaneously. A variational Expectation-Maximization algorithm for detecting anomalies directly on the hypergraph domain without any feature selection or dimensionality reduction is presented. The resulting estimate can be used to calculate a measure of anomalousness based on the False Discovery Rate. The algorithm has O(np) computational complexity, where n is the number of training observations and p is the number of potential participants in each co-occurrence event. This efficiency makes the method ideally suited for very high-dimensional settings, and requires no tuning, bandwidth or regularization parameters. The proposed approach is validated on both high-dimensional synthetic data and the Enron email database, where p > 75,000, and it is shown that it can outperform other state-of-the-art methods.

Studies of a Next-Generation Silicon-Photomultiplier-Based Time-of-Flight PET/CT System.

PubMed

Hsu, David F C; Ilan, Ezgi; Peterson, William T; Uribe, Jorge; Lubberink, Mark; Levin, Craig S

2017-09-01

This article presents system performance studies for the Discovery MI PET/CT system, a new time-of-flight system based on silicon photomultipliers. System performance and clinical imaging were compared between this next-generation system and other commercially available PET/CT and PET/MR systems, as well as between different reconstruction algorithms. Methods: Spatial resolution, sensitivity, noise-equivalent counting rate, scatter fraction, counting rate accuracy, and image quality were characterized with the National Electrical Manufacturers Association NU-2 2012 standards. Energy resolution and coincidence time resolution were measured. Tests were conducted independently on two Discovery MI scanners installed at Stanford University and Uppsala University, and the results were averaged. Back-to-back patient scans were also performed between the Discovery MI, Discovery 690 PET/CT, and SIGNA PET/MR systems. Clinical images were reconstructed using both ordered-subset expectation maximization and Q.Clear (block-sequential regularized expectation maximization with point-spread function modeling) and were examined qualitatively. Results: The averaged full widths at half maximum (FWHMs) of the radial/tangential/axial spatial resolution reconstructed with filtered backprojection at 1, 10, and 20 cm from the system center were, respectively, 4.10/4.19/4.48 mm, 5.47/4.49/6.01 mm, and 7.53/4.90/6.10 mm. The averaged sensitivity was 13.7 cps/kBq at the center of the field of view. The averaged peak noise-equivalent counting rate was 193.4 kcps at 21.9 kBq/mL, with a scatter fraction of 40.6%. The averaged contrast recovery coefficients for the image-quality phantom were 53.7, 64.0, 73.1, 82.7, 86.8, and 90.7 for the 10-, 13-, 17-, 22-, 28-, and 37-mm-diameter spheres, respectively. The average photopeak energy resolution was 9.40% FWHM, and the average coincidence time resolution was 375.4 ps FWHM. Clinical image comparisons between the PET/CT systems demonstrated the high quality of the Discovery MI. Comparisons between the Discovery MI and SIGNA showed a similar spatial resolution and overall imaging performance. Lastly, the results indicated significantly enhanced image quality and contrast-to-noise performance for Q.Clear, compared with ordered-subset expectation maximization. Conclusion: Excellent performance was achieved with the Discovery MI, including 375 ps FWHM coincidence time resolution and sensitivity of 14 cps/kBq. Comparisons between reconstruction algorithms and other multimodal silicon photomultiplier and non-silicon photomultiplier PET detector system designs indicated that performance can be substantially enhanced with this next-generation system. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

In vitro simulator with numerical stress analysis for evaluation of stent-assisted coiling embolization in cerebral aneurysm treatments.

PubMed

Shi, Chaoyang; Kojima, Masahiro; Tercero, Carlos; Najdovski, Zoran; Ikeda, Seiichi; Fukuda, Toshio; Arai, Fumihito; Negoro, Makoto

2014-12-01

There are several complications associated with Stent-assisted Coil Embolization (SACE) in cerebral aneurysm treatments, due to damaging operations by surgeons and undesirable mechanical properties of stents. Therefore, it is necessary to develop an in vitro simulator that provides both training and research for evaluating the mechanical properties of stents. A new in vitro simulator for three-dimensional digital subtraction angiography was constructed, followed by aneurysm models fabricated with new materials. Next, this platform was used to provide training and to conduct photoelastic stress analysis to evaluate the SACE technique. The average interaction stress increasingly varied for the two different stents. Improvements for the Maximum-Likelihood Expectation-Maximization method were developed to reconstruct cross-sections with both thickness and stress information. The technique presented can improve a surgeon's skills and quantify the performance of stents to improve mechanical design and classification. This method can contribute to three-dimensional stress and volume variation evaluation and assess a surgeon's skills. Copyright © 2013 John Wiley & Sons, Ltd.

Estimation of parameters in Shot-Noise-Driven Doubly Stochastic Poisson processes using the EM algorithm--modeling of pre- and postsynaptic spike trains.

PubMed

Mino, H

2007-01-01

To estimate the parameters, the impulse response (IR) functions of some linear time-invariant systems generating intensity processes, in Shot-Noise-Driven Doubly Stochastic Poisson Process (SND-DSPP) in which multivariate presynaptic spike trains and postsynaptic spike trains can be assumed to be modeled by the SND-DSPPs. An explicit formula for estimating the IR functions from observations of multivariate input processes of the linear systems and the corresponding counting process (output process) is derived utilizing the expectation maximization (EM) algorithm. The validity of the estimation formula was verified through Monte Carlo simulations in which two presynaptic spike trains and one postsynaptic spike train were assumed to be observable. The IR functions estimated on the basis of the proposed identification method were close to the true IR functions. The proposed method will play an important role in identifying the input-output relationship of pre- and postsynaptic neural spike trains in practical situations.

Ischemic stroke enhancement in computed tomography scans using a computational approach

NASA Astrophysics Data System (ADS)

Alves, Allan F. F.; Pavan, Ana L. M.; Jennane, Rachid; Miranda, José R. A.; Freitas, Carlos C. M.; Abdala, Nitamar; Pina, Diana R.

2018-03-01

In this work, a novel approach was proposed to enhance the visual perception of ischemic stroke in computed tomography scans. Through different image processing techniques, we enabled less experienced physicians, to reliably detect early signs of stroke. A set of 40 retrospective CT scans of patients were used, divided into two groups: 25 cases of acute ischemic stroke and 15 normal cases used as control group. All cases were obtained within 4 hours of symptoms onset. Our approach was based on the variational decomposition model and three different segmentation methods. A test determined observers' performance to correctly diagnose stroke cases. The Expectation Maximization method provided the best results among all observers. The overall sensitivity of the observer's analysis was 64% and increased to 79%. The overall specificity was 67% and increased to 78%. These results show the importance of a computational tool to assist neuroradiology decisions, especially in critical situations such as the diagnosis of ischemic stroke.

Monitoring the distribution of prompt gamma rays in boron neutron capture therapy using a multiple-scattering Compton camera: A Monte Carlo simulation study

NASA Astrophysics Data System (ADS)

Lee, Taewoong; Lee, Hyounggun; Lee, Wonho

2015-10-01

This study evaluated the use of Compton imaging technology to monitor prompt gamma rays emitted by 10B in boron neutron capture therapy (BNCT) applied to a computerized human phantom. The Monte Carlo method, including particle-tracking techniques, was used for simulation. The distribution of prompt gamma rays emitted by the phantom during irradiation with neutron beams is closely associated with the distribution of the boron in the phantom. Maximum likelihood expectation maximization (MLEM) method was applied to the information obtained from the detected prompt gamma rays to reconstruct the distribution of the tumor including the boron uptake regions (BURs). The reconstructed Compton images of the prompt gamma rays were combined with the cross-sectional images of the human phantom. Quantitative analysis of the intensity curves showed that all combined images matched the predetermined conditions of the simulation. The tumors including the BURs were distinguishable if they were more than 2 cm apart.

A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types

PubMed Central

Elashoff, Robert M.; Li, Gang; Li, Ning

2009-01-01

Summary In this article we study a joint model for longitudinal measurements and competing risks survival data. Our joint model provides a flexible approach to handle possible nonignorable missing data in the longitudinal measurements due to dropout. It is also an extension of previous joint models with a single failure type, offering a possible way to model informatively censored events as a competing risk. Our model consists of a linear mixed effects submodel for the longitudinal outcome and a proportional cause-specific hazards frailty submodel (Prentice et al., 1978, Biometrics 34, 541-554) for the competing risks survival data, linked together by some latent random effects. We propose to obtain the maximum likelihood estimates of the parameters by an expectation maximization (EM) algorithm and estimate their standard errors using a profile likelihood method. The developed method works well in our simulation studies and is applied to a clinical trial for the scleroderma lung disease. PMID:18162112

Application and performance of an ML-EM algorithm in NEXT

NASA Astrophysics Data System (ADS)

Simón, A.; Lerche, C.; Monrabal, F.; Gómez-Cadenas, J. J.; Álvarez, V.; Azevedo, C. D. R.; Benlloch-Rodríguez, J. M.; Borges, F. I. G. M.; Botas, A.; Cárcel, S.; Carrión, J. V.; Cebrián, S.; Conde, C. A. N.; Díaz, J.; Diesburg, M.; Escada, J.; Esteve, R.; Felkai, R.; Fernandes, L. M. P.; Ferrario, P.; Ferreira, A. L.; Freitas, E. D. C.; Goldschmidt, A.; González-Díaz, D.; Gutiérrez, R. M.; Hauptman, J.; Henriques, C. A. O.; Hernandez, A. I.; Hernando Morata, J. A.; Herrero, V.; Jones, B. J. P.; Labarga, L.; Laing, A.; Lebrun, P.; Liubarsky, I.; López-March, N.; Losada, M.; Martín-Albo, J.; Martínez-Lema, G.; Martínez, A.; McDonald, A. D.; Monteiro, C. M. B.; Mora, F. J.; Moutinho, L. M.; Muñoz Vidal, J.; Musti, M.; Nebot-Guinot, M.; Novella, P.; Nygren, D. R.; Palmeiro, B.; Para, A.; Pérez, J.; Querol, M.; Renner, J.; Ripoll, L.; Rodríguez, J.; Rogers, L.; Santos, F. P.; dos Santos, J. M. F.; Sofka, C.; Sorel, M.; Stiegler, T.; Toledo, J. F.; Torrent, J.; Tsamalaidze, Z.; Veloso, J. F. C. A.; Webb, R.; White, J. T.; Yahlali, N.

2017-08-01

The goal of the NEXT experiment is the observation of neutrinoless double beta decay in 136Xe using a gaseous xenon TPC with electroluminescent amplification and specialized photodetector arrays for calorimetry and tracking. The NEXT Collaboration is exploring a number of reconstruction algorithms to exploit the full potential of the detector. This paper describes one of them: the Maximum Likelihood Expectation Maximization (ML-EM) method, a generic iterative algorithm to find maximum-likelihood estimates of parameters that has been applied to solve many different types of complex inverse problems. In particular, we discuss a bi-dimensional version of the method in which the photosensor signals integrated over time are used to reconstruct a transverse projection of the event. First results show that, when applied to detector simulation data, the algorithm achieves nearly optimal energy resolution (better than 0.5% FWHM at the Q value of 136Xe) for events distributed over the full active volume of the TPC.

Load controller and method to enhance effective capacity of a photovoltaic power supply

DOEpatents

Perez, Richard

2000-01-01

A load controller and method are provided for maximizing effective capacity of a non-controllable, renewable power supply coupled to a variable electrical load also coupled to a conventional power grid. Effective capacity is enhanced by monitoring power output of the renewable supply and loading, and comparing the loading against the power output and a load adjustment threshold determined from an expected peak loading. A value for a load adjustment parameter is calculated by subtracting the renewable supply output and the load adjustment parameter from the current load. This value is then employed to control the variable load in an amount proportional to the value of the load control parameter when the parameter is within a predefined range. By so controlling the load, the effective capacity of the non-controllable, renewable power supply is increased without any attempt at operational feedback control of the renewable supply. The renewable supply may comprise, for example, a photovoltaic power supply or a wind-based power supply.

Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering.

PubMed

Chah, E; Hok, V; Della-Chiesa, A; Miller, J J H; O'Mara, S M; Reilly, R B

2011-02-01

This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.

Multivariate longitudinal data analysis with censored and intermittent missing responses.

PubMed

Lin, Tsung-I; Lachos, Victor H; Wang, Wan-Lun

2018-05-08

The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements because of a detection limit of the assay in combination with unavoidable missing values arising when subjects miss some of their scheduled visits intermittently. This paper presents a generalization of the MLMM approach, called the MLMM-CM, for a joint analysis of the multivariate longitudinal data with censored and intermittent missing responses. A computationally feasible expectation maximization-based procedure is developed to carry out maximum likelihood estimation within the MLMM-CM framework. Moreover, the asymptotic standard errors of fixed effects are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and a case study from an AIDS clinical trial. Experimental results reveal that the proposed method is able to provide more satisfactory performance as compared with the traditional MLMM approach. Copyright © 2018 John Wiley & Sons, Ltd.

Linkage disequilibrium interval mapping of quantitative trait loci.

PubMed

Boitard, Simon; Abdallah, Jihad; de Rochambeau, Hubert; Cierco-Ayrolles, Christine; Mangin, Brigitte

2006-03-16

For many years gene mapping studies have been performed through linkage analyses based on pedigree data. Recently, linkage disequilibrium methods based on unrelated individuals have been advocated as powerful tools to refine estimates of gene location. Many strategies have been proposed to deal with simply inherited disease traits. However, locating quantitative trait loci is statistically more challenging and considerable research is needed to provide robust and computationally efficient methods. Under a three-locus Wright-Fisher model, we derived approximate expressions for the expected haplotype frequencies in a population. We considered haplotypes comprising one trait locus and two flanking markers. Using these theoretical expressions, we built a likelihood-maximization method, called HAPim, for estimating the location of a quantitative trait locus. For each postulated position, the method only requires information from the two flanking markers. Over a wide range of simulation scenarios it was found to be more accurate than a two-marker composite likelihood method. It also performed as well as identity by descent methods, whilst being valuable in a wider range of populations. Our method makes efficient use of marker information, and can be valuable for fine mapping purposes. Its performance is increased if multiallelic markers are available. Several improvements can be developed to account for more complex evolution scenarios or provide robust confidence intervals for the location estimates.

Power maximization of a point absorber wave energy converter using improved model predictive control

NASA Astrophysics Data System (ADS)

Milani, Farideh; Moghaddam, Reihaneh Kardehi

2017-08-01

This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves' behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method's efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.

Condition-dependent mate choice: A stochastic dynamic programming approach.

PubMed

Frame, Alicia M; Mills, Alex F

2014-09-01

We study how changing female condition during the mating season and condition-dependent search costs impact female mate choice, and what strategies a female could employ in choosing mates to maximize her own fitness. We address this problem via a stochastic dynamic programming model of mate choice. In the model, a female encounters males sequentially and must choose whether to mate or continue searching. As the female searches, her own condition changes stochastically, and she incurs condition-dependent search costs. The female attempts to maximize the quality of the offspring, which is a function of the female's condition at mating and the quality of the male with whom she mates. The mating strategy that maximizes the female's net expected reward is a quality threshold. We compare the optimal policy with other well-known mate choice strategies, and we use simulations to examine how well the optimal policy fares under imperfect information. Copyright © 2014 Elsevier Inc. All rights reserved.

College Student Perceptions and Learning Points from the Formal University Judicial Process: A Multiple Case Study

ERIC Educational Resources Information Center

Lucas, Christopher M.

2009-01-01

For educators in the field of higher education and judicial affairs, issues are growing. Campus adjudicators must somehow maximize every opportunity for student education and development in the context of declining resources and increasing expectations of public accountability. Numbers of student misconduct cases, including matters of violence and…

Optimizing Experimental Designs Relative to Costs and Effect Sizes.

ERIC Educational Resources Information Center

Headrick, Todd C.; Zumbo, Bruno D.

A general model is derived for the purpose of efficiently allocating integral numbers of units in multi-level designs given prespecified power levels. The derivation of the model is based on a constrained optimization problem that maximizes a general form of a ratio of expected mean squares subject to a budget constraint. This model provides more…

Thinning Guidelines For Loblolly Pine Plantations in Eastern Texas Based on Alternative Management Criteria

Treesearch

Charles T. Stiff; William F. Stansfield

2004-01-01

Separate thinning guidelines were developed for maximizing land expectation value (LEV), present net worth (PNW), and total sawlog yield (TSY) of existing and future loblolly pine (Pinus taeda L.) plantations in eastern Texas. The guidelines were created using data from simulated stands which were thinned one time during their rotation using a...

77 FR 21955 - Fisheries of the Caribbean, Gulf of Mexico, and South Atlantic; Reef Fish Fishery of the Gulf of...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-12

... the season through December 31, the end of the fishing year, thus maximizing this sector's opportunity... expected to significantly reduce profits for a substantial number of small entities. This proposed rule... and associated increased profits for for-hire entities associated with the recreational harvest of red...

Clustering and Profiling Students According to Their Interactions with an Intelligent Tutoring System Fostering Self-Regulated Learning

ERIC Educational Resources Information Center

Bouchet, Francois; Harley, Jason M.; Trevors, Gregory J.; Azevedo, Roger

2013-01-01

In this paper, we present the results obtained using a clustering algorithm (Expectation-Maximization) on data collected from 106 college students learning about the circulatory system with MetaTutor, an agent-based Intelligent Tutoring System (ITS) designed to foster self-regulated learning (SRL). The three extracted clusters were validated and…

Maximizing the value of limited irrigation water: USDA researchers study how producers on limited irrigation can save water and be profitable

USDA-ARS?s Scientific Manuscript database

Water shortages are responsible for the greatest crop losses around the world and are expected to worsen. In arid areas where agriculture is dependent on irrigation, various forms of deficit irrigation management have been suggested to optimize crop yields for available soil water. The relationshi...

Optimizing reserve expansion for disjunct populations of San Joaquin kit fox

Treesearch

Robert G. Haight; Brian Cypher; Patrick A. Kelly; Scott Phillips; Katherine Ralls; Hugh P. Possingham

2004-01-01

Expanding habitat protection is a common strategy for species conservation. We present a model to optimize the expansion of reserves for disjunct populations of an endangered species. The objective is to maximize the expected number of surviving populations subject to budget and habitat constraints. The model accounts for benefits of reserve expansion in terms of...

Benefits of advanced software techniques for mission planning systems

NASA Technical Reports Server (NTRS)

Gasquet, A.; Parrod, Y.; Desaintvincent, A.

1994-01-01

The increasing complexity of modern spacecraft, and the stringent requirement for maximizing their mission return, call for a new generation of Mission Planning Systems (MPS). In this paper, we discuss the requirements for the Space Mission Planning and the benefits which can be expected from Artificial Intelligence techniques through examples of applications developed by Matra Marconi Space.

Benefits of advanced software techniques for mission planning systems

NASA Astrophysics Data System (ADS)

Gasquet, A.; Parrod, Y.; Desaintvincent, A.

1994-10-01

The increasing complexity of modern spacecraft, and the stringent requirement for maximizing their mission return, call for a new generation of Mission Planning Systems (MPS). In this paper, we discuss the requirements for the Space Mission Planning and the benefits which can be expected from Artificial Intelligence techniques through examples of applications developed by Matra Marconi Space.

What Influences Young Canadians to Pursue Post-Secondary Studies? Final Report

ERIC Educational Resources Information Center

Dubois, Julie

2002-01-01

This paper uses the theory of human capital to model post-secondary education enrolment decisions. The model is based on the assumption that high school graduates assess the costs and benefits associated with various levels of post-secondary education (college or university) and select the option that maximizes the expected net present value.…

Towards a framework for selection of supervisory control for commercial buildings: HVAC system energy efficiency

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramachandran, Thiagarajan; Kundu, Soumya; Chen, Yan

This paper develops and utilizes an optimization based framework to investigate the maximal energy efficiency potentially attainable by HVAC system operation in a non-predictive context. Performance is evaluated relative to the existing state of the art set point reset strategies. The expected efficiency increase driven by operation constraints relaxations is evaluated.

Towards a framework for selection of supervisory control for commercial buildings: HVAC system energy efficiency

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramachandran, Thiagarajan; Kundu, Soumya; Chen, Yan

This paper develops and utilizes an optimization based framework to investigate the maximal energy efficiency potentially attainable by HVAC system operation in a non-predictive context. Performance is evaluated relative to the existing state of the art set-point reset strategies. The expected efficiency increase driven by operation constraints relaxations is evaluated.

Optimizing the Use of Response Times for Item Selection in Computerized Adaptive Testing

ERIC Educational Resources Information Center

Choe, Edison M.; Kern, Justin L.; Chang, Hua-Hua

2018-01-01

Despite common operationalization, measurement efficiency of computerized adaptive testing should not only be assessed in terms of the number of items administered but also the time it takes to complete the test. To this end, a recent study introduced a novel item selection criterion that maximizes Fisher information per unit of expected response…

Feeding Practices and Expectations among Middle-Class Anglo and Puerto Rican Mothers of 12-Month-Old Infants.

ERIC Educational Resources Information Center

Schulze, Pamela A.; Harwood, Robin L.; Schoelmerich, Axel

2001-01-01

Investigated differences in beliefs and practices about infant feeding among middle class Anglo and Puerto Rican mothers. Interviews and observations indicated that Anglo mothers reported earlier attainment of self-feeding and more emphasis on child rearing goals related to self-maximization. Puerto Rican mothers reported later attainment of…

Solar-Energy System for a Commercial Building--Topeka, Kansas

NASA Technical Reports Server (NTRS)

1982-01-01

Report describes a solar-energy system for space heating, cooling and domestic hot water at a 5,600 square-foot (520-square-meter) Topeka, Kansas, commercial building. System is expected to provide 74% of annual cooling load, 47% of heating load, and 95% of domestic hot-water load. System was included in building design to maximize energy conservation.

Magnetic Tape Storage and Handling: A Guide for Libraries and Archives.

ERIC Educational Resources Information Center

Van Bogart, John W. C.

This document provides a guide on how to properly store and care for magnetic media to maximize their life expectancies. An introduction compares magnetic media to paper and film and outlines the scope of the report. The second section discusses things that can go wrong with magnetic media. Binder degradation, magnetic particle instabilities,…

Informed actions: where to cost effectively manage multiple threats to species to maximize return on investment.

PubMed

Auerbach, Nancy A; Tulloch, Ayesha I T; Possingham, Hugh P

Conservation practitioners, faced with managing multiple threats to biodiversity and limited funding, must prioritize investment in different management actions. From an economic perspective, it is routine practice to invest where the highest rate of return is expected. This return-on-investment (ROI) thinking can also benefit species conservation, and researchers are developing sophisticated approaches to support decision-making for cost-effective conservation. However, applied use of these approaches is limited. Managers may be wary of “black-box” algorithms or complex methods that are difficult to explain to funding agencies. As an alternative, we demonstrate the use of a basic ROI analysis for determining where to invest in cost-effective management to address threats to species. This method can be applied using basic geographic information system and spreadsheet calculations. We illustrate the approach in a management action prioritization for a biodiverse region of eastern Australia. We use ROI to prioritize management actions for two threats to a suite of threatened species: habitat degradation by cattle grazing, and predation by invasive red foxes (Vulpes vulpes). We show how decisions based on cost-effective threat management depend upon how expected benefits to species are defined and how benefits and costs co-vary. By considering a combination of species richness, restricted habitats, species vulnerability, and costs of management actions, small investments can result in greater expected benefit compared with management decisions that consider only species richness. Furthermore, a landscape management strategy that implements multiple actions is more efficient than managing only for one threat, or more traditional approaches that don't consider ROI. Our approach provides transparent and logical decision support for prioritizing different actions intended to abate threats associated with multiple species; it is of use when managers need a justifiable and repeatable approach to investment.

On the Rate of Relaxation for the Landau Kinetic Equation and Related Models

NASA Astrophysics Data System (ADS)

Bobylev, Alexander; Gamba, Irene M.; Zhang, Chenglong

2017-08-01

We study the rate of relaxation to equilibrium for Landau kinetic equation and some related models by considering the relatively simple case of radial solutions of the linear Landau-type equations. The well-known difficulty is that the evolution operator has no spectral gap, i.e. its spectrum is not separated from zero. Hence we do not expect purely exponential relaxation for large values of time t>0. One of the main goals of our work is to numerically identify the large time asymptotics for the relaxation to equilibrium. We recall the work of Strain and Guo (Arch Rat Mech Anal 187:287-339 2008, Commun Partial Differ Equ 31:17-429 2006), who rigorously show that the expected law of relaxation is \\exp (-ct^{2/3}) with some c > 0. In this manuscript, we find an heuristic way, performed by asymptotic methods, that finds this "law of two thirds", and then study this question numerically. More specifically, the linear Landau equation is approximated by a set of ODEs based on expansions in generalized Laguerre polynomials. We analyze the corresponding quadratic form and the solution of these ODEs in detail. It is shown that the solution has two different asymptotic stages for large values of time t and maximal order of polynomials N: the first one focus on intermediate asymptotics which agrees with the "law of two thirds" for moderately large values of time t and then the second one on absolute, purely exponential asymptotics for very large t, as expected for linear ODEs. We believe that appearance of intermediate asymptotics in finite dimensional approximations must be a generic behavior for different classes of equations in functional spaces (some PDEs, Boltzmann equations for soft potentials, etc.) and that our methods can be applied to related problems.

Using whole disease modeling to inform resource allocation decisions: economic evaluation of a clinical guideline for colorectal cancer using a single model.

PubMed

Tappenden, Paul; Chilcott, Jim; Brennan, Alan; Squires, Hazel; Glynne-Jones, Rob; Tappenden, Janine

2013-06-01

To assess the feasibility and value of simulating whole disease and treatment pathways within a single model to provide a common economic basis for informing resource allocation decisions. A patient-level simulation model was developed with the intention of being capable of evaluating multiple topics within National Institute for Health and Clinical Excellence's colorectal cancer clinical guideline. The model simulates disease and treatment pathways from preclinical disease through to detection, diagnosis, adjuvant/neoadjuvant treatments, follow-up, curative/palliative treatments for metastases, supportive care, and eventual death. The model parameters were informed by meta-analyses, randomized trials, observational studies, health utility studies, audit data, costing sources, and expert opinion. Unobservable natural history parameters were calibrated against external data using Bayesian Markov chain Monte Carlo methods. Economic analysis was undertaken using conventional cost-utility decision rules within each guideline topic and constrained maximization rules across multiple topics. Under usual processes for guideline development, piecewise economic modeling would have been used to evaluate between one and three topics. The Whole Disease Model was capable of evaluating 11 of 15 guideline topics, ranging from alternative diagnostic technologies through to treatments for metastatic disease. The constrained maximization analysis identified a configuration of colorectal services that is expected to maximize quality-adjusted life-year gains without exceeding current expenditure levels. This study indicates that Whole Disease Model development is feasible and can allow for the economic analysis of most interventions across a disease service within a consistent conceptual and mathematical infrastructure. This disease-level modeling approach may be of particular value in providing an economic basis to support other clinical guidelines. Copyright © 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Tuning micropillar tapering for optimal friction performance of thermoplastic gecko-inspired adhesive.

PubMed

Kim, Yongkwan; Chung, Yunsie; Tsao, Angela; Maboudian, Roya

2014-05-14

We present a fabrication method and friction testing of a gecko-inspired thermoplastic micropillar array with control over the tapering angle of the pillar sidewall. A combination of deep reactive ion etching of vertical silicon pillars and subsequent maskless chemical etching produces templates with various widths and degrees of taper, which are then replicated with low-density polyethylene. As the silicon pillars on the template are chemically etched in a bath consisting of hydrofluoric acid, nitric acid, and acetic acid (HNA), the pillars are progressively thinned, then shortened. The replicated polyethylene pillar arrays exhibit a corresponding increase in friction as the stiffness is reduced with thinning and then a decrease in friction as the stiffness is again increased. The dilution of the HNA bath in water influences the tapering angle of the silicon pillars. The friction of the replicated pillars is maximized for the taper angle that maximizes the contact area at the tip which in turn is influenced by the stiffness of the tapered pillars. To provide insights on how changes in microscale geometry and contact behavior may affect friction of the pillar array, the pillars are imaged by scanning electron microscopy after friction testing, and the observed deformation behavior from shearing is related to the magnitude of the macroscale friction values. It is shown that the tapering angle critically changes the pillar compliance and the available contact area. Simple finite element modeling calculations are performed to support that the observed deformation is consistent with what is expected from a mechanical analysis. We conclude that friction can be maximized via proper pillar tapering with low stiffness that still maintains enough contact area to ensure high adhesion.

A New Look at the Impact of Maximizing on Unhappiness: Two Competing Mediating Effects

PubMed Central

Peng, Jiaxi; Zhang, Jiaxi; Zhang, Yan; Gong, Pinjia; Han, Bing; Sun, Hao; Cao, Fei; Miao, Danmin

2018-01-01

The current study aims to explore how the decision-making style of maximizing affects subjective well-being (SWB), which mainly focuses on the confirmation of the mediator role of regret and suppressing role of achievement motivation. A total of 402 Chinese undergraduate students participated in this study, in which they responded to the maximization, regret, and achievement motivation scales and SWB measures. Results suggested that maximizing significantly predicted SWB. Moreover, regret and achievement motivation (hope for success dimension) could completely mediate and suppress this effect. That is, two competing indirect pathways exist between maximizing and SWB. One pathway is through regret. Maximizing typically leads one to regret, which could negatively predict SWB. Alternatively, maximizing could lead to high levels of hope for success, which were positively correlated with SWB. Findings offered a complex method of thinking about the relationship between maximizing and SWB. PMID:29467694

The usefulness of biliopancreatic diversion/Scopinaro operation in treatment of patients with Prader-Willi syndrome

PubMed Central

Michalik, Maciej; Frask, Agata; Zdrojewski, Michal; Doboszynska, Anna

2015-01-01

Prader-Willi syndrome (PWS) is the most common form of obesity with a genetic basis. The short expected survival time due to numerous accompanying diseases and their complications is the reason for research on the maximally efficient method of treatment of obesity in this syndrome. Undertaken attempts of conservative treatment, for example with somatostatin, are ineffective. It seems that the only effective treatment of obesity in this syndrome may be surgical. In this article we present 2 cases of patients with PWS who underwent surgery consisting of biliopancreatic diversion (BPD)/Scopinaro procedure. The BPD/Scopinaro operation in selected cases of disciplined patients with a co-operative family, which we find of key importance, can be considered as one option of treatment of this syndrome in patients with prior neglect of conservative treatment. PMID:26240637

SubspaceEM: A Fast Maximum-a-posteriori Algorithm for Cryo-EM Single Particle Reconstruction

PubMed Central

Dvornek, Nicha C.; Sigworth, Fred J.; Tagare, Hemant D.

2015-01-01

Single particle reconstruction methods based on the maximum-likelihood principle and the expectation-maximization (E–M) algorithm are popular because of their ability to produce high resolution structures. However, these algorithms are computationally very expensive, requiring a network of computational servers. To overcome this computational bottleneck, we propose a new mathematical framework for accelerating maximum-likelihood reconstructions. The speedup is by orders of magnitude and the proposed algorithm produces similar quality reconstructions compared to the standard maximum-likelihood formulation. Our approach uses subspace approximations of the cryo-electron microscopy (cryo-EM) data and projection images, greatly reducing the number of image transformations and comparisons that are computed. Experiments using simulated and actual cryo-EM data show that speedup in overall execution time compared to traditional maximum-likelihood reconstruction reaches factors of over 300. PMID:25839831

Decision Making in Kidney Paired Donation Programs with Altruistic Donors*

PubMed Central

Li, Yijiang; Song, Peter X.-K.; Leichtman, Alan B.; Rees, Michael A.; Kalbfleisch, John D.

2014-01-01

In recent years, kidney paired donation (KPD) has been extended to include living non-directed or altruistic donors, in which an altruistic donor donates to the candidate of an incompatible donor-candidate pair with the understanding that the donor in that pair will further donate to the candidate of a second pair, and so on; such a process continues and thus forms an altruistic donor-initiated chain. In this paper, we propose a novel strategy to sequentially allocate the altruistic donor (or bridge donor) so as to maximize the expected utility; analogous to the way a computer plays chess, the idea is to evaluate different allocations for each altruistic donor (or bridge donor) by looking several moves ahead in a derived look-ahead search tree. Simulation studies are provided to illustrate and evaluate our proposed method. PMID:25309603

Classification Comparisons Between Compact Polarimetric and Quad-Pol SAR Imagery

NASA Astrophysics Data System (ADS)

Souissi, Boularbah; Doulgeris, Anthony P.; Eltoft, Torbjørn

2015-04-01

Recent interest in dual-pol SAR systems has lead to a novel approach, the so-called compact polarimetric imaging mode (CP) which attempts to reconstruct fully polarimetric information based on a few simple assumptions. In this work, the CP image is simulated from the full quad-pol (QP) image. We present here the initial comparison of polarimetric information content between QP and CP imaging modes. The analysis of multi-look polarimetric covariance matrix data uses an automated statistical clustering method based upon the expectation maximization (EM) algorithm for finite mixture modeling, using the complex Wishart probability density function. Our results showed that there are some different characteristics between the QP and CP modes. The classification is demonstrated using a E-SAR and Radarsat2 polarimetric SAR images acquired over DLR Oberpfaffenhofen in Germany and Algiers in Algeria respectively.

Likelihood-based inference for discretely observed birth-death-shift processes, with applications to evolution of mobile genetic elements.

PubMed

Xu, Jason; Guttorp, Peter; Kato-Maeda, Midori; Minin, Vladimir N

2015-12-01

Continuous-time birth-death-shift (BDS) processes are frequently used in stochastic modeling, with many applications in ecology and epidemiology. In particular, such processes can model evolutionary dynamics of transposable elements-important genetic markers in molecular epidemiology. Estimation of the effects of individual covariates on the birth, death, and shift rates of the process can be accomplished by analyzing patient data, but inferring these rates in a discretely and unevenly observed setting presents computational challenges. We propose a multi-type branching process approximation to BDS processes and develop a corresponding expectation maximization algorithm, where we use spectral techniques to reduce calculation of expected sufficient statistics to low-dimensional integration. These techniques yield an efficient and robust optimization routine for inferring the rates of the BDS process, and apply broadly to multi-type branching processes whose rates can depend on many covariates. After rigorously testing our methodology in simulation studies, we apply our method to study intrapatient time evolution of IS6110 transposable element, a genetic marker frequently used during estimation of epidemiological clusters of Mycobacterium tuberculosis infections. © 2015, The International Biometric Society.

[Measures to reduce lighting-related energy use and costs at hospital nursing stations].

PubMed

Su, Chiu-Ching; Chen, Chen-Hui; Chen, Shu-Hwa; Ping, Tsui-Chu

2011-06-01

Hospitals have long been expected to deliver medical services in an environment that is comfortable and bright. This expectation keeps hospital energy demand stubbornly high and energy costs spiraling due to escalating utility fees. Hospitals must identify appropriate strategies to control electricity usage in order to control operating costs effectively. This paper proposes several electricity saving measures that both support government policies aimed at reducing global warming and help reduce energy consumption at the authors' hospital. The authors held educational seminars, established a website teaching energy saving methods, maximized facility and equipment use effectiveness (e.g., adjusting lamp placements, power switch and computer saving modes), posted signs promoting electricity saving, and established a regularized energy saving review mechanism. After implementation, average nursing staff energy saving knowledge had risen from 71.8% to 100% and total nursing station electricity costs fell from NT$16,456 to NT$10,208 per month, representing an effective monthly savings of 37.9% (NT$6,248). This project demonstrated the ability of a program designed to slightly modify nursing staff behavior to achieve effective and meaningful results in reducing overall electricity use.

Robust EM Continual Reassessment Method in Oncology Dose Finding

PubMed Central

Yuan, Ying; Yin, Guosheng

2012-01-01

The continual reassessment method (CRM) is a commonly used dose-finding design for phase I clinical trials. Practical applications of this method have been restricted by two limitations: (1) the requirement that the toxicity outcome needs to be observed shortly after the initiation of the treatment; and (2) the potential sensitivity to the prespecified toxicity probability at each dose. To overcome these limitations, we naturally treat the unobserved toxicity outcomes as missing data, and use the expectation-maximization (EM) algorithm to estimate the dose toxicity probabilities based on the incomplete data to direct dose assignment. To enhance the robustness of the design, we propose prespecifying multiple sets of toxicity probabilities, each set corresponding to an individual CRM model. We carry out these multiple CRMs in parallel, across which model selection and model averaging procedures are used to make more robust inference. We evaluate the operating characteristics of the proposed robust EM-CRM designs through simulation studies and show that the proposed methods satisfactorily resolve both limitations of the CRM. Besides improving the MTD selection percentage, the new designs dramatically shorten the duration of the trial, and are robust to the prespecification of the toxicity probabilities. PMID:22375092

Calibration of DEM parameters on shear test experiments using Kriging method

NASA Astrophysics Data System (ADS)

Bednarek, Xavier; Martin, Sylvain; Ndiaye, Abibatou; Peres, Véronique; Bonnefoy, Olivier

2017-06-01

Calibration of powder mixing simulation using Discrete-Element-Method is still an issue. Achieving good agreement with experimental results is difficult because time-efficient use of DEM involves strong assumptions. This work presents a methodology to calibrate DEM parameters using Efficient Global Optimization (EGO) algorithm based on Kriging interpolation method. Classical shear test experiments are used as calibration experiments. The calibration is made on two parameters - Young modulus and friction coefficient. The determination of the minimal number of grains that has to be used is a critical step. Simulations of a too small amount of grains would indeed not represent the realistic behavior of powder when using huge amout of grains will be strongly time consuming. The optimization goal is the minimization of the objective function which is the distance between simulated and measured behaviors. The EGO algorithm uses the maximization of the Expected Improvement criterion to find next point that has to be simulated. This stochastic criterion handles with the two interpolations made by the Kriging method : prediction of the objective function and estimation of the error made. It is thus able to quantify the improvement in the minimization that new simulations at specified DEM parameters would lead to.

A method for evaluating the murine pulmonary vasculature using micro-computed tomography.

PubMed

Phillips, Michael R; Moore, Scott M; Shah, Mansi; Lee, Clara; Lee, Yueh Z; Faber, James E; McLean, Sean E

2017-01-01

Significant mortality and morbidity are associated with alterations in the pulmonary vasculature. While techniques have been described for quantitative morphometry of whole-lung arterial trees in larger animals, no methods have been described in mice. We report a method for the quantitative assessment of murine pulmonary arterial vasculature using high-resolution computed tomography scanning. Mice were harvested at 2 weeks, 4 weeks, and 3 months of age. The pulmonary artery vascular tree was pressure perfused to maximal dilation with a radio-opaque casting material with viscosity and pressure set to prevent capillary transit and venous filling. The lungs were fixed and scanned on a specimen computed tomography scanner at 8-μm resolution, and the vessels were segmented. Vessels were grouped into categories based on lumen diameter and branch generation. Robust high-resolution segmentation was achieved, permitting detailed quantitation of pulmonary vascular morphometrics. As expected, postnatal lung development was associated with progressive increase in small-vessel number and arterial branching complexity. These methods for quantitative analysis of the pulmonary vasculature in postnatal and adult mice provide a useful tool for the evaluation of mouse models of disease that affect the pulmonary vasculature. Copyright © 2016 Elsevier Inc. All rights reserved.

Joint Prior Learning for Visual Sensor Network Noisy Image Super-Resolution

PubMed Central

Yue, Bo; Wang, Shuang; Liang, Xuefeng; Jiao, Licheng; Xu, Caijin

2016-01-01

The visual sensor network (VSN), a new type of wireless sensor network composed of low-cost wireless camera nodes, is being applied for numerous complex visual analyses in wild environments, such as visual surveillance, object recognition, etc. However, the captured images/videos are often low resolution with noise. Such visual data cannot be directly delivered to the advanced visual analysis. In this paper, we propose a joint-prior image super-resolution (JPISR) method using expectation maximization (EM) algorithm to improve VSN image quality. Unlike conventional methods that only focus on upscaling images, JPISR alternatively solves upscaling mapping and denoising in the E-step and M-step. To meet the requirement of the M-step, we introduce a novel non-local group-sparsity image filtering method to learn the explicit prior and induce the geometric duality between images to learn the implicit prior. The EM algorithm inherently combines the explicit prior and implicit prior by joint learning. Moreover, JPISR does not rely on large external datasets for training, which is much more practical in a VSN. Extensive experiments show that JPISR outperforms five state-of-the-art methods in terms of both PSNR, SSIM and visual perception. PMID:26927114

The value of foresight: how prospection affects decision-making.

PubMed

Pezzulo, Giovanni; Rigoli, Francesco

2011-01-01

Traditional theories of decision-making assume that utilities are based on the intrinsic value of outcomes; in turn, these values depend on associations between expected outcomes and the current motivational state of the decision-maker. This view disregards the fact that humans (and possibly other animals) have prospection abilities, which permit anticipating future mental processes and motivational and emotional states. For instance, we can evaluate future outcomes in light of the motivational state we expect to have when the outcome is collected, not (only) when we make a decision. Consequently, we can plan for the future and choose to store food to be consumed when we expect to be hungry, not immediately. Furthermore, similarly to any expected outcome, we can assign a value to our anticipated mental processes and emotions. It has been reported that (in some circumstances) human subjects prefer to receive an unavoidable punishment immediately, probably because they are anticipating the dread associated with the time spent waiting for the punishment. This article offers a formal framework to guide neuroeconomic research on how prospection affects decision-making. The model has two characteristics. First, it uses model-based Bayesian inference to describe anticipation of cognitive and motivational processes. Second, the utility-maximization process considers these anticipations in two ways: to evaluate outcomes (e.g., the pleasure of eating a pie is evaluated differently at the beginning of a dinner, when one is hungry, and at the end of the dinner, when one is satiated), and as outcomes having a value themselves (e.g., the case of dread as a cost of waiting for punishment). By explicitly accounting for the relationship between prospection and value, our model provides a framework to reconcile the utility-maximization approach with psychological phenomena such as planning for the future and dread.

The Value of Foresight: How Prospection Affects Decision-Making

PubMed Central

Pezzulo, Giovanni; Rigoli, Francesco

2011-01-01

Traditional theories of decision-making assume that utilities are based on the intrinsic value of outcomes; in turn, these values depend on associations between expected outcomes and the current motivational state of the decision-maker. This view disregards the fact that humans (and possibly other animals) have prospection abilities, which permit anticipating future mental processes and motivational and emotional states. For instance, we can evaluate future outcomes in light of the motivational state we expect to have when the outcome is collected, not (only) when we make a decision. Consequently, we can plan for the future and choose to store food to be consumed when we expect to be hungry, not immediately. Furthermore, similarly to any expected outcome, we can assign a value to our anticipated mental processes and emotions. It has been reported that (in some circumstances) human subjects prefer to receive an unavoidable punishment immediately, probably because they are anticipating the dread associated with the time spent waiting for the punishment. This article offers a formal framework to guide neuroeconomic research on how prospection affects decision-making. The model has two characteristics. First, it uses model-based Bayesian inference to describe anticipation of cognitive and motivational processes. Second, the utility-maximization process considers these anticipations in two ways: to evaluate outcomes (e.g., the pleasure of eating a pie is evaluated differently at the beginning of a dinner, when one is hungry, and at the end of the dinner, when one is satiated), and as outcomes having a value themselves (e.g., the case of dread as a cost of waiting for punishment). By explicitly accounting for the relationship between prospection and value, our model provides a framework to reconcile the utility-maximization approach with psychological phenomena such as planning for the future and dread. PMID:21747755

An analysis of competitive bidding by providers for indigent medical care contracts.

PubMed Central

Kirkman-Liff, B L; Christianson, J B; Hillman, D G

1985-01-01

This article develops a model of behavior in bidding for indigent medical care contracts in which bidders set bid prices to maximize their expected utility, conditional on estimates of variables which affect the payoff associated with winning or losing a contract. The hypotheses generated by this model are tested empirically using data from the first round of bidding in the Arizona indigent health care experiment. The behavior of bidding organizations in Arizona is found to be consistent in most respects with the predictions of the model. Bid prices appear to have been influenced by estimated costs and by expectations concerning the potential loss from not securing a contract, the initial wealth of the bidding organization, and the expected number of competitors in the bidding process. PMID:4086301

Assessment of Optimal Flexibility in Ensemble of Frequency Responsive Loads

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kundu, Soumya; Hansen, Jacob; Lian, Jianming

2018-04-19

Potential of electrical loads in providing grid ancillary services is often limited due to the uncertainties associated with the load behavior. A knowledge of the expected uncertainties with a load control program would invariably yield to better informed control policies, opening up the possibility of extracting the maximal load control potential without affecting grid operations. In the context of frequency responsive load control, a probabilistic uncertainty analysis framework is presented to quantify the expected error between the target and actual load response, under uncertainties in the load dynamics. A closed-form expression of an optimal demand flexibility, minimizing the expected errormore » in actual and committed flexibility, is provided. Analytical results are validated through Monte Carlo simulations of ensembles of electric water heaters.« less

Evaluation of two methods for using MR information in PET reconstruction

NASA Astrophysics Data System (ADS)

Caldeira, L.; Scheins, J.; Almeida, P.; Herzog, H.

2013-02-01

Using magnetic resonance (MR) information in maximum a posteriori (MAP) algorithms for positron emission tomography (PET) image reconstruction has been investigated in the last years. Recently, three methods to introduce this information have been evaluated and the Bowsher prior was considered the best. Its main advantage is that it does not require image segmentation. Another method that has been widely used for incorporating MR information is using boundaries obtained by segmentation. This method has also shown improvements in image quality. In this paper, two methods for incorporating MR information in PET reconstruction are compared. After a Bayes parameter optimization, the reconstructed images were compared using the mean squared error (MSE) and the coefficient of variation (CV). MSE values are 3% lower in Bowsher than using boundaries. CV values are 10% lower in Bowsher than using boundaries. Both methods performed better than using no prior, that is, maximum likelihood expectation maximization (MLEM) or MAP without anatomic information in terms of MSE and CV. Concluding, incorporating MR information using the Bowsher prior gives better results in terms of MSE and CV than boundaries. MAP algorithms showed again to be effective in noise reduction and convergence, specially when MR information is incorporated. The robustness of the priors in respect to noise and inhomogeneities in the MR image has however still to be performed.

Impact of hydrogen SAE J2601 fueling methods on fueling time of light-duty fuel cell electric vehicles

DOE PAGES

Reddi, Krishna; Elgowainy, Amgad; Rustagi, Neha; ...

2017-05-16

Hydrogen fuel cell electric vehicles (HFCEVs) are zero-emission vehicles (ZEVs) that can provide drivers a similar experience to conventional internal combustion engine vehicles (ICEVs), in terms of fueling time and performance (i.e. power and driving range). The Society of Automotive Engineers (SAE) developed fueling protocol J2601 for light-duty HFCEVs to ensure safe vehicle fills while maximizing fueling performance. This study employs a physical model that simulates and compares the fueling performance of two fueling methods, known as the “lookup table” method and the “MC formula” method, within the SAE J2601 protocol. Both the fueling methods provide fast fueling of HFCEVsmore » within minutes, but the MC formula method takes advantage of active measurement of precooling temperature to dynamically control the fueling process, and thereby provides faster vehicle fills. Here, the MC formula method greatly reduces fueling time compared to the lookup table method at higher ambient temperatures, as well as when the precooling temperature falls on the colder side of the expected temperature window for all station types. Although the SAE J2601 lookup table method is the currently implemented standard for refueling hydrogen fuel cell vehicles, the MC formula method provides significant fueling time advantages in certain conditions; these warrant its implementation in future hydrogen refueling stations for better customer satisfaction with fueling experience of HFCEVs.« less

Impact of hydrogen SAE J2601 fueling methods on fueling time of light-duty fuel cell electric vehicles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reddi, Krishna; Elgowainy, Amgad; Rustagi, Neha

Hydrogen fuel cell electric vehicles (HFCEVs) are zero-emission vehicles (ZEVs) that can provide drivers a similar experience to conventional internal combustion engine vehicles (ICEVs), in terms of fueling time and performance (i.e. power and driving range). The Society of Automotive Engineers (SAE) developed fueling protocol J2601 for light-duty HFCEVs to ensure safe vehicle fills while maximizing fueling performance. This study employs a physical model that simulates and compares the fueling performance of two fueling methods, known as the “lookup table” method and the “MC formula” method, within the SAE J2601 protocol. Both the fueling methods provide fast fueling of HFCEVsmore » within minutes, but the MC formula method takes advantage of active measurement of precooling temperature to dynamically control the fueling process, and thereby provides faster vehicle fills. Here, the MC formula method greatly reduces fueling time compared to the lookup table method at higher ambient temperatures, as well as when the precooling temperature falls on the colder side of the expected temperature window for all station types. Although the SAE J2601 lookup table method is the currently implemented standard for refueling hydrogen fuel cell vehicles, the MC formula method provides significant fueling time advantages in certain conditions; these warrant its implementation in future hydrogen refueling stations for better customer satisfaction with fueling experience of HFCEVs.« less

Agent-Based Model Approach to Complex Phenomena in Real Economy

NASA Astrophysics Data System (ADS)

Iyetomi, H.; Aoyama, H.; Fujiwara, Y.; Ikeda, Y.; Souma, W.

An agent-based model for firms' dynamics is developed. The model consists of firm agents with identical characteristic parameters and a bank agent. Dynamics of those agents are described by their balance sheets. Each firm tries to maximize its expected profit with possible risks in market. Infinite growth of a firm directed by the ``profit maximization" principle is suppressed by a concept of ``going concern". Possibility of bankruptcy of firms is also introduced by incorporating a retardation effect of information on firms' decision. The firms, mutually interacting through the monopolistic bank, become heterogeneous in the course of temporal evolution. Statistical properties of firms' dynamics obtained by simulations based on the model are discussed in light of observations in the real economy.

Strong correlation of maximal squat strength with sprint performance and vertical jump height in elite soccer players

PubMed Central

Wisloff, U; Castagna, C; Helgerud, J; Jones, R; Hoff, J

2004-01-01

Background: A high level of strength is inherent in elite soccer play, but the relation between maximal strength and sprint and jumping performance has not been studied thoroughly. Objective: To determine whether maximal strength correlates with sprint and vertical jump height in elite male soccer players. Methods: Seventeen international male soccer players (mean (SD) age 25.8 (2.9) years, height 177.3 (4.1) cm, weight 76.5 (7.6) kg, and maximal oxygen uptake 65.7 (4.3) ml/kg/min) were tested for maximal strength in half squats and sprinting ability (0–30 m and 10 m shuttle run sprint) and vertical jumping height. Result: There was a strong correlation between maximal strength in half squats and sprint performance and jumping height. Conclusions: Maximal strength in half squats determines sprint performance and jumping height in high level soccer players. High squat strength did not imply reduced maximal oxygen consumption. Elite soccer players should focus on maximal strength training, with emphasis on maximal mobilisation of concentric movements, which may improve their sprinting and jumping performance. PMID:15155427

Topology optimization of thermal fluid flows with an adjoint Lattice Boltzmann Method

NASA Astrophysics Data System (ADS)

Dugast, Florian; Favennec, Yann; Josset, Christophe; Fan, Yilin; Luo, Lingai

2018-07-01

This paper presents an adjoint Lattice Boltzmann Method (LBM) coupled with the Level-Set Method (LSM) for topology optimization of thermal fluid flows. The adjoint-state formulation implies discrete velocity directions in order to take into account the LBM boundary conditions. These boundary conditions are introduced at the beginning of the adjoint-state method as the LBM residuals, so that the adjoint-state boundary conditions can appear directly during the adjoint-state equation formulation. The proposed method is tested with 3 numerical examples concerning thermal fluid flows, but with different objectives: minimization of the mean temperature in the domain, maximization of the heat evacuated by the fluid, and maximization of the heat exchange with heated solid parts. This latter example, treated in several articles, is used to validate our method. In these optimization problems, a limitation of the maximal pressure drop and of the porosity (number of fluid elements) is also applied. The obtained results demonstrate that the method is robust and effective for solving topology optimization of thermal fluid flows.

EM Adaptive LASSO—A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes

PubMed Central

Mallick, Himel; Tiwari, Hemant K.

2016-01-01

Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice. PMID:27066062

EM Adaptive LASSO-A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes.

PubMed

Mallick, Himel; Tiwari, Hemant K

2016-01-01

Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice.

Gradient Dynamics and Entropy Production Maximization

NASA Astrophysics Data System (ADS)

Janečka, Adam; Pavelka, Michal

2018-01-01

We compare two methods for modeling dissipative processes, namely gradient dynamics and entropy production maximization. Both methods require similar physical inputs-how energy (or entropy) is stored and how it is dissipated. Gradient dynamics describes irreversible evolution by means of dissipation potential and entropy, it automatically satisfies Onsager reciprocal relations as well as their nonlinear generalization (Maxwell-Onsager relations), and it has statistical interpretation. Entropy production maximization is based on knowledge of free energy (or another thermodynamic potential) and entropy production. It also leads to the linear Onsager reciprocal relations and it has proven successful in thermodynamics of complex materials. Both methods are thermodynamically sound as they ensure approach to equilibrium, and we compare them and discuss their advantages and shortcomings. In particular, conditions under which the two approaches coincide and are capable of providing the same constitutive relations are identified. Besides, a commonly used but not often mentioned step in the entropy production maximization is pinpointed and the condition of incompressibility is incorporated into gradient dynamics.

A Method for Evaluating Tuning Functions of Single Neurons based on Mutual Information Maximization

NASA Astrophysics Data System (ADS)

Brostek, Lukas; Eggert, Thomas; Ono, Seiji; Mustari, Michael J.; Büttner, Ulrich; Glasauer, Stefan

2011-03-01

We introduce a novel approach for evaluation of neuronal tuning functions, which can be expressed by the conditional probability of observing a spike given any combination of independent variables. This probability can be estimated out of experimentally available data. By maximizing the mutual information between the probability distribution of the spike occurrence and that of the variables, the dependence of the spike on the input variables is maximized as well. We used this method to analyze the dependence of neuronal activity in cortical area MSTd on signals related to movement of the eye and retinal image movement.

Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise

PubMed Central

Burge, Johannes

2017-01-01

Accuracy Maximization Analysis (AMA) is a recently developed Bayesian ideal observer method for task-specific dimensionality reduction. Given a training set of proximal stimuli (e.g. retinal images), a response noise model, and a cost function, AMA returns the filters (i.e. receptive fields) that extract the most useful stimulus features for estimating a user-specified latent variable from those stimuli. Here, we first contribute two technical advances that significantly reduce AMA’s compute time: we derive gradients of cost functions for which two popular estimators are appropriate, and we implement a stochastic gradient descent (AMA-SGD) routine for filter learning. Next, we show how the method can be used to simultaneously probe the impact on neural encoding of natural stimulus variability, the prior over the latent variable, noise power, and the choice of cost function. Then, we examine the geometry of AMA’s unique combination of properties that distinguish it from better-known statistical methods. Using binocular disparity estimation as a concrete test case, we develop insights that have general implications for understanding neural encoding and decoding in a broad class of fundamental sensory-perceptual tasks connected to the energy model. Specifically, we find that non-orthogonal (partially redundant) filters with scaled additive noise tend to outperform orthogonal filters with constant additive noise; non-orthogonal filters and scaled additive noise can interact to sculpt noise-induced stimulus encoding uncertainty to match task-irrelevant stimulus variability. Thus, we show that some properties of neural response thought to be biophysical nuisances can confer coding advantages to neural systems. Finally, we speculate that, if repurposed for the problem of neural systems identification, AMA may be able to overcome a fundamental limitation of standard subunit model estimation. As natural stimuli become more widely used in the study of psychophysical and neurophysiological performance, we expect that task-specific methods for feature learning like AMA will become increasingly important. PMID:28178266

Time to clinical response and remission for therapeutics in inflammatory bowel diseases: What should the clinician expect, what should patients be told?

PubMed Central

Vasudevan, Abhinav; Gibson, Peter R; van Langenberg, Daniel R

2017-01-01

An awareness of the expected time for therapies to induce symptomatic improvement and remission is necessary for determining the timing of follow-up, disease (re)assessment, and the duration to persist with therapies, yet this is seldom reported as an outcome in clinical trials. In this review, we explore the time to clinical response and remission of current therapies for inflammatory bowel disease (IBD) as well as medication, patient and disease related factors that may influence the time to clinical response. It appears that the time to therapeutic response varies depending on the indication for therapy (Crohn’s disease or ulcerative colitis). Agents with the most rapid time to clinical response included corticosteroids, calcineurin inhibitors, exclusive enteral nutrition, aminosalicylates and anti-tumor necrosis factor therapy which will work in most patients within the first 2 mo. Vedolizumab, methotrexate and thiopurines had a longer time to clinical response and can take several months to achieve maximal efficacy. Factors affecting the time to clinical response of therapies included use of concomitant therapy, disease duration, smoking status, disease phenotype and advanced age. There appears to be marked variation in time to clinical response for therapies used in IBD which is further influenced by disease and patient related factors. Understanding the expected time to therapeutic response is integral to inform further decision making, maintain a patient-centered approach and ensure treatment is given an appropriate timeframe to achieve maximal benefit prior to cessation. PMID:29085188

Optimal location selection for the installation of urban green roofs considering honeybee habitats along with socio-economic and environmental effects.

PubMed

Gwak, Jae Ha; Lee, Bo Kyeong; Lee, Won Kyung; Sohn, So Young

2017-03-15

This study proposes a new framework for the selection of optimal locations for green roofs to achieve a sustainable urban ecosystem. The proposed framework selects building sites that can maximize the benefits of green roofs, based not only on the socio-economic and environmental benefits to urban residents, but also on the provision of urban foraging sites for honeybees. The framework comprises three steps. First, building candidates for green roofs are selected considering the building type. Second, the selected building candidates are ranked in terms of their expected socio-economic and environmental effects. The benefits of green roofs are improved energy efficiency and air quality, reduction of urban flood risk and infrastructure improvement costs, reuse of storm water, and creation of space for education and leisure. Furthermore, the estimated cost of installing green roofs is also considered. We employ spatial data to determine the expected effects of green roofs on each building unit, because the benefits and costs may vary depending on the location of the building. This is due to the heterogeneous spatial conditions. In the third step, the final building sites are proposed by solving the maximal covering location problem (MCLP) to determine the optimal locations for green roofs as urban honeybee foraging sites. As an illustrative example, we apply the proposed framework in Seoul, Korea. This new framework is expected to contribute to sustainable urban ecosystems. Copyright © 2016 Elsevier Ltd. All rights reserved.

Time to clinical response and remission for therapeutics in inflammatory bowel diseases: What should the clinician expect, what should patients be told?

PubMed

Vasudevan, Abhinav; Gibson, Peter R; van Langenberg, Daniel R

2017-09-21

An awareness of the expected time for therapies to induce symptomatic improvement and remission is necessary for determining the timing of follow-up, disease (re)assessment, and the duration to persist with therapies, yet this is seldom reported as an outcome in clinical trials. In this review, we explore the time to clinical response and remission of current therapies for inflammatory bowel disease (IBD) as well as medication, patient and disease related factors that may influence the time to clinical response. It appears that the time to therapeutic response varies depending on the indication for therapy (Crohn's disease or ulcerative colitis). Agents with the most rapid time to clinical response included corticosteroids, calcineurin inhibitors, exclusive enteral nutrition, aminosalicylates and anti-tumor necrosis factor therapy which will work in most patients within the first 2 mo. Vedolizumab, methotrexate and thiopurines had a longer time to clinical response and can take several months to achieve maximal efficacy. Factors affecting the time to clinical response of therapies included use of concomitant therapy, disease duration, smoking status, disease phenotype and advanced age. There appears to be marked variation in time to clinical response for therapies used in IBD which is further influenced by disease and patient related factors. Understanding the expected time to therapeutic response is integral to inform further decision making, maintain a patient-centered approach and ensure treatment is given an appropriate timeframe to achieve maximal benefit prior to cessation.

Maximizing Federal IT Dollars: A Connection Between IT Investments and Organizational Performance

DTIC Science & Technology

2011-04-01

Theory for investments, where diversification of financial assets (stocks, bonds, and cash) is balanced by expected returns and risk (Markowitz, 1952...Stakeholder satisfaction (stakeholder may not pay proportionally for service) Stakeholders Stockholders , owners, market Taxpayers; legislative...Adviser for Off-Campus Programs in the Department of Engineering Manage- ment and Systems Engineering. His current research interests include stochastic

Expectation Maximization and its Application in Modeling, Segmentation and Anomaly Detection

DTIC Science & Technology

2008-05-01

ocomplNc <la!a rrot>lcm,. ",., i’lCOll\\l>lc,c,ICSS of Ihc dala mayan "" IIuc lu missing dala. (J,,,,,,.,ed di,nibu!ions . elc . 0"" such c • ..- is a...Estimation Techniques in Computer Huiyan, Z., Yongfeng, C., Wen, Y. SAR Image Segmentation Using MPM Constrained Stochastic Relaxation. Civil Engineering

State-Space Modeling of Dynamic Psychological Processes via the Kalman Smoother Algorithm: Rationale, Finite Sample Properties, and Applications

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2009-01-01

This article presents a state-space modeling (SSM) technique for fitting process factor analysis models directly to raw data. The Kalman smoother via the expectation-maximization algorithm to obtain maximum likelihood parameter estimates is used. To examine the finite sample properties of the estimates in SSM when common factors are involved, a…

Optimal control of orientation and entanglement for two dipole-dipole coupled quantum planar rotors.

PubMed

Yu, Hongling; Ho, Tak-San; Rabitz, Herschel

2018-05-09

Optimal control simulations are performed for orientation and entanglement of two dipole-dipole coupled identical quantum rotors. The rotors at various fixed separations lie on a model non-interacting plane with an applied control field. It is shown that optimal control of orientation or entanglement represents two contrasting control scenarios. In particular, the maximally oriented state (MOS) of the two rotors has a zero entanglement entropy and is readily attainable at all rotor separations. Whereas, the contrasting maximally entangled state (MES) has a zero orientation expectation value and is most conveniently attainable at small separations where the dipole-dipole coupling is strong. It is demonstrated that the peak orientation expectation value attained by the MOS at large separations exhibits a long time revival pattern due to the small energy splittings arising form the extremely weak dipole-dipole coupling between the degenerate product states of the two free rotors. Moreover, it is found that the peak entanglement entropy value attained by the MES remains largely unchanged as the two rotors are transported to large separations after turning off the control field. Finally, optimal control simulations of transition dynamics between the MOS and the MES reveal the intricate interplay between orientation and entanglement.

Time perspective and well-being: Swedish survey questionnaires and data.

PubMed

Garcia, Danilo; Nima, Ali Al; Lindskär, Erik

2016-12-01

The data pertains 448 Swedes' responses to questionnaires on time perspective (Zimbardo Time Perspective Inventory), temporal life satisfaction (Temporal Satisfaction with Life Scale), affect (Positive Affect and Negative Affect Schedule), and psychological well-being (Ryff׳s Scales of Psychological Well-Being-short version). The data was collected among university students and individuals at a training facility (see U. Sailer, P. Rosenberg, A.A. Nima, A. Gamble, T. Gärling, T. Archer, D. Garcia, 2014; [1]). Since there were no differences in any of the other background variables, but exercise frequency, all subsequent analyses were conducted on the 448 participants as one single sample. In this article we include the Swedish versions of the questionnaires used to operationalize the time perspective and well-being variables. The data is available, SPSS file, as Supplementary material in this article. We used the Expectation-Maximization Algorithm to input missing values. Little׳s Chi-Square test for Missing Completely at Random showed a χ (2)=67.25 (df=53, p=.09) for men and χ (2)=77.65 (df=72, p=.31) for women. These values suggested that the Expectation-Maximization Algorithm was suitable to use on this data for missing data imputation.

A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Srivastava, Ashok N.

2009-01-01

This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining tasks in a distributed environment such as clustering, anomaly detection, target tracking to name a few. This technology is crucial for many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Centralizing all or some of the data for building global models is impractical in such peer-to-peer environments because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. The distributed algorithm we have developed in this paper is provably-correct i.e. it converges to the same result compared to a similar centralized algorithm and can automatically adapt to changes to the data and the network. We show that the communication overhead of the algorithm is very low due to its local nature. This monitoring algorithm is then used as a feedback loop to sample data from the network and rebuild the model when it is outdated. We present thorough experimental results to verify our theoretical claims.

Latent variable method for automatic adaptation to background states in motor imagery BCI

NASA Astrophysics Data System (ADS)

Dagaev, Nikolay; Volkova, Ksenia; Ossadtchi, Alexei

2018-02-01

Objective. Brain-computer interface (BCI) systems are known to be vulnerable to variabilities in background states of a user. Usually, no detailed information on these states is available even during the training stage. Thus there is a need in a method which is capable of taking background states into account in an unsupervised way. Approach. We propose a latent variable method that is based on a probabilistic model with a discrete latent variable. In order to estimate the model’s parameters, we suggest to use the expectation maximization algorithm. The proposed method is aimed at assessing characteristics of background states without any corresponding data labeling. In the context of asynchronous motor imagery paradigm, we applied this method to the real data from twelve able-bodied subjects with open/closed eyes serving as background states. Main results. We found that the latent variable method improved classification of target states compared to the baseline method (in seven of twelve subjects). In addition, we found that our method was also capable of background states recognition (in six of twelve subjects). Significance. Without any supervised information on background states, the latent variable method provides a way to improve classification in BCI by taking background states into account at the training stage and then by making decisions on target states weighted by posterior probabilities of background states at the prediction stage.

Multi-Objective Bidding Strategy for Genco Using Non-Dominated Sorting Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Saksinchai, Apinat; Boonchuay, Chanwit; Ongsakul, Weerakorn

2010-06-01

This paper proposes a multi-objective bidding strategy for a generation company (GenCo) in uniform price spot market using non-dominated sorting particle swarm optimization (NSPSO). Instead of using a tradeoff technique, NSPSO is introduced to solve the multi-objective strategic bidding problem considering expected profit maximization and risk (profit variation) minimization. Monte Carlo simulation is employed to simulate rivals' bidding behavior. Test results indicate that the proposed approach can provide the efficient non-dominated solution front effectively. In addition, it can be used as a decision making tool for a GenCo compromising between expected profit and price risk in spot market.

How much segmentation is needed in the health care marketplace? An exploratory study of HMO and non-HMO customers.

PubMed

Tootelian, Dennis H; Mikhailitchenko, Andrey; Holst, Cindy; Gaedeke, Ralph M

2016-01-01

The health care landscape has changed dramatically. Consumers now seek plans whose benefits better fit their health care needs and desires for access to providers. This exploratory survey of more than 1,000 HMO and non-HMO customers found significant differences with respect to their selection processes for health plans and providers, and their expectations regarding access to and communication with health care providers. While there are some similarities in factors affecting choice, segmentation strategies are necessary to maximize the appeal of a plan, satisfy customers in the selection of physicians, and meet their expectations regarding access to those physicians.

Noisy Preferences in Risky Choice: A Cautionary Note

PubMed Central

2017-01-01

We examine the effects of multiple sources of noise in risky decision making. Noise in the parameters that characterize an individual’s preferences can combine with noise in the response process to distort observed choice proportions. Thus, underlying preferences that conform to expected value maximization can appear to show systematic risk aversion or risk seeking. Similarly, core preferences that are consistent with expected utility theory, when perturbed by such noise, can appear to display nonlinear probability weighting. For this reason, modal choices cannot be used simplistically to infer underlying preferences. Quantitative model fits that do not allow for both sorts of noise can lead to wrong conclusions. PMID:28569526

Collective states in social systems with interacting learning agents

NASA Astrophysics Data System (ADS)

Semeshenko, Viktoriya; Gordon, Mirta B.; Nadal, Jean-Pierre

2008-08-01

We study the implications of social interactions and individual learning features on consumer demand in a simple market model. We consider a social system of interacting heterogeneous agents with learning abilities. Given a fixed price, agents repeatedly decide whether or not to buy a unit of a good, so as to maximize their expected utilities. This model is close to Random Field Ising Models, where the random field corresponds to the idiosyncratic willingness to pay. We show that the equilibrium reached depends on the nature of the information agents use to estimate their expected utilities. It may be different from the systems’ Nash equilibria.

Multi-criteria decision making development of ion chromatographic method for determination of inorganic anions in oilfield waters based on artificial neural networks retention model.

PubMed

Stefanović, Stefica Cerjan; Bolanča, Tomislav; Luša, Melita; Ukić, Sime; Rogošić, Marko

2012-02-24

This paper describes the development of ad hoc methodology for determination of inorganic anions in oilfield water, since their composition often significantly differs from the average (concentration of components and/or matrix). Therefore, fast and reliable method development has to be performed in order to ensure the monitoring of desired properties under new conditions. The method development was based on computer assisted multi-criteria decision making strategy. The used criteria were: maximal value of objective functions used, maximal robustness of the separation method, minimal analysis time, and maximal retention distance between two nearest components. Artificial neural networks were used for modeling of anion retention. The reliability of developed method was extensively tested by the validation of performance characteristics. Based on validation results, the developed method shows satisfactory performance characteristics, proving the successful application of computer assisted methodology in the described case study. Copyright © 2011 Elsevier B.V. All rights reserved.

AUC-Maximizing Ensembles through Metalearning.

PubMed

LeDell, Erin; van der Laan, Mark J; Petersen, Maya

2016-05-01

Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree.

AUC-Maximizing Ensembles through Metalearning

PubMed Central

LeDell, Erin; van der Laan, Mark J.; Peterson, Maya

2016-01-01

Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree. PMID:27227721

Fast GPU-based computation of spatial multigrid multiframe LMEM for PET.

PubMed

Nassiri, Moulay Ali; Carrier, Jean-François; Després, Philippe

2015-09-01

Significant efforts were invested during the last decade to accelerate PET list-mode reconstructions, notably with GPU devices. However, the computation time per event is still relatively long, and the list-mode efficiency on the GPU is well below the histogram-mode efficiency. Since list-mode data are not arranged in any regular pattern, costly accesses to the GPU global memory can hardly be optimized and geometrical symmetries cannot be used. To overcome obstacles that limit the acceleration of reconstruction from list-mode on the GPU, a multigrid and multiframe approach of an expectation-maximization algorithm was developed. The reconstruction process is started during data acquisition, and calculations are executed concurrently on the GPU and the CPU, while the system matrix is computed on-the-fly. A new convergence criterion also was introduced, which is computationally more efficient on the GPU. The implementation was tested on a Tesla C2050 GPU device for a Gemini GXL PET system geometry. The results show that the proposed algorithm (multigrid and multiframe list-mode expectation-maximization, MGMF-LMEM) converges to the same solution as the LMEM algorithm more than three times faster. The execution time of the MGMF-LMEM algorithm was 1.1 s per million of events on the Tesla C2050 hardware used, for a reconstructed space of 188 x 188 x 57 voxels of 2 x 2 x 3.15 mm3. For 17- and 22-mm simulated hot lesions, the MGMF-LMEM algorithm led on the first iteration to contrast recovery coefficients (CRC) of more than 75 % of the maximum CRC while achieving a minimum in the relative mean square error. Therefore, the MGMF-LMEM algorithm can be used as a one-pass method to perform real-time reconstructions for low-count acquisitions, as in list-mode gated studies. The computation time for one iteration and 60 millions of events was approximately 66 s.

Statistical inference approach to structural reconstruction of complex networks from binary time series

NASA Astrophysics Data System (ADS)

Ma, Chuang; Chen, Han-Shuang; Lai, Ying-Cheng; Zhang, Hai-Feng

2018-02-01

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum-likelihood estimation of the probabilities associated with actual or nonexistent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any a priori knowledge of the detailed dynamical processes, is parameter-free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding "toolbox" of data based reverse engineering of complex networked systems.

Statistical inference approach to structural reconstruction of complex networks from binary time series.

PubMed

Ma, Chuang; Chen, Han-Shuang; Lai, Ying-Cheng; Zhang, Hai-Feng

2018-02-01

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum-likelihood estimation of the probabilities associated with actual or nonexistent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any a priori knowledge of the detailed dynamical processes, is parameter-free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding "toolbox" of data based reverse engineering of complex networked systems.

Enumerating all maximal frequent subtrees in collections of phylogenetic trees

PubMed Central

2014-01-01

Background A common problem in phylogenetic analysis is to identify frequent patterns in a collection of phylogenetic trees. The goal is, roughly, to find a subset of the species (taxa) on which all or some significant subset of the trees agree. One popular method to do so is through maximum agreement subtrees (MASTs). MASTs are also used, among other things, as a metric for comparing phylogenetic trees, computing congruence indices and to identify horizontal gene transfer events. Results We give algorithms and experimental results for two approaches to identify common patterns in a collection of phylogenetic trees, one based on agreement subtrees, called maximal agreement subtrees, the other on frequent subtrees, called maximal frequent subtrees. These approaches can return subtrees on larger sets of taxa than MASTs, and can reveal new common phylogenetic relationships not present in either MASTs or the majority rule tree (a popular consensus method). Our current implementation is available on the web at https://code.google.com/p/mfst-miner/. Conclusions Our computational results confirm that maximal agreement subtrees and all maximal frequent subtrees can reveal a more complete phylogenetic picture of the common patterns in collections of phylogenetic trees than maximum agreement subtrees; they are also often more resolved than the majority rule tree. Further, our experiments show that enumerating maximal frequent subtrees is considerably more practical than enumerating ordinary (not necessarily maximal) frequent subtrees. PMID:25061474

Enumerating all maximal frequent subtrees in collections of phylogenetic trees.

PubMed

Deepak, Akshay; Fernández-Baca, David

2014-01-01

A common problem in phylogenetic analysis is to identify frequent patterns in a collection of phylogenetic trees. The goal is, roughly, to find a subset of the species (taxa) on which all or some significant subset of the trees agree. One popular method to do so is through maximum agreement subtrees (MASTs). MASTs are also used, among other things, as a metric for comparing phylogenetic trees, computing congruence indices and to identify horizontal gene transfer events. We give algorithms and experimental results for two approaches to identify common patterns in a collection of phylogenetic trees, one based on agreement subtrees, called maximal agreement subtrees, the other on frequent subtrees, called maximal frequent subtrees. These approaches can return subtrees on larger sets of taxa than MASTs, and can reveal new common phylogenetic relationships not present in either MASTs or the majority rule tree (a popular consensus method). Our current implementation is available on the web at https://code.google.com/p/mfst-miner/. Our computational results confirm that maximal agreement subtrees and all maximal frequent subtrees can reveal a more complete phylogenetic picture of the common patterns in collections of phylogenetic trees than maximum agreement subtrees; they are also often more resolved than the majority rule tree. Further, our experiments show that enumerating maximal frequent subtrees is considerably more practical than enumerating ordinary (not necessarily maximal) frequent subtrees.

A dimension-wise analysis method for the structural-acoustic system with interval parameters

NASA Astrophysics Data System (ADS)

Xu, Menghui; Du, Jianke; Wang, Chong; Li, Yunlong

2017-04-01

The interval structural-acoustic analysis is mainly accomplished by interval and subinterval perturbation methods. Potential limitations for these intrusive methods include overestimation or interval translation effect for the former and prohibitive computational cost for the latter. In this paper, a dimension-wise analysis method is thus proposed to overcome these potential limitations. In this method, a sectional curve of the system response surface along each input dimensionality is firstly extracted, the minimal and maximal points of which are identified based on its Legendre polynomial approximation. And two input vectors, i.e. the minimal and maximal input vectors, are dimension-wisely assembled by the minimal and maximal points of all sectional curves. Finally, the lower and upper bounds of system response are computed by deterministic finite element analysis at the two input vectors. Two numerical examples are studied to demonstrate the effectiveness of the proposed method and show that, compared to the interval and subinterval perturbation method, a better accuracy is achieved without much compromise on efficiency by the proposed method, especially for nonlinear problems with large interval parameters.

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks.

PubMed

Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X

2015-12-26

Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks

PubMed Central

Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X.

2015-01-01

Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols. PMID:26712764

An entropic framework for modeling economies

NASA Astrophysics Data System (ADS)

Caticha, Ariel; Golan, Amos

2014-08-01

We develop an information-theoretic framework for economic modeling. This framework is based on principles of entropic inference that are designed for reasoning on the basis of incomplete information. We take the point of view of an external observer who has access to limited information about broad macroscopic economic features. We view this framework as complementary to more traditional methods. The economy is modeled as a collection of agents about whom we make no assumptions of rationality (in the sense of maximizing utility or profit). States of statistical equilibrium are introduced as those macrostates that maximize entropy subject to the relevant information codified into constraints. The basic assumption is that this information refers to supply and demand and is expressed in the form of the expected values of certain quantities (such as inputs, resources, goods, production functions, utility functions and budgets). The notion of economic entropy is introduced. It provides a measure of the uniformity of the distribution of goods and resources. It captures both the welfare state of the economy as well as the characteristics of the market (say, monopolistic, concentrated or competitive). Prices, which turn out to be the Lagrange multipliers, are endogenously generated by the economy. Further studies include the equilibrium between two economies and the conditions for stability. As an example, the case of the nonlinear economy that arises from linear production and utility functions is treated in some detail.

A Direct Method for Obtaining Approximate Standard Error and Confidence Interval of Maximal Reliability for Composites with Congeneric Measures

ERIC Educational Resources Information Center

Raykov, Tenko; Penev, Spiridon

2006-01-01

Unlike a substantial part of reliability literature in the past, this article is concerned with weighted combinations of a given set of congeneric measures with uncorrelated errors. The relationship between maximal coefficient alpha and maximal reliability for such composites is initially dealt with, and it is shown that the former is a lower…

Maximal likelihood correspondence estimation for face recognition across pose.

PubMed

Li, Shaoxin; Liu, Xin; Chai, Xiujuan; Zhang, Haihong; Lao, Shihong; Shan, Shiguang

2014-10-01

Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.

Expected p-values in light of an ROC curve analysis applied to optimal multiple testing procedures.

PubMed

Vexler, Albert; Yu, Jihnhee; Zhao, Yang; Hutson, Alan D; Gurevich, Gregory

2017-01-01

Many statistical studies report p-values for inferential purposes. In several scenarios, the stochastic aspect of p-values is neglected, which may contribute to drawing wrong conclusions in real data experiments. The stochastic nature of p-values makes their use to examine the performance of given testing procedures or associations between investigated factors to be difficult. We turn our focus on the modern statistical literature to address the expected p-value (EPV) as a measure of the performance of decision-making rules. During the course of our study, we prove that the EPV can be considered in the context of receiver operating characteristic (ROC) curve analysis, a well-established biostatistical methodology. The ROC-based framework provides a new and efficient methodology for investigating and constructing statistical decision-making procedures, including: (1) evaluation and visualization of properties of the testing mechanisms, considering, e.g. partial EPVs; (2) developing optimal tests via the minimization of EPVs; (3) creation of novel methods for optimally combining multiple test statistics. We demonstrate that the proposed EPV-based approach allows us to maximize the integrated power of testing algorithms with respect to various significance levels. In an application, we use the proposed method to construct the optimal test and analyze a myocardial infarction disease dataset. We outline the usefulness of the "EPV/ROC" technique for evaluating different decision-making procedures, their constructions and properties with an eye towards practical applications.

Dynamics and control of Ebola virus transmission in Montserrado, Liberia: a mathematical modeling analysis

PubMed Central

Lewnard, Joseph A.; Ndeffo Mbah, Martial L.; Alfaro-Murillo, Jorge A.; Altice, Frederick L.; Bawo, Luke; Nyenswah, Tolbert G.; Galvani, Alison P.

2014-01-01

Background A substantial scale-up in public health response is needed to control the unprecedented Ebola virus disease (EVD) epidemic in West Africa. Current international commitments seek to expand intervention capacity in three areas: new EVD Treatment Centers (ETCs); case ascertainment through contact tracing; and household protective kit allocation. Methods We developed a transmission model of Ebola virus that we fitted to reported EVD cases and deaths in Montserrado County, Liberia. We used this model to evaluate effectiveness of expanding ETCs, improving case ascertainment, and allocating protective kits for controlling the outbreak in Montserrado. Findings We estimated the basic reproductive number for EVD in Montserrado to be 2·49 [2·38–2·60]. We expect that allocating 4,800 additional ETC beds and increasing case ascertainment fivefold in November can avert 77312 [68400–85870] cases relative to the status quo by 15 December. Complementing these measures with protective kit allocation increases the expectation as high as 97940 [90096–105606] cases. If deployed by 15 October, equivalent interventions would have been expected to avert 137432 [129736–145874] cases. If delayed to 15 November, we expect the interventions will at best avert 53957 [49963–60490] cases. Interpretation The number of ETC beds needed to effectively control EVD in Montserrado substantially exceeds the total pledged by the United States to West Africa. Accelerated case ascertainment is required to maximize effectiveness of expanding ETC capacity. Distributing protective kits can further augment EVD prevention. Our findings highlight the rapidly closing window of opportunity for controlling the outbreak and averting a catastrophic toll of EVD cases and deaths. Funding NIH: U01-GM087719, U01-GM105627, K24-DA017072 PMID:25455986

Unified sensor management in unknown dynamic clutter

NASA Astrophysics Data System (ADS)

Mahler, Ronald; El-Fallah, Adel

2010-04-01

In recent years the first author has developed a unified, computationally tractable approach to multisensor-multitarget sensor management. This approach consists of closed-loop recursion of a PHD or CPHD filter with maximization of a "natural" sensor management objective function called PENT (posterior expected number of targets). In this paper we extend this approach so that it can be used in unknown, dynamic clutter backgrounds.

High-Performance Psychometrics: The Parallel-E Parallel-M Algorithm for Generalized Latent Variable Models. Research Report. ETS RR-16-34

ERIC Educational Resources Information Center

von Davier, Matthias

2016-01-01

This report presents results on a parallel implementation of the expectation-maximization (EM) algorithm for multidimensional latent variable models. The developments presented here are based on code that parallelizes both the E step and the M step of the parallel-E parallel-M algorithm. Examples presented in this report include item response…

Effects of Requiring Students to Meet High Expectation Levels within an On-Line Homework Environment

ERIC Educational Resources Information Center

Weber, William J., Jr.

2010-01-01

On-line homework is becoming a larger part of mathematics classrooms each year. Thus, ways to maximize the effectiveness of on-line homework for both students and teachers must be investigated. This study sought to provide one possible answer to this aim, by requiring students to achieve at least 50% for any on-line homework assignment in order to…

State-Dependent Risk Preferences in Evolutionary Games

NASA Astrophysics Data System (ADS)

Roos, Patrick; Nau, Dana

There is much empirical evidence that human decision-making under risk does not correspond the decision-theoretic notion of "rational" decision making, namely to make choices that maximize the expected value. An open question is how such behavior could have arisen evolutionarily. We believe that the answer to this question lies, at least in part, in the interplay between risk-taking and sequentiality of choice in evolutionary environments.

EM algorithm applied for estimating non-stationary region boundaries using electrical impedance tomography

NASA Astrophysics Data System (ADS)

Khambampati, A. K.; Rashid, A.; Kim, B. S.; Liu, Dong; Kim, S.; Kim, K. Y.

2010-04-01

EIT has been used for the dynamic estimation of organ boundaries. One specific application in this context is the estimation of lung boundaries during pulmonary circulation. This would help track the size and shape of lungs of the patients suffering from diseases like pulmonary edema and acute respiratory failure (ARF). The dynamic boundary estimation of the lungs can also be utilized to set and control the air volume and pressure delivered to the patients during artificial ventilation. In this paper, the expectation-maximization (EM) algorithm is used as an inverse algorithm to estimate the non-stationary lung boundary. The uncertainties caused in Kalman-type filters due to inaccurate selection of model parameters are overcome using EM algorithm. Numerical experiments using chest shaped geometry are carried out with proposed method and the performance is compared with extended Kalman filter (EKF). Results show superior performance of EM in estimation of the lung boundary.

Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model

PubMed Central

Gooya, Ali; Pohl, Kilian M.; Bilello, Michel; Biros, George; Davatzikos, Christos

2011-01-01

This paper presents an approach for joint segmentation and deformable registration of brain scans of glioma patients to a normal atlas. The proposed method is based on the Expectation Maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the normal atlas into one with a tumor and edema. The modified atlas is registered into the patient space and utilized for the posterior probability estimation of various tissue labels. EM iteratively refines the estimates of the registration parameters, the posterior probabilities of tissue labels and the tumor growth model parameters. We have applied this approach to 10 glioma scans acquired with four Magnetic Resonance (MR) modalities (T1, T1-CE, T2 and FLAIR ) and validated the result by comparing them to manual segmentations by clinical experts. The resulting segmentations look promising and quantitatively match well with the expert provided ground truth. PMID:21995070

Joint segmentation and deformable registration of brain scans guided by a tumor growth model.

PubMed

Gooya, Ali; Pohl, Kilian M; Bilello, Michel; Biros, George; Davatzikos, Christos

2011-01-01

This paper presents an approach for joint segmentation and deformable registration of brain scans of glioma patients to a normal atlas. The proposed method is based on the Expectation Maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the normal atlas into one with a tumor and edema. The modified atlas is registered into the patient space and utilized for the posterior probability estimation of various tissue labels. EM iteratively refines the estimates of the registration parameters, the posterior probabilities of tissue labels and the tumor growth model parameters. We have applied this approach to 10 glioma scans acquired with four Magnetic Resonance (MR) modalities (T1, T1-CE, T2 and FLAIR) and validated the result by comparing them to manual segmentations by clinical experts. The resulting segmentations look promising and quantitatively match well with the expert provided ground truth.

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.

PubMed

Bakas, Spyridon; Zeng, Ke; Sotiras, Aristeidis; Rathore, Saima; Akbari, Hamed; Gaonkar, Bilwaj; Rozycki, Martin; Pati, Sarthak; Davatzikos, Christos

2016-01-01

We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.

Multiclass feature selection for improved pediatric brain tumor segmentation

NASA Astrophysics Data System (ADS)

Ahmed, Shaheen; Iftekharuddin, Khan M.

2012-03-01

In our previous work, we showed that fractal-based texture features are effective in detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and ranking different texture features. We further incorporated the feature selection technique with segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor (NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.

Aluminum Target Dissolution in Support of the Pu-238 Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

McFarlane, Joanna; Benker, Dennis; DePaoli, David W

2014-09-01

Selection of an aluminum alloy for target cladding affects post-irradiation target dissolution and separations. Recent tests with aluminum alloy 6061 yielded greater than expected precipitation in the caustic dissolution step, forming up to 10 wt.% solids of aluminum hydroxides and aluminosilicates. We present a study to maximize dissolution of aluminum metal alloy, along with silicon, magnesium, and copper impurities, through control of temperature, the rate of reagent addition, and incubation time. Aluminum phase transformations have been identified as a function of time and temperature, using X-ray diffraction. Solutions have been analyzed using wet chemical methods and X-ray fluorescence. These datamore » have been compared with published calculations of aluminum phase diagrams. Temperature logging during the transients has been investigated as a means to generate kinetic and mass transport data on the dissolution process. Approaches are given to enhance the dissolution of aluminum and aluminosilicate phases in caustic solution.« less

Dishonest Academic Conduct: From the Perspective of the Utility Function.

PubMed

Sun, Ying; Tian, Rui

Dishonest academic conduct has aroused extensive attention in academic circles. To explore how scholars make decisions according to the principle of maximal utility, the author has constructed the general utility function based on the expected utility theory. The concrete utility functions of different types of scholars were deduced. They are as follows: risk neutral, risk averse, and risk preference. Following this, the assignment method was adopted to analyze and compare the scholars' utilities of academic conduct. It was concluded that changing the values of risk costs, internal condemnation costs, academic benefits, and the subjective estimation of penalties following dishonest academic conduct can lead to changes in the utility of academic dishonesty. The results of the current study suggest that within scientific research, measures to prevent and govern dishonest academic conduct should be formulated according to the various effects of the above four variables.

DNAPrint Genomics, Inc.: better drugs for segmented markets.

PubMed

Frudakis, Tony

2008-02-01

The postgenome era promises more efficient drug-development cycles and medications targeted to compatible populations, resulting in improved outcomes, fewer drug-company failures, less litigation, fewer recalls and a refurbished image of 'pharma' in the mind of the customer. DNAPrint was founded to help precipitate these changes. Since 1999, we have developed and optimized novel methods for assessing patient response proclivities as individuals but also as constituents of populations, and we have introduced a computational platform for modeling drug biology. We expect these tools will allow us to maximize the efficiency of our clinical trials and, more importantly, ensure better postmarket performance parameters. With these tools, we are now carefully engineering select drug-development projects in an attempt to illustrate the viability of a novel drug-development model - one based on the application of intelligence and new technologies for superior drug performance in segmented markets.

Dissolution and Separation of Aluminum and Aluminosilicates

DOE PAGES

McFarlane, Joanna; Benker, Dennis; DePaoli, David W.; ...

2015-12-19

The selection of an aluminum alloy for target irradiation affects post-irradiation target dissolution and separations. Recent tests with aluminum alloy 6061 yielded greater than expected precipitation in the dissolver, forming up to 10 wt.% solids of aluminum hydroxides and aluminosilicates. Aluminosilicate dissolution presents challenges in a number of different areas, metals extraction from minerals, flyash treatment, and separations from aluminum alloys. We present experimental work that attempts to maximize dissolution of aluminum metal, along with silicon, magnesium, and copper impurities, through control of temperature, the rate of reagent addition, and incubation time. Aluminum phase transformations have been identified as amore » function of time and temperature, using X-ray diffraction. Solutions have been analyzed using wet chemical methods and X-ray fluorescence. Our data have been compared with published calculations of aluminum phase diagrams. Approaches are given to enhance the dissolution of aluminum and aluminosilicate phases in caustic solution.« less

[Poverty profile regarding households participating in a food assistance program].

PubMed

Álvarez-Uribe, Martha C; Aguirre-Acevedo, Daniel C

2012-06-01

This study was aimed at establishing subgroups having specific socioeconomic characteristics by using latent class analysis as a method for segmenting target population members of the MANA-ICBF supplementary food program in the Antioquia department of Colombia and determine their differences regarding poverty and health conditions in efficiently addressing pertinent resources, programs and policies. The target population consisted of 200,000 children and their households involved in the MANA food assistance program; a representative sample by region was used. Latent class analysis was used, as were the expectation-maximization and Newton Raphson algorithms for identifying the appropriate number of classes. The final model classified the households into four clusters or classes, differing according to well-defined socio-demographic conditions affecting children's health. Some homes had a greater depth of poverty, therefore lowering the families' quality of life and affecting the health of the children in this age group.

Probabilistic self-organizing maps for continuous data.

PubMed

Lopez-Rubio, Ezequiel

2010-10-01

The original self-organizing feature map did not define any probability distribution on the input space. However, the advantages of introducing probabilistic methodologies into self-organizing map models were soon evident. This has led to a wide range of proposals which reflect the current emergence of probabilistic approaches to computational intelligence. The underlying estimation theories behind them derive from two main lines of thought: the expectation maximization methodology and stochastic approximation methods. Here, we present a comprehensive view of the state of the art, with a unifying perspective of the involved theoretical frameworks. In particular, we examine the most commonly used continuous probability distributions, self-organization mechanisms, and learning schemes. Special emphasis is given to the connections among them and their relative advantages depending on the characteristics of the problem at hand. Furthermore, we evaluate their performance in two typical applications of self-organizing maps: classification and visualization.

DOE Office of Scientific and Technical Information (OSTI.GOV)

McMillan, Kyle; Marleau, Peter; Brubaker, Erik

In coded aperture imaging, one of the most important factors determining the quality of reconstructed images is the choice of mask/aperture pattern. In many applications, uniformly redundant arrays (URAs) are widely accepted as the optimal mask pattern. Under ideal conditions, thin and highly opaque masks, URA patterns are mathematically constructed to provide artifact-free reconstruction however, the number of URAs for a chosen number of mask elements is limited and when highly penetrating particles such as fast neutrons and high-energy gamma-rays are being imaged, the optimum is seldom achieved. In this case more robust mask patterns that provide better reconstructed imagemore » quality may exist. Through the use of heuristic optimization methods and maximum likelihood expectation maximization (MLEM) image reconstruction, we show that for both point and extended neutron sources a random mask pattern can be optimized to provide better image quality than that of a URA.« less

A Monte Carlo simulation study for the gamma-ray/neutron dual-particle imager using rotational modulation collimator (RMC).

PubMed

Kim, Hyun Suk; Choi, Hong Yeop; Lee, Gyemin; Ye, Sung-Joon; Smith, Martin B; Kim, Geehyun

2018-03-01

The aim of this work is to develop a gamma-ray/neutron dual-particle imager, based on rotational modulation collimators (RMCs) and pulse shape discrimination (PSD)-capable scintillators, for possible applications for radioactivity monitoring as well as nuclear security and safeguards. A Monte Carlo simulation study was performed to design an RMC system for the dual-particle imaging, and modulation patterns were obtained for gamma-ray and neutron sources in various configurations. We applied an image reconstruction algorithm utilizing the maximum-likelihood expectation-maximization method based on the analytical modeling of source-detector configurations, to the Monte Carlo simulation results. Both gamma-ray and neutron source distributions were reconstructed and evaluated in terms of signal-to-noise ratio, showing the viability of developing an RMC-based gamma-ray/neutron dual-particle imager using PSD-capable scintillators.

Public health care and private insurance demand: the waiting time as a link.

PubMed

Jofre-Bonet, M

2000-01-01

This paper analyzes the effect of waiting times in the Spanish public health system on the demand for private health insurance. Expected utility maximization determines whether or not individuals buy a private health insurance. The decision depends not only on consumer's covariates such as income, socio-demographic characteristics and health status, but also on the quality of the treatment by the public provider. We interpret waiting time as a qualitative attribute of the health care provision. The empirical analysis uses the Spanish Health Survey of 1993. We cope with the absence of income data by using the Spanish Family Budget Survey of 1990-91 as a complementary data set, following the Arellano-Meghir method [4]. Results indicate that a reduction in the waiting time lowers the probability of buying private health insurance. This suggests the existence of a crowd-out in the health care provision market.

Semi-supervised Learning for Phenotyping Tasks.

PubMed

Dligach, Dmitriy; Miller, Timothy; Savova, Guergana K

2015-01-01

Supervised learning is the dominant approach to automatic electronic health records-based phenotyping, but it is expensive due to the cost of manual chart review. Semi-supervised learning takes advantage of both scarce labeled and plentiful unlabeled data. In this work, we study a family of semi-supervised learning algorithms based on Expectation Maximization (EM) in the context of several phenotyping tasks. We first experiment with the basic EM algorithm. When the modeling assumptions are violated, basic EM leads to inaccurate parameter estimation. Augmented EM attenuates this shortcoming by introducing a weighting factor that downweights the unlabeled data. Cross-validation does not always lead to the best setting of the weighting factor and other heuristic methods may be preferred. We show that accurate phenotyping models can be trained with only a few hundred labeled (and a large number of unlabeled) examples, potentially providing substantial savings in the amount of the required manual chart review.

Mixture models with entropy regularization for community detection in networks

NASA Astrophysics Data System (ADS)

Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

2018-04-01

Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

Enhancing ultra-high CPV passive cooling using least-material finned heat sinks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Micheli, Leonardo, E-mail: lm409@exeter.ac.uk; Mallick, Tapas K., E-mail: T.K.Mallick@exeter.ac.uk; Fernandez, Eduardo F., E-mail: E.Fernandez-Fernandez2@exeter.ac.uk

2015-09-28

Ultra-high concentrating photovoltaic (CPV) systems aim to increase the cost-competiveness of CPV by increasing the concentrations over 2000 suns. In this work, the design of a heat sink for ultra-high concentrating photovoltaic (CPV) applications is presented. For the first time, the least-material approach, widely used in electronics to maximize the thermal dissipation while minimizing the weight of the heat sink, has been applied in CPV. This method has the potential to further decrease the cost of this technology and to keep the multijunction cell within the operative temperature range. The designing procedure is described in the paper and the resultsmore » of a thermal simulation are shown to prove the reliability of the solution. A prediction of the costs is also reported: a cost of 0.151$/W{sub p} is expected for a passive least-material heat sink developed for 4000x applications.« less

Effective return, risk aversion and drawdowns

NASA Astrophysics Data System (ADS)

Dacorogna, Michel M.; Gençay, Ramazan; Müller, Ulrich A.; Pictet, Olivier V.

2001-01-01

We derive two risk-adjusted performance measures for investors with risk averse preferences. Maximizing these measures is equivalent to maximizing the expected utility of an investor. The first measure, Xeff, is derived assuming a constant risk aversion while the second measure, Reff, is based on a stronger risk aversion to clustering of losses than of gains. The clustering of returns is captured through a multi-horizon framework. The empirical properties of Xeff, Reff are studied within the context of real-time trading models for foreign exchange rates and their properties are compared to those of more traditional measures like the annualized return, the Sharpe Ratio and the maximum drawdown. Our measures are shown to be more robust against clustering of losses and have the ability to fully characterize the dynamic behaviour of investment strategies.

Influencing Busy People in a Social Network

PubMed Central

Sarkar, Kaushik; Sundaram, Hari

2016-01-01

We identify influential early adopters in a social network, where individuals are resource constrained, to maximize the spread of multiple, costly behaviors. A solution to this problem is especially important for viral marketing. The problem of maximizing influence in a social network is challenging since it is computationally intractable. We make three contributions. First, we propose a new model of collective behavior that incorporates individual intent, knowledge of neighbors actions and resource constraints. Second, we show that the multiple behavior influence maximization is NP-hard. Furthermore, we show that the problem is submodular, implying the existence of a greedy solution that approximates the optimal solution to within a constant. However, since the greedy algorithm is expensive for large networks, we propose efficient heuristics to identify the influential individuals, including heuristics to assign behaviors to the different early adopters. We test our approach on synthetic and real-world topologies with excellent results. We evaluate the effectiveness under three metrics: unique number of participants, total number of active behaviors and network resource utilization. Our heuristics produce 15-51% increase in expected resource utilization over the naïve approach. PMID:27711127

Influencing Busy People in a Social Network.

PubMed

Sarkar, Kaushik; Sundaram, Hari

2016-01-01

We identify influential early adopters in a social network, where individuals are resource constrained, to maximize the spread of multiple, costly behaviors. A solution to this problem is especially important for viral marketing. The problem of maximizing influence in a social network is challenging since it is computationally intractable. We make three contributions. First, we propose a new model of collective behavior that incorporates individual intent, knowledge of neighbors actions and resource constraints. Second, we show that the multiple behavior influence maximization is NP-hard. Furthermore, we show that the problem is submodular, implying the existence of a greedy solution that approximates the optimal solution to within a constant. However, since the greedy algorithm is expensive for large networks, we propose efficient heuristics to identify the influential individuals, including heuristics to assign behaviors to the different early adopters. We test our approach on synthetic and real-world topologies with excellent results. We evaluate the effectiveness under three metrics: unique number of participants, total number of active behaviors and network resource utilization. Our heuristics produce 15-51% increase in expected resource utilization over the naïve approach.

Complementary contributions of basolateral amygdala and orbitofrontal cortex to value learning under uncertainty

PubMed Central

Stolyarova, Alexandra; Izquierdo, Alicia

2017-01-01

We make choices based on the values of expected outcomes, informed by previous experience in similar settings. When the outcomes of our decisions consistently violate expectations, new learning is needed to maximize rewards. Yet not every surprising event indicates a meaningful change in the environment. Even when conditions are stable overall, outcomes of a single experience can still be unpredictable due to small fluctuations (i.e., expected uncertainty) in reward or costs. In the present work, we investigate causal contributions of the basolateral amygdala (BLA) and orbitofrontal cortex (OFC) in rats to learning under expected outcome uncertainty in a novel delay-based task that incorporates both predictable fluctuations and directional shifts in outcome values. We demonstrate that OFC is required to accurately represent the distribution of wait times to stabilize choice preferences despite trial-by-trial fluctuations in outcomes, whereas BLA is necessary for the facilitation of learning in response to surprising events. DOI: http://dx.doi.org/10.7554/eLife.27483.001 PMID:28682238

Consumer Expectations of Online Services in the Insurance Industry: An Exploratory Study of Drivers and Outcomes.

PubMed

Méndez-Aparicio, M Dolores; Izquierdo-Yusta, Alicia; Jiménez-Zarco, Ana I

2017-01-01

Today, the customer-brand relationship is fundamental to a company's bottom line, especially in the service sector and with services offered via online channels. In order to maximize its effects, organizations need (1) to know which factors influence the formation of an individual's service expectations in an online environment; and (2) to establish the influence of these expectations on customers' likelihood of recommending a service before they have even used it. In accordance with the TAM model (Davis, 1989; Davis et al., 1992), the TRA model (Fishbein and Ajzen, 1975), the extended UTAUT model (Venkatesh et al., 2012), and the approach described by Alloza (2011), this work proposes a theoretical model of the antecedents and consequences of consumer expectations of online services. In order to validate the proposed theoretical model, a sample of individual insurance company customers was analyzed. The results showed, first, the importance of customers' expectations with regard to the intention to recommend the "private area" of the company's website to other customers prior to using it themselves. They also revealed the importance to expectations of the antecedents perceived usefulness, ease of use, frequency of use, reputation, and subjective norm.

Consumer Expectations of Online Services in the Insurance Industry: An Exploratory Study of Drivers and Outcomes

PubMed Central

Méndez-Aparicio, M. Dolores; Izquierdo-Yusta, Alicia; Jiménez-Zarco, Ana I.

2017-01-01

Today, the customer-brand relationship is fundamental to a company’s bottom line, especially in the service sector and with services offered via online channels. In order to maximize its effects, organizations need (1) to know which factors influence the formation of an individual’s service expectations in an online environment; and (2) to establish the influence of these expectations on customers’ likelihood of recommending a service before they have even used it. In accordance with the TAM model (Davis, 1989; Davis et al., 1992), the TRA model (Fishbein and Ajzen, 1975), the extended UTAUT model (Venkatesh et al., 2012), and the approach described by Alloza (2011), this work proposes a theoretical model of the antecedents and consequences of consumer expectations of online services. In order to validate the proposed theoretical model, a sample of individual insurance company customers was analyzed. The results showed, first, the importance of customers’ expectations with regard to the intention to recommend the “private area” of the company’s website to other customers prior to using it themselves. They also revealed the importance to expectations of the antecedents perceived usefulness, ease of use, frequency of use, reputation, and subjective norm. PMID:28798705

Insertion of lithium into electrochromic devices after completion

DOEpatents

Berland, Brian Spencer; Lanning, Bruce Roy; Frey, Jonathan Mack; Barrett, Kathryn Suzanne; DuPont, Paul Damon; Schaller, Ronald William

2015-12-22

The present disclosure describes methods of inserting lithium into an electrochromic device after completion. In the disclosed methods, an ideal amount of lithium can be added post-fabrication to maximize or tailor the free lithium ion density of a layer or the coloration range of a device. Embodiments are directed towards a method to insert lithium into the main device layers of an electrochromic device as a post-processing step after the device has been manufactured. In an embodiment, the methods described are designed to maximize the coloration range while compensating for blind charge loss.

Optimization of oncological {sup 18}F-FDG PET/CT imaging based on a multiparameter analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Menezes, Vinicius O., E-mail: vinicius@radtec.com.br; Machado, Marcos A. D.; Queiroz, Cleiton C.

2016-02-15

Purpose: This paper describes a method to achieve consistent clinical image quality in {sup 18}F-FDG scans accounting for patient habitus, dose regimen, image acquisition, and processing techniques. Methods: Oncological PET/CT scan data for 58 subjects were evaluated retrospectively to derive analytical curves that predict image quality. Patient noise equivalent count rate and coefficient of variation (CV) were used as metrics in their analysis. Optimized acquisition protocols were identified and prospectively applied to 179 subjects. Results: The adoption of different schemes for three body mass ranges (<60 kg, 60–90 kg, >90 kg) allows improved image quality with both point spread functionmore » and ordered-subsets expectation maximization-3D reconstruction methods. The application of this methodology showed that CV improved significantly (p < 0.0001) in clinical practice. Conclusions: Consistent oncological PET/CT image quality on a high-performance scanner was achieved from an analysis of the relations existing between dose regimen, patient habitus, acquisition, and processing techniques. The proposed methodology may be used by PET/CT centers to develop protocols to standardize PET/CT imaging procedures and achieve better patient management and cost-effective operations.« less

Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.

PubMed

Lee, David; Park, Sang-Hoon; Lee, Sang-Goog

2017-10-07

In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them. Subsequently, the GMM universal background model is trained by the expectation-maximization (EM) algorithm to purify the training data and reduce its size. Finally, a purified and reduced GMM-supervector is used to train the support vector machine classifier. The performance of the proposed method was evaluated for three different motor imagery datasets in terms of accuracy, kappa, mutual information, and computation time, and compared with the state-of-the-art algorithms. The results from the study indicate that the proposed method achieves high accuracy with a small amount of training data compared with the state-of-the-art algorithms in motor imagery EEG classification.

Research on the feature set construction method for spherical stereo vision

NASA Astrophysics Data System (ADS)

Zhu, Junchao; Wan, Li; Röning, Juha; Feng, Weijia

2015-01-01

Spherical stereo vision is a kind of stereo vision system built by fish-eye lenses, which discussing the stereo algorithms conform to the spherical model. Epipolar geometry is the theory which describes the relationship of the two imaging plane in cameras for the stereo vision system based on perspective projection model. However, the epipolar in uncorrected fish-eye image will not be a line but an arc which intersects at the poles. It is polar curve. In this paper, the theory of nonlinear epipolar geometry will be explored and the method of nonlinear epipolar rectification will be proposed to eliminate the vertical parallax between two fish-eye images. Maximally Stable Extremal Region (MSER) utilizes grayscale as independent variables, and uses the local extremum of the area variation as the testing results. It is demonstrated in literatures that MSER is only depending on the gray variations of images, and not relating with local structural characteristics and resolution of image. Here, MSER will be combined with the nonlinear epipolar rectification method proposed in this paper. The intersection of the rectified epipolar and the corresponding MSER region is determined as the feature set of spherical stereo vision. Experiments show that this study achieved the expected results.

Statistical segmentation of multidimensional brain datasets

NASA Astrophysics Data System (ADS)

Desco, Manuel; Gispert, Juan D.; Reig, Santiago; Santos, Andres; Pascau, Javier; Malpica, Norberto; Garcia-Barreno, Pedro

2001-07-01

This paper presents an automatic segmentation procedure for MRI neuroimages that overcomes part of the problems involved in multidimensional clustering techniques like partial volume effects (PVE), processing speed and difficulty of incorporating a priori knowledge. The method is a three-stage procedure: 1) Exclusion of background and skull voxels using threshold-based region growing techniques with fully automated seed selection. 2) Expectation Maximization algorithms are used to estimate the probability density function (PDF) of the remaining pixels, which are assumed to be mixtures of gaussians. These pixels can then be classified into cerebrospinal fluid (CSF), white matter and grey matter. Using this procedure, our method takes advantage of using the full covariance matrix (instead of the diagonal) for the joint PDF estimation. On the other hand, logistic discrimination techniques are more robust against violation of multi-gaussian assumptions. 3) A priori knowledge is added using Markov Random Field techniques. The algorithm has been tested with a dataset of 30 brain MRI studies (co-registered T1 and T2 MRI). Our method was compared with clustering techniques and with template-based statistical segmentation, using manual segmentation as a gold-standard. Our results were more robust and closer to the gold-standard.

Solving delay differential equations in S-ADAPT by method of steps.

PubMed

Bauer, Robert J; Mo, Gary; Krzyzanski, Wojciech

2013-09-01

S-ADAPT is a version of the ADAPT program that contains additional simulation and optimization abilities such as parametric population analysis. S-ADAPT utilizes LSODA to solve ordinary differential equations (ODEs), an algorithm designed for large dimension non-stiff and stiff problems. However, S-ADAPT does not have a solver for delay differential equations (DDEs). Our objective was to implement in S-ADAPT a DDE solver using the methods of steps. The method of steps allows one to solve virtually any DDE system by transforming it to an ODE system. The solver was validated for scalar linear DDEs with one delay and bolus and infusion inputs for which explicit analytic solutions were derived. Solutions of nonlinear DDE problems coded in S-ADAPT were validated by comparing them with ones obtained by the MATLAB DDE solver dde23. The estimation of parameters was tested on the MATLB simulated population pharmacodynamics data. The comparison of S-ADAPT generated solutions for DDE problems with the explicit solutions as well as MATLAB produced solutions which agreed to at least 7 significant digits. The population parameter estimates from using importance sampling expectation-maximization in S-ADAPT agreed with ones used to generate the data. Published by Elsevier Ireland Ltd.

Interaction and Synergism of Microbial Fuel Cell Bacteria within Methanogenesis

NASA Technical Reports Server (NTRS)

Klaus, David

2004-01-01

Biological hydrogen production from waste biomass has both terrestrial and Martian advanced life support applications. On earth, biological hydrogen production is being explored as a greenhouse neutral form of clean and efficient energy. In a permanently enclosed space habitat, carbon loop closure is required to reduce mission costs. Plants are grown to revitalize oxygen supply and are consumed by habitat inhabitants. Unharvested portions must then be recycled for reuse in the habitat. Several biological degradation techniques exist, but one process, biophotolysis, can be used to produce hydrogen from inedible plant biomass. This process is two-stage, with one stage using dark fermentation to convert plant wastes into organic acids. The second stage, photofermentation, uses photoheterotrophic purple non-sulfur bacteria with the addition of light to turn the organic acids into hydrogen and carbon dioxide. Such a system can prove useful as a co-generation scheme, providing some of the energy needed to power a larger primary carbon recovery system, such as composting. Since butyrate is expected as one of the major inputs into photofermentation, a characterization study was conducted with the bacterium Rhodobacter sphaeroides SCJ, a novel photoheterotrophic non-sulfur purple bacteria, to examine hydrogen production performance at 10 mM-100 mM butyrate concentrations. As butyrate levels increased, hydrogen production increased up to 25 mM, and then decreased and ceased by 100 mM. Additionally, lag phase increased with butyrate concentration, possibly indicating some product inhibition. Maximal substrate conversion efficiency was 8.0%; maximal light efficiency was 0.89%; and maximal hydrogen production rate was 7.7 Umol/mg/cdw/hr (173 ul/mg cdw/hr). These values were either consistent or lower than expected from literature.

Predicting Endurance Time in a Repetitive Lift and Carry Task Using Linear Mixed Models

PubMed Central

Ham, Daniel J.; Best, Stuart A.; Carstairs, Greg L.; Savage, Robert J.; Straney, Lahn; Caldwell, Joanne N.

2016-01-01

Objectives Repetitive manual handling tasks account for a substantial portion of work-related injuries. However, few studies report endurance time in repetitive manual handling tasks. Consequently, there is little guidance to inform expected work time for repetitive manual handling tasks. We aimed to investigate endurance time and oxygen consumption of a repetitive lift and carry task using linear mixed models. Methods Fourteen male soldiers (age 22.4 ± 4.5 yrs, height 1.78 ± 0.04 m, body mass 76.3 ± 10.1 kg) conducted four assessment sessions that consisted of one maximal box lifting session and three lift and carry sessions. The relationships between carry mass (range 17.5–37.5 kg) and the duration of carry, and carry mass and oxygen consumption, were assessed using linear mixed models with random effects to account for between-subject variation. Results Results demonstrated that endurance time was inversely associated with carry mass (R2 = 0.24), with significant individual-level variation (R2 = 0.85). Normalising carry mass to performance in a maximal box lifting test improved the prediction of endurance time (R2 = 0.40). Oxygen consumption presented relative to total mass (body mass, external load and carried mass) was not significantly related to lift and carry mass (β1 = 0.16, SE = 0.10, 95%CI: -0.04, 0.36, p = 0.12), indicating that there was no change in oxygen consumption relative to total mass with increasing lift and carry mass. Conclusion Practically, these data can be used to guide work-rest schedules and provide insight into methods assessing the physical capacity of workers conducting repetitive manual handling tasks. PMID:27379902

Optimized 3D stitching algorithm for whole body SPECT based on transition error minimization (TEM)

NASA Astrophysics Data System (ADS)

Cao, Xinhua; Xu, Xiaoyin; Voss, Stephan

2017-02-01

Standard Single Photon Emission Computed Tomography (SPECT) has a limited field of view (FOV) and cannot provide a 3D image of an entire long whole body SPECT. To produce a 3D whole body SPECT image, two to five overlapped SPECT FOVs from head to foot are acquired and assembled using image stitching. Most commercial software from medical imaging manufacturers applies a direct mid-slice stitching method to avoid blurring or ghosting from 3D image blending. Due to intensity changes across the middle slice of overlapped images, direct mid-slice stitching often produces visible seams in the coronal and sagittal views and maximal intensity projection (MIP). In this study, we proposed an optimized algorithm to reduce the visibility of stitching edges. The new algorithm computed, based on transition error minimization (TEM), a 3D stitching interface between two overlapped 3D SPECT images. To test the suggested algorithm, four studies of 2-FOV whole body SPECT were used and included two different reconstruction methods (filtered back projection (FBP) and ordered subset expectation maximization (OSEM)) as well as two different radiopharmaceuticals (Tc-99m MDP for bone metastases and I-131 MIBG for neuroblastoma tumors). Relative transition errors of stitched whole body SPECT using mid-slice stitching and the TEM-based algorithm were measured for objective evaluation. Preliminary experiments showed that the new algorithm reduced the visibility of the stitching interface in the coronal, sagittal, and MIP views. Average relative transition errors were reduced from 56.7% of mid-slice stitching to 11.7% of TEM-based stitching. The proposed algorithm also avoids blurring artifacts by preserving the noise properties of the original SPECT images.

Acoustic stimulation on the round window for active middle ear implants.

PubMed

Seong, Kiwoong; Lee, Kyuyup; Puria, Sunil; Cho, Jin-Ho

2018-06-01

Many clinical reports have discussed the effectiveness of stimulating the ear's round window (RW) with a tool to mitigate conductive and mixed hearing loss. The RW is one of the two openings from the middle ear into the inner ear. Various methods have been proposed to construct a highly efficient, easily implanted, and reliable RW transducer. Devices, however, such as floating mass transducers, have difficulty establishing proper contact without some degree of bone incision around the RW. Additionally, vibration energy may not be fully transmitted to the cochlea, but instead will be spread through the soft tissue around the transducer. We propose a more direct RW stimulation with very high acoustical impedance using a receiver that is a volume velocity source. We expect this source to overcome large acoustic impedance by maximizing sound pressure in a confined space, the RW niche. To verify the effectiveness of the proposed method, ear canal pressure, RW pressure, and stapes velocity are measured by acoustic RW stimulation of human temporal bones. Copyright © 2018 Elsevier Ltd. All rights reserved.

Entanglement properties of the antiferromagnetic-singlet transition in the Hubbard model on bilayer square lattices

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Chia-Chen; Singh, Rajiv R. P.; Scalettar, Richard T.

Here, we calculate the bipartite R enyi entanglement entropy of an L x L x 2 bilayer Hubbard model using a determinantal quantum Monte Carlo method recently proposed by Grover [Phys. Rev. Lett. 111, 130402 (2013)]. Two types of bipartition are studied: (i) One that divides the lattice into two L x L planes, and (ii) One that divides the lattice into two equal-size (L x L=2 x 2) bilayers. Furthermore, we compare our calculations with those for the tight-binding model studied by the correlation matrix method. As expected, the entropy for bipartition (i) scales as L 2, while themore » latter scales with L with possible logarithmic corrections. The onset of the antiferromagnet to singlet transition shows up by a saturation of the former to a maximal value and the latter to a small value in the singlet phase. We also comment on the large uncertainties in the numerical results with increasing U, which would have to be overcome before the critical behavior and logarithmic corrections can be quanti ed.« less

Entanglement properties of the antiferromagnetic-singlet transition in the Hubbard model on bilayer square lattices

DOE PAGES

Chang, Chia-Chen; Singh, Rajiv R. P.; Scalettar, Richard T.

2014-10-10

Here, we calculate the bipartite R enyi entanglement entropy of an L x L x 2 bilayer Hubbard model using a determinantal quantum Monte Carlo method recently proposed by Grover [Phys. Rev. Lett. 111, 130402 (2013)]. Two types of bipartition are studied: (i) One that divides the lattice into two L x L planes, and (ii) One that divides the lattice into two equal-size (L x L=2 x 2) bilayers. Furthermore, we compare our calculations with those for the tight-binding model studied by the correlation matrix method. As expected, the entropy for bipartition (i) scales as L 2, while themore » latter scales with L with possible logarithmic corrections. The onset of the antiferromagnet to singlet transition shows up by a saturation of the former to a maximal value and the latter to a small value in the singlet phase. We also comment on the large uncertainties in the numerical results with increasing U, which would have to be overcome before the critical behavior and logarithmic corrections can be quanti ed.« less

UniEnt: uniform entropy model for the dynamics of a neuronal population

NASA Astrophysics Data System (ADS)

Hernandez Lahme, Damian; Nemenman, Ilya

Sensory information and motor responses are encoded in the brain in a collective spiking activity of a large number of neurons. Understanding the neural code requires inferring statistical properties of such collective dynamics from multicellular neurophysiological recordings. Questions of whether synchronous activity or silence of multiple neurons carries information about the stimuli or the motor responses are especially interesting. Unfortunately, detection of such high order statistical interactions from data is especially challenging due to the exponentially large dimensionality of the state space of neural collectives. Here we present UniEnt, a method for the inference of strengths of multivariate neural interaction patterns. The method is based on the Bayesian prior that makes no assumptions (uniform a priori expectations) about the value of the entropy of the observed multivariate neural activity, in contrast to popular approaches that maximize this entropy. We then study previously published multi-electrode recordings data from salamander retina, exposing the relevance of higher order neural interaction patterns for information encoding in this system. This work was supported in part by Grants JSMF/220020321 and NSF/IOS/1208126.

Optimal stimulus scheduling for active estimation of evoked brain networks.

PubMed

Kafashan, MohammadMehdi; Ching, ShiNung

2015-12-01

We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.

Metadata from data: identifying holidays from anesthesia data.

PubMed

Starnes, Joseph R; Wanderer, Jonathan P; Ehrenfeld, Jesse M

2015-05-01

The increasingly large databases available to researchers necessitate high-quality metadata that is not always available. We describe a method for generating this metadata independently. Cluster analysis and expectation-maximization were used to separate days into holidays/weekends and regular workdays using anesthesia data from Vanderbilt University Medical Center from 2004 to 2014. This classification was then used to describe differences between the two sets of days over time. We evaluated 3802 days and correctly categorized 3797 based on anesthesia case time (representing an error rate of 0.13%). Use of other metrics for categorization, such as billed anesthesia hours and number of anesthesia cases per day, led to similar results. Analysis of the two categories showed that surgical volume increased more quickly with time for non-holidays than holidays (p < 0.001). We were able to successfully generate metadata from data by distinguishing holidays based on anesthesia data. This data can then be used for economic analysis and scheduling purposes. It is possible that the method can be expanded to similar bimodal and multimodal variables.

Selecting salient frames for spatiotemporal video modeling and segmentation.

PubMed

Song, Xiaomu; Fan, Guoliang

2007-12-01

We propose a new statistical generative model for spatiotemporal video segmentation. The objective is to partition a video sequence into homogeneous segments that can be used as "building blocks" for semantic video segmentation. The baseline framework is a Gaussian mixture model (GMM)-based video modeling approach that involves a six-dimensional spatiotemporal feature space. Specifically, we introduce the concept of frame saliency to quantify the relevancy of a video frame to the GMM-based spatiotemporal video modeling. This helps us use a small set of salient frames to facilitate the model training by reducing data redundancy and irrelevance. A modified expectation maximization algorithm is developed for simultaneous GMM training and frame saliency estimation, and the frames with the highest saliency values are extracted to refine the GMM estimation for video segmentation. Moreover, it is interesting to find that frame saliency can imply some object behaviors. This makes the proposed method also applicable to other frame-related video analysis tasks, such as key-frame extraction, video skimming, etc. Experiments on real videos demonstrate the effectiveness and efficiency of the proposed method.

Optimal stimulus scheduling for active estimation of evoked brain networks

NASA Astrophysics Data System (ADS)

Kafashan, MohammadMehdi; Ching, ShiNung

2015-12-01

Objective. We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. Approach. We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. Main results. By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. Significance. The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.

Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols.

PubMed

Zhang, Zhenzhen; O'Neill, Marie S; Sánchez, Brisa N

2016-04-01

Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM 2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany

PubMed Central

Wang, Zhu; Shuangge, Ma; Wang, Ching-Yun

2017-01-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using an open-source R package mpath. PMID:26059498

Performance analysis of EM-based blind detection for ON-OFF keying modulation over atmospheric optical channels

NASA Astrophysics Data System (ADS)

Dabiri, Mohammad Taghi; Sadough, Seyed Mohammad Sajad

2018-04-01

In the free-space optical (FSO) links, atmospheric turbulence lead to scintillation in the received signal. Due to its ease of implementation, intensity modulation with direct detection (IM/DD) based on ON-OFF keying (OOK) is a popular signaling scheme in these systems. Over turbulence channel, to detect OOK symbols in a blind way, i.e., without sending pilot symbols, an expectation-maximization (EM)-based detection method was recently proposed in the literature related to free-space optical (FSO) communication. However, the performance of EM-based detection methods severely depends on the length of the observation interval (Ls). To choose the optimum values of Ls at target bit error rates (BER)s of FSO communications which are commonly lower than 10-9, Monte-Carlo simulations would be very cumbersome and require a very long processing time. To facilitate performance evaluation, in this letter we derive the analytic expressions for BER and outage probability. Numerical results validate the accuracy of our derived analytic expressions. Our results may serve to evaluate the optimum value for Ls without resorting to time-consuming Monte-Carlo simulations.

Towards real-time detection of tumor margins using photothermal imaging of immune-targeted gold nanoparticles

PubMed Central

Jakobsohn, Kobi; Motiei, Menachem; Sinvani, Moshe; Popovtzer, Rachela

2012-01-01

Background One of the critical problems in cancer management is local recurrence of disease. Between 20% and 30% of patients who undergo tumor resection surgery require reoperation due to incomplete excision. Currently, there are no validated methods for intraoperative tumor margin detection. In the present work, we demonstrate the potential use of gold nanoparticles (GNPs) as a novel contrast agent for photothermal molecular imaging of cancer. Methods Phantoms containing different concentrations of GNPs were irradiated with continuous-wave laser and measured with a thermal imaging camera which detected the temperature field of the irradiated phantoms. Results The results clearly demonstrate the ability to distinguish between cancerous cells specifically targeted with GNPs and normal cells. This technique, which allows highly sensitive discrimination between adjacent low GNP concentrations, will allow tumor margin detection while the temperature increases by only a few degrees Celsius (for GNPs in relevant biological concentrations). Conclusion We expect this real-time intraoperative imaging technique to assist surgeons in determining clear tumor margins and to maximize the extent of tumor resection while sparing normal background tissue. PMID:22956871

Rough Sets and Stomped Normal Distribution for Simultaneous Segmentation and Bias Field Correction in Brain MR Images.

PubMed

Banerjee, Abhirup; Maji, Pradipta

2015-12-01

The segmentation of brain MR images into different tissue classes is an important task for automatic image analysis technique, particularly due to the presence of intensity inhomogeneity artifact in MR images. In this regard, this paper presents a novel approach for simultaneous segmentation and bias field correction in brain MR images. It integrates judiciously the concept of rough sets and the merit of a novel probability distribution, called stomped normal (SN) distribution. The intensity distribution of a tissue class is represented by SN distribution, where each tissue class consists of a crisp lower approximation and a probabilistic boundary region. The intensity distribution of brain MR image is modeled as a mixture of finite number of SN distributions and one uniform distribution. The proposed method incorporates both the expectation-maximization and hidden Markov random field frameworks to provide an accurate and robust segmentation. The performance of the proposed approach, along with a comparison with related methods, is demonstrated on a set of synthetic and real brain MR images for different bias fields and noise levels.

Gamma-Ray Simulated Spectrum Deconvolution of a LaBr₃ 1-in. x 1-in. Scintillator for Nondestructive ATR Fuel Burnup On-Site Predictions

DOE PAGES

Navarro, Jorge; Ring, Terry A.; Nigg, David W.

2015-03-01

A deconvolution method for a LaBr₃ 1"x1" detector for nondestructive Advanced Test Reactor (ATR) fuel burnup applications was developed. The method consisted of obtaining the detector response function, applying a deconvolution algorithm to 1”x1” LaBr₃ simulated, data along with evaluating the effects that deconvolution have on nondestructively determining ATR fuel burnup. The simulated response function of the detector was obtained using MCNPX as well with experimental data. The Maximum-Likelihood Expectation Maximization (MLEM) deconvolution algorithm was selected to enhance one-isotope source-simulated and fuel- simulated spectra. The final evaluation of the study consisted of measuring the performance of the fuel burnup calibrationmore » curve for the convoluted and deconvoluted cases. The methodology was developed in order to help design a reliable, high resolution, rugged and robust detection system for the ATR fuel canal capable of collecting high performance data for model validation, along with a system that can calculate burnup and using experimental scintillator detector data.« less

Optimal Energy Management for a Smart Grid using Resource-Aware Utility Maximization

NASA Astrophysics Data System (ADS)

Abegaz, Brook W.; Mahajan, Satish M.; Negeri, Ebisa O.

2016-06-01

Heterogeneous energy prosumers are aggregated to form a smart grid based energy community managed by a central controller which could maximize their collective energy resource utilization. Using the central controller and distributed energy management systems, various mechanisms that harness the power profile of the energy community are developed for optimal, multi-objective energy management. The proposed mechanisms include resource-aware, multi-variable energy utility maximization objectives, namely: (1) maximizing the net green energy utilization, (2) maximizing the prosumers' level of comfortable, high quality power usage, and (3) maximizing the economic dispatch of energy storage units that minimize the net energy cost of the energy community. Moreover, an optimal energy management solution that combines the three objectives has been implemented by developing novel techniques of optimally flexible (un)certainty projection and appliance based pricing decomposition in an IBM ILOG CPLEX studio. A real-world, per-minute data from an energy community consisting of forty prosumers in Amsterdam, Netherlands is used. Results show that each of the proposed mechanisms yields significant increases in the aggregate energy resource utilization and welfare of prosumers as compared to traditional peak-power reduction methods. Furthermore, the multi-objective, resource-aware utility maximization approach leads to an optimal energy equilibrium and provides a sustainable energy management solution as verified by the Lagrangian method. The proposed resource-aware mechanisms could directly benefit emerging energy communities in the world to attain their energy resource utilization targets.

Measurement the thickness of the transverse abdominal muscle in different tasks.

PubMed

Pang, Ling; Yin, Liquan; Tajiri, Kimiko; Huo, Ming; Maruyama, Hitoshi

2017-02-01

[Purpose] This study examined the measurement of the thickness of the transverse abdominal muscle in different tasks. [Subjects and Methods] The subjects were eleven healthy adult females. Thicknesses of transverse abdominal muscle were measured in seven tasks in the supine position. The tasks were: 1) Resting state, 2) Maximal contraction of transverse abdominal muscle, 3) Maximal contraction of levator ani muscle, 4) Maximal simultaneous contraction of both transverse abdominal muscle and levator ani muscle, 5) Maximal simultaneous contraction of both transverse abdominal muscle and levator ani muscle with front side resistance added to both knee, 6) Maximal simultaneous contraction of both transverse abdominal muscle and levator ani muscle with diagonal resistance added to both knees, and 7) Maximal simultaneous contraction of both transverse abdominal muscle and levator ani muscle with lateral resistance added to both knees. [Results] The thicknesses of transverse abdominal muscle during maximal simultaneous contraction and maximal simultaneous contraction with resistance were greater than during the resting state. [Conclusion] The muscle output during simultaneous contraction and resistance movement were larger than that of each individual muscle.

Variation in opsin genes correlates with signaling ecology in North American fireflies

PubMed Central

Sander, Sarah E.; Hall, David W.

2015-01-01

Genes underlying signal reception should evolve to maximize signal detection in a particular environment. In animals, opsins, the protein component of visual pigments, are predicted to evolve according to this expectation. Fireflies are known for their bioluminescent mating signals. The eyes of nocturnal species are expected to maximize detection of conspecific signal colors emitted in the typical low-light environment. This is not expected for species that have transitioned to diurnal activity in bright daytime environments. Here we test the hypothesis that opsin gene sequence plays a role in modifying firefly eye spectral sensitivity. We use genome and transcriptome sequencing in four firefly species, transcriptome sequencing in six additional species, and targeted gene sequencing in 28 other species to identify all opsin genes present in North American fireflies and to elucidate amino acid sites under positive selection. We also determine whether amino acid substitutions in opsins are linked to evolutionary changes in signal mode, signal color, and light environment. We find only two opsins, one long wavelength and one ultraviolet, in all firefly species and identify 25 candidate sites that may be involved in determining spectral sensitivity. In addition, we find elevated rates of evolution at transitions to diurnal activity, and changes in selective constraint on LW opsin associated with changes in light environment. Our results suggest that changes in eye spectral sensitivity are at least partially due to opsin sequence. Fireflies continue to be a promising system in which to investigate the evolution of signals, receptors, and signaling environments. PMID:26289828

Sequoia Messaging Rate Benchmark

DOE Office of Scientific and Technical Information (OSTI.GOV)

Friedley, Andrew

2008-01-22

The purpose of this benchmark is to measure the maximal message rate of a single compute node. The first num_cores ranks are expected to reside on the 'core' compute node for which message rate is being tested. After that, the next num_nbors ranks are neighbors for the first core rank, the next set of num_nbors ranks are neighbors for the second core rank, and so on. For example, testing an 8-core node (num_cores = 8) with 4 neighbors (num_nbors = 4) requires 8 + 8 * 4 - 40 ranks. The first 8 of those 40 ranks are expected tomore » be on the 'core' node being benchmarked, while the rest of the ranks are on separate nodes.« less

Net present value analysis to select public R&D programs and valuate expected private sector participation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hinman, N.D.; Yancey, M.A.

1997-12-31

One of the main functions of government is to invest taxpayers dollars in projects, programs, and properties that will result in social benefit. Public programs focused on the development of technology are examples of such opportunities. Selecting these programs requires the same investment analysis approaches that private companies and individuals use. Good use of investment analysis approaches to these programs will minimize our tax costs and maximize public benefit from tax dollars invested. This article describes the use of the net present value (NPV) analysis approach to select public R&D programs and valuate expected private sector participation in the programs.more » 5 refs.« less

Noisy preferences in risky choice: A cautionary note.

PubMed

Bhatia, Sudeep; Loomes, Graham

2017-10-01

We examine the effects of multiple sources of noise in risky decision making. Noise in the parameters that characterize an individual's preferences can combine with noise in the response process to distort observed choice proportions. Thus, underlying preferences that conform to expected value maximization can appear to show systematic risk aversion or risk seeking. Similarly, core preferences that are consistent with expected utility theory, when perturbed by such noise, can appear to display nonlinear probability weighting. For this reason, modal choices cannot be used simplistically to infer underlying preferences. Quantitative model fits that do not allow for both sorts of noise can lead to wrong conclusions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Caregiver and expecting caregiver support for early peanut introduction guidelines.

PubMed

Greenhawt, Matthew; Chan, Edmond S; Fleischer, David M; Hicks, Allison; Wilson, Rachel; Shaker, Marcus; Venter, Carina; Stukus, David

2018-03-07

Recent guidelines recommend early peanut introduction (EPI) beginning around 4 to 6 months of age in infants with severe eczema and/or egg allergy and around 6 months for all other infants. Caregiver preferences for such practices are unknown. We explored preferences for EPI and in-office allergy risk assessment (IRA) through a nationally representative survey of expecting (n = 1,000) and new caregivers of infants younger than 1 year (n = 1,000). Among a primarily female (99.7%), married (80.3%), and white (74.4%) sample, 29% had no or vague awareness of the new guidelines, 61% had no or minimal concern for their child developing food allergy, but 54% felt timing of food introduction has moderate to strong importance for developing food allergy. Only 31% expressed willingness for EPI before or around 6 months of age, with 40% reporting willingness to introduce peanut after 11 months of age, similar to tree nuts and seafood. However, 60% reported willingness to introduce egg before 8 months of age. A total of 51% and 56.8% were unwilling to allow IRA methods, such as skin testing and oral challenge, before 11 months of age, respectively. Odds of willingness to delay peanut introduction (odds ratio, 0.79; 95% confidence interval, 0.65-0.96) and undergo challenge (odds ratio, 0.67; 95% confidence interval, 0.54-0.82) after 6 months of age were lower among expecting caregivers. Among new and expecting caregivers, there is poor current willingness and questionable support for early allergenic solid food recommendations, including IRA before introduction. Willingness was better among expecting vs current caregivers. These trends underscore a need for broader formal implementation planning to facilitate early allergen introduction and maximize its preventive benefits. Copyright © 2018 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Profiting from competition: Financial tools for electric generation companies

NASA Astrophysics Data System (ADS)

Richter, Charles William, Jr.

Regulations governing the operation of electric power systems in North America and many other areas of the world are undergoing major changes designed to promote competition. This process of change is often referred to as deregulation. Participants in deregulated electricity systems may find that their profits will greatly benefit from the implementation of successful bidding strategies. While the goal of the regulators may be to create rules which balance reliable power system operation with maximization of the total benefit to society, the goal of generation companies is to maximize their profit, i.e., return to their shareholders. The majority of the research described here is conducted from the point of view of generation companies (GENCOs) wishing to maximize their expected utility function, which is generally comprised of expected profit and risk. Strategies that help a GENCO to maximize its objective function must consider the impact of (and aid in making) operating decisions that may occur within a few seconds to multiple years. The work described here assumes an environment in which energy service companies (ESCOs) buy and GENCOs sell power via double auctions in regional commodity exchanges. Power is transported on wires owned by transmission companies (TRANSCOs) and distribution companies (DISTCOs). The proposed market framework allows participants to trade electrical energy contracts via the spot, futures, options, planning, and swap markets. An important method of studying these proposed markets and the behavior of participating agents is the field of experimental/computational economics. For much of the research reported here, the market simulator developed by Kumar and Sheble and similar simulators has been adapted to allow computerized agents to trade energy. Creating computerized agents that can react as rationally or irrationally as a human trader is a difficult problem for which we have turned to the field of artificial intelligence. Some of our work uses GP-Automata, a technique which combines genetic programming and finite state machines, to represent adaptive agents. We use a genetic algorithm to evolve these adaptive agents (each with its own bidding strategy) for use in a double auction. The agent's strategies may be judged by the amount of profit they produce and are tested by computerized agents repeatedly buying and selling electricity in an auction simulator. In addition to the obvious profit-maximization strategies, one can also design strategies which exhibit other types of trading behaviors. The resulting strategies can be used directly in on-line trading, or as realistic models of competitors in a trading simulator. In addition to developing double auction bidding strategies, we investigate and discuss methods of an energy trader's risk. This can be done using such financial vehicles as futures and options contracts or through the inclusion of risk while judging strategies used in the market simulations described above. We discuss the role of fuzzy logic in the competitive electric marketplace, including how it can be applied in developing bidding strategies. Since competition promises to drive the power system closer to its operating limits, improvements in measurement and system control will be important. We provide an example of using fuzzy logic to do automatic generation control and discuss extensions that would make it superior to traditional controllers. Since the GENCO's forte is primarily generating electricity, we examine unit commitment and discuss how to update it for the competitive environment. We discuss the role of unit commitment in developing bidding strategies, as well as, the role of bidding strategies in solving the unit commitment problem. Depending on the market structure adopted by a particular location, large amounts of bidding data may be available to regulators or market participants. Ideally, regulators could use this data to verify dig the market is efficient. Market participants with access to this data might gain an advantage over their competitors if they could somehow determine their competitor's bidding strategy. We outline methods of automatically inferring other participants' trading rules based on historical data. Much of the work described here should aid in the design of effective operating procedures, trading strategies and profitable portfolios for energy producers.

Regularized minimum I-divergence methods for the inverse blackbody radiation problem

NASA Astrophysics Data System (ADS)

Choi, Kerkil; Lanterman, Aaron D.; Shin, Jaemin

2006-08-01

This paper proposes iterative methods for estimating the area temperature distribution of a blackbody from its total radiated power spectrum measurements. This is called the inverse blackbody radiation problem. This problem is inherently ill-posed due to the characteristics of the kernel in the underlying integral equation given by Planck's law. The functions involved in the problem are all non-negative. Csiszár's I-divergence is an information-theoretic discrepancy measure between two non-negative functions. We derive iterative methods for minimizing Csiszár's I-divergence between the measured power spectrum and the power spectrum arising from the estimate according to the integral equation. Due to the ill-posedness of the problem, unconstrained algorithms often produce poor estimates, especially when the measurements are corrupted by noise. To alleviate this difficulty, we apply regularization methods to our algorithms. Penalties based on Shannon's entropy, the L1-norm and Good's roughness are chosen to suppress the undesirable artefacts. When a penalty is applied, the pertinent optimization that needs to be performed at each iteration is no longer trivial. In particular, Good's roughness causes couplings between estimate components. To handle this issue, we adapt Green's one-step-late method. This choice is based on the important fact that our minimum I-divergence algorithms can be interpreted as asymptotic forms of certain expectation-maximization algorithms. The effectiveness of our methods is illustrated via various numerical experiments.

Quantifying the Value of Downscaled Climate Model Information for Adaptation Decisions: When is Downscaling a Smart Decision?

NASA Astrophysics Data System (ADS)

Terando, A. J.; Wootten, A.; Eaton, M. J.; Runge, M. C.; Littell, J. S.; Bryan, A. M.; Carter, S. L.

2015-12-01

Two types of decisions face society with respect to anthropogenic climate change: (1) whether to enact a global greenhouse gas abatement policy, and (2) how to adapt to the local consequences of current and future climatic changes. The practice of downscaling global climate models (GCMs) is often used to address (2) because GCMs do not resolve key features that will mediate global climate change at the local scale. In response, the development of downscaling techniques and models has accelerated to aid decision makers seeking adaptation guidance. However, quantifiable estimates of the value of information are difficult to obtain, particularly in decision contexts characterized by deep uncertainty and low system-controllability. Here we demonstrate a method to quantify the additional value that decision makers could expect if research investments are directed towards developing new downscaled climate projections. As a proof of concept we focus on a real-world management problem: whether to undertake assisted migration for an endangered tropical avian species. We also take advantage of recently published multivariate methods that account for three vexing issues in climate impacts modeling: maximizing climate model quality information, accounting for model dependence in ensembles of opportunity, and deriving probabilistic projections. We expand on these global methods by including regional (Caribbean Basin) and local (Puerto Rico) domains. In the local domain, we test whether a high resolution (2km) dynamically downscaled GCM reduces the multivariate error estimate compared to the original coarse-scale GCM. Initial tests show little difference between the downscaled and original GCM multivariate error. When propagated through to a species population model, the Value of Information analysis indicates that the expected utility that would accrue to the manager (and species) if this downscaling were completed may not justify the cost compared to alternative actions.

A topological proof of chaos for two nonlinear heterogeneous triopoly game models

NASA Astrophysics Data System (ADS)

Pireddu, Marina

2016-08-01

We rigorously prove the existence of chaotic dynamics for two nonlinear Cournot triopoly game models with heterogeneous players, for which in the existing literature the presence of complex phenomena and strange attractors has been shown via numerical simulations. In the first model that we analyze, costs are linear but the demand function is isoelastic, while, in the second model, the demand function is linear and production costs are quadratic. As concerns the decisional mechanisms adopted by the firms, in both models one firm adopts a myopic adjustment mechanism, considering the marginal profit of the last period; the second firm maximizes its own expected profit under the assumption that the competitors' production levels will not vary with respect to the previous period; the third firm acts adaptively, changing its output proportionally to the difference between its own output in the previous period and the naive expectation value. The topological method we employ in our analysis is the so-called "Stretching Along the Paths" technique, based on the Poincaré-Miranda Theorem and the properties of the cutting surfaces, which allows to prove the existence of a semi-conjugacy between the system under consideration and the Bernoulli shift, so that the former inherits from the latter several crucial chaotic features, among which a positive topological entropy.

Maximum entropy approach to statistical inference for an ocean acoustic waveguide.

PubMed

Knobles, D P; Sagers, J D; Koch, R A

2012-02-01

A conditional probability distribution suitable for estimating the statistical properties of ocean seabed parameter values inferred from acoustic measurements is derived from a maximum entropy principle. The specification of the expectation value for an error function constrains the maximization of an entropy functional. This constraint determines the sensitivity factor (β) to the error function of the resulting probability distribution, which is a canonical form that provides a conservative estimate of the uncertainty of the parameter values. From the conditional distribution, marginal distributions for individual parameters can be determined from integration over the other parameters. The approach is an alternative to obtaining the posterior probability distribution without an intermediary determination of the likelihood function followed by an application of Bayes' rule. In this paper the expectation value that specifies the constraint is determined from the values of the error function for the model solutions obtained from a sparse number of data samples. The method is applied to ocean acoustic measurements taken on the New Jersey continental shelf. The marginal probability distribution for the values of the sound speed ratio at the surface of the seabed and the source levels of a towed source are examined for different geoacoustic model representations. © 2012 Acoustical Society of America

Constrained inference in mixed-effects models for longitudinal data with application to hearing loss.

PubMed

Davidov, Ori; Rosen, Sophia

2011-04-01

In medical studies, endpoints are often measured for each patient longitudinally. The mixed-effects model has been a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, in hearing loss studies, we expect hearing to deteriorate with time. This means that hearing thresholds which reflect hearing acuity will, on average, increase over time. Therefore, the regression coefficients associated with the mean effect of time on hearing ability will be constrained. Such constraints should be accounted for in the analysis. We propose maximum likelihood estimation procedures, based on the expectation-conditional maximization either algorithm, to estimate the parameters of the model while accounting for the constraints on them. The proposed methods improve, in terms of mean square error, on the unconstrained estimators. In some settings, the improvement may be substantial. Hypotheses testing procedures that incorporate the constraints are developed. Specifically, likelihood ratio, Wald, and score tests are proposed and investigated. Their empirical significance levels and power are studied using simulations. It is shown that incorporating the constraints improves the mean squared error of the estimates and the power of the tests. These improvements may be substantial. The methodology is used to analyze a hearing loss study.

Robust Bayesian Experimental Design for Conceptual Model Discrimination

NASA Astrophysics Data System (ADS)

Pham, H. V.; Tsai, F. T. C.

2015-12-01

A robust Bayesian optimal experimental design under uncertainty is presented to provide firm information for model discrimination, given the least number of pumping wells and observation wells. Firm information is the maximum information of a system can be guaranteed from an experimental design. The design is based on the Box-Hill expected entropy decrease (EED) before and after the experiment design and the Bayesian model averaging (BMA) framework. A max-min programming is introduced to choose the robust design that maximizes the minimal Box-Hill EED subject to that the highest expected posterior model probability satisfies a desired probability threshold. The EED is calculated by the Gauss-Hermite quadrature. The BMA method is used to predict future observations and to quantify future observation uncertainty arising from conceptual and parametric uncertainties in calculating EED. Monte Carlo approach is adopted to quantify the uncertainty in the posterior model probabilities. The optimal experimental design is tested by a synthetic 5-layer anisotropic confined aquifer. Nine conceptual groundwater models are constructed due to uncertain geological architecture and boundary condition. High-performance computing is used to enumerate all possible design solutions in order to identify the most plausible groundwater model. Results highlight the impacts of scedasticity in future observation data as well as uncertainty sources on potential pumping and observation locations.

Cure frailty models for survival data: application to recurrences for breast cancer and to hospital readmissions for colorectal cancer.

PubMed

Rondeau, Virginie; Schaffner, Emmanuel; Corbière, Fabien; Gonzalez, Juan R; Mathoulin-Pélissier, Simone

2013-06-01

Owing to the natural evolution of a disease, several events often arise after a first treatment for the same subject. For example, patients with a primary invasive breast cancer and treated with breast conserving surgery may experience breast cancer recurrences, metastases or death. A certain proportion of subjects in the population who are not expected to experience the events of interest are considered to be 'cured' or non-susceptible. To model correlated failure time data incorporating a surviving fraction, we compare several forms of cure rate frailty models. In the first model already proposed non-susceptible patients are those who are not expected to experience the event of interest over a sufficiently long period of time. The other proposed models account for the possibility of cure after each event. We illustrate the cure frailty models with two data sets. First to analyse time-dependent prognostic factors associated with breast cancer recurrences, metastases, new primary malignancy and death. Second to analyse successive rehospitalizations of patients diagnosed with colorectal cancer. Estimates were obtained by maximization of likelihood using SAS proc NLMIXED for a piecewise constant hazards model. As opposed to the simple frailty model, the proposed methods demonstrate great potential in modelling multivariate survival data with long-term survivors ('cured' individuals).

A topological proof of chaos for two nonlinear heterogeneous triopoly game models.

PubMed

Pireddu, Marina

2016-08-01

We rigorously prove the existence of chaotic dynamics for two nonlinear Cournot triopoly game models with heterogeneous players, for which in the existing literature the presence of complex phenomena and strange attractors has been shown via numerical simulations. In the first model that we analyze, costs are linear but the demand function is isoelastic, while, in the second model, the demand function is linear and production costs are quadratic. As concerns the decisional mechanisms adopted by the firms, in both models one firm adopts a myopic adjustment mechanism, considering the marginal profit of the last period; the second firm maximizes its own expected profit under the assumption that the competitors' production levels will not vary with respect to the previous period; the third firm acts adaptively, changing its output proportionally to the difference between its own output in the previous period and the naive expectation value. The topological method we employ in our analysis is the so-called "Stretching Along the Paths" technique, based on the Poincaré-Miranda Theorem and the properties of the cutting surfaces, which allows to prove the existence of a semi-conjugacy between the system under consideration and the Bernoulli shift, so that the former inherits from the latter several crucial chaotic features, among which a positive topological entropy.

Bayesian cross-entropy methodology for optimal design of validation experiments

NASA Astrophysics Data System (ADS)

Jiang, X.; Mahadevan, S.

2006-07-01

An important concern in the design of validation experiments is how to incorporate the mathematical model in the design in order to allow conclusive comparisons of model prediction with experimental output in model assessment. The classical experimental design methods are more suitable for phenomena discovery and may result in a subjective, expensive, time-consuming and ineffective design that may adversely impact these comparisons. In this paper, an integrated Bayesian cross-entropy methodology is proposed to perform the optimal design of validation experiments incorporating the computational model. The expected cross entropy, an information-theoretic distance between the distributions of model prediction and experimental observation, is defined as a utility function to measure the similarity of two distributions. A simulated annealing algorithm is used to find optimal values of input variables through minimizing or maximizing the expected cross entropy. The measured data after testing with the optimum input values are used to update the distribution of the experimental output using Bayes theorem. The procedure is repeated to adaptively design the required number of experiments for model assessment, each time ensuring that the experiment provides effective comparison for validation. The methodology is illustrated for the optimal design of validation experiments for a three-leg bolted joint structure and a composite helicopter rotor hub component.

Evidence for maximal acceleration and singularity resolution in covariant loop quantum gravity.

PubMed

Rovelli, Carlo; Vidotto, Francesca

2013-08-30

A simple argument indicates that covariant loop gravity (spin foam theory) predicts a maximal acceleration and hence forbids the development of curvature singularities. This supports the results obtained for cosmology and black holes using canonical methods.

Formal Darwinism, the individual-as-maximizing-agent analogy and bet-hedging

PubMed Central

Grafen, A.

1999-01-01

The central argument of The origin of species was that mechanical processes (inheritance of features and the differential reproduction they cause) can give rise to the appearance of design. The 'mechanical processes' are now mathematically represented by the dynamic systems of population genetics, and the appearance of design by optimization and game theory in which the individual plays the part of the maximizing agent. Establishing a precise individual-as-maximizing-agent (IMA) analogy for a population-genetics system justifies optimization approaches, and so provides a modern formal representation of the core of Darwinism. It is a hitherto unnoticed implication of recent population-genetics models that, contrary to a decades-long consensus, an IMA analogy can be found in models with stochastic environments (subject to a convexity assumption), in which individuals maximize expected reproductive value. The key is that the total reproductive value of a species must be considered as constant, so therefore reproductive value should always be calculated in relative terms. This result removes a major obstacle from the theoretical challenge to find a unifying framework which establishes the IMA analogy for all of Darwinian biology, including as special cases inclusive fitness, evolutionarily stable strategies, evolutionary life-history theory, age-structured models and sex ratio theory. This would provide a formal, mathematical justification of fruitful and widespread but 'intentional' terms in evolutionary biology, such as 'selfish', 'altruism' and 'conflict'.

Do framing effects reveal irrational choice?

PubMed

Mandel, David R

2014-06-01

Framing effects have long been viewed as compelling evidence of irrationality in human decision making, yet that view rests on the questionable assumption that numeric quantifiers used to convey the expected values of choice options are uniformly interpreted as exact values. Two experiments show that when the exactness of such quantifiers is made explicit by the experimenter, framing effects vanish. However, when the same quantifiers are given a lower bound (at least) meaning, the typical framing effect is found. A 3rd experiment confirmed that most people spontaneously interpret the quantifiers in standard framing tests as lower bounded and that their interpretations strongly moderate the framing effect. Notably, in each experiment, a significant majority of participants made rational choices, either choosing the option that maximized expected value (i.e., lives saved) or choosing consistently across frames when the options were of equal expected value. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Do Hypervolumes Have Holes?

PubMed

Blonder, Benjamin

2016-04-01

Hypervolumes are used widely to conceptualize niches and trait distributions for both species and communities. Some hypervolumes are expected to be convex, with boundaries defined by only upper and lower limits (e.g., fundamental niches), while others are expected to be maximal, with boundaries defined by the limits of available space (e.g., potential niches). However, observed hypervolumes (e.g., realized niches) could also have holes, defined as unoccupied hyperspace representing deviations from these expectations that may indicate unconsidered ecological or evolutionary processes. Detecting holes in more than two dimensions has to date not been possible. I develop a mathematical approach, implemented in the hypervolume R package, to infer holes in large and high-dimensional data sets. As a demonstration analysis, I assess evidence for vacant niches in a Galapagos finch community on Isabela Island. These mathematical concepts and software tools for detecting holes provide approaches for addressing contemporary research questions across ecology and evolutionary biology.