Understanding seasonal variability of uncertainty in hydrological prediction

NASA Astrophysics Data System (ADS)

Li, M.; Wang, Q. J.

2012-04-01

Understanding uncertainty in hydrological prediction can be highly valuable for improving the reliability of streamflow prediction. In this study, a monthly water balance model, WAPABA, in a Bayesian joint probability with error models are presented to investigate the seasonal dependency of prediction error structure. A seasonal invariant error model, analogous to traditional time series analysis, uses constant parameters for model error and account for no seasonal variations. In contrast, a seasonal variant error model uses a different set of parameters for bias, variance and autocorrelation for each individual calendar month. Potential connection amongst model parameters from similar months is not considered within the seasonal variant model and could result in over-fitting and over-parameterization. A hierarchical error model further applies some distributional restrictions on model parameters within a Bayesian hierarchical framework. An iterative algorithm is implemented to expedite the maximum a posterior (MAP) estimation of a hierarchical error model. Three error models are applied to forecasting streamflow at a catchment in southeast Australia in a cross-validation analysis. This study also presents a number of statistical measures and graphical tools to compare the predictive skills of different error models. From probability integral transform histograms and other diagnostic graphs, the hierarchical error model conforms better to reliability when compared to the seasonal invariant error model. The hierarchical error model also generally provides the most accurate mean prediction in terms of the Nash-Sutcliffe model efficiency coefficient and the best probabilistic prediction in terms of the continuous ranked probability score (CRPS). The model parameters of the seasonal variant error model are very sensitive to each cross validation, while the hierarchical error model produces much more robust and reliable model parameters. Furthermore, the result of the hierarchical error model shows that most of model parameters are not seasonal variant except for error bias. The seasonal variant error model is likely to use more parameters than necessary to maximize the posterior likelihood. The model flexibility and robustness indicates that the hierarchical error model has great potential for future streamflow predictions.

Hierarchical models for informing general biomass equations with felled tree data

Treesearch

Brian J. Clough; Matthew B. Russell; Christopher W. Woodall; Grant M. Domke; Philip J. Radtke

2015-01-01

We present a hierarchical framework that uses a large multispecies felled tree database to inform a set of general models for predicting tree foliage biomass, with accompanying uncertainty, within the FIA database. Results suggest significant prediction uncertainty for individual trees and reveal higher errors when predicting foliage biomass for larger trees and for...

Predicting Motivation: Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease.

PubMed

Vassena, Eliana; Deraeve, James; Alexander, William H

2017-10-01

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response-outcome [PRO] model [Alexander, W. H., & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14, 1338-1344, 2011], hierarchical error representation [HER] model [Alexander, W. H., & Brown, J. W. Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354-2410, 2015]). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO model based on hierarchical error prediction, developed to explain MPFC-DLPFC interactions. We derive behavioral predictions that describe how effort and reward information is coded in PFC and how changing the configuration of such environmental information might affect decision-making and task performance involving motivation.

Computational intelligence in earth sciences and environmental applications: issues and challenges.

PubMed

Cherkassky, V; Krasnopolsky, V; Solomatine, D P; Valdes, J

2006-03-01

This paper introduces a generic theoretical framework for predictive learning, and relates it to data-driven and learning applications in earth and environmental sciences. The issues of data quality, selection of the error function, incorporation of the predictive learning methods into the existing modeling frameworks, expert knowledge, model uncertainty, and other application-domain specific problems are discussed. A brief overview of the papers in the Special Issue is provided, followed by discussion of open issues and directions for future research.

Stochastic stability of sigma-point Unscented Predictive Filter.

PubMed

Cao, Lu; Tang, Yu; Chen, Xiaoqian; Zhao, Yong

2015-07-01

In this paper, the Unscented Predictive Filter (UPF) is derived based on unscented transformation for nonlinear estimation, which breaks the confine of conventional sigma-point filters by employing Kalman filter as subject investigated merely. In order to facilitate the new method, the algorithm flow of UPF is given firstly. Then, the theoretical analyses demonstrate that the estimate accuracy of the model error and system for the UPF is higher than that of the conventional PF. Moreover, the authors analyze the stochastic boundedness and the error behavior of Unscented Predictive Filter (UPF) for general nonlinear systems in a stochastic framework. In particular, the theoretical results present that the estimation error remains bounded and the covariance keeps stable if the system׳s initial estimation error, disturbing noise terms as well as the model error are small enough, which is the core part of the UPF theory. All of the results have been demonstrated by numerical simulations for a nonlinear example system. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Five-equation and robust three-equation methods for solution verification of large eddy simulation

NASA Astrophysics Data System (ADS)

Dutta, Rabijit; Xing, Tao

2018-02-01

This study evaluates the recently developed general framework for solution verification methods for large eddy simulation (LES) using implicitly filtered LES of periodic channel flows at friction Reynolds number of 395 on eight systematically refined grids. The seven-equation method shows that the coupling error based on Hypothesis I is much smaller as compared with the numerical and modeling errors and therefore can be neglected. The authors recommend five-equation method based on Hypothesis II, which shows a monotonic convergence behavior of the predicted numerical benchmark ( S C ), and provides realistic error estimates without the need of fixing the orders of accuracy for either numerical or modeling errors. Based on the results from seven-equation and five-equation methods, less expensive three and four-equation methods for practical LES applications were derived. It was found that the new three-equation method is robust as it can be applied to any convergence types and reasonably predict the error trends. It was also observed that the numerical and modeling errors usually have opposite signs, which suggests error cancellation play an essential role in LES. When Reynolds averaged Navier-Stokes (RANS) based error estimation method is applied, it shows significant error in the prediction of S C on coarse meshes. However, it predicts reasonable S C when the grids resolve at least 80% of the total turbulent kinetic energy.

Does the sensorimotor system minimize prediction error or select the most likely prediction during object lifting?

PubMed Central

McGregor, Heather R.; Pun, Henry C. H.; Buckingham, Gavin; Gribble, Paul L.

2016-01-01

The human sensorimotor system is routinely capable of making accurate predictions about an object's weight, which allows for energetically efficient lifts and prevents objects from being dropped. Often, however, poor predictions arise when the weight of an object can vary and sensory cues about object weight are sparse (e.g., picking up an opaque water bottle). The question arises, what strategies does the sensorimotor system use to make weight predictions when one is dealing with an object whose weight may vary? For example, does the sensorimotor system use a strategy that minimizes prediction error (minimal squared error) or one that selects the weight that is most likely to be correct (maximum a posteriori)? In this study we dissociated the predictions of these two strategies by having participants lift an object whose weight varied according to a skewed probability distribution. We found, using a small range of weight uncertainty, that four indexes of sensorimotor prediction (grip force rate, grip force, load force rate, and load force) were consistent with a feedforward strategy that minimizes the square of prediction errors. These findings match research in the visuomotor system, suggesting parallels in underlying processes. We interpret our findings within a Bayesian framework and discuss the potential benefits of using a minimal squared error strategy. NEW & NOTEWORTHY Using a novel experimental model of object lifting, we tested whether the sensorimotor system models the weight of objects by minimizing lifting errors or by selecting the statistically most likely weight. We found that the sensorimotor system minimizes the square of prediction errors for object lifting. This parallels the results of studies that investigated visually guided reaching, suggesting an overlap in the underlying mechanisms between tasks that involve different sensory systems. PMID:27760821

The role of bias in simulation of the Indian monsoon and its relationship to predictability

NASA Astrophysics Data System (ADS)

Kelly, P.

2016-12-01

Confidence in future projections of how climate change will affect the Indian monsoon is currently limited by- among other things-model biases. That is, the systematic error in simulating the mean present day climate. An important priority question in seamless prediction involves the role of the mean state. How much of the prediction error in imperfect models stems from a biased mean state (itself a result of many interacting process errors), and how much stems from the flow dependence of processes during an oscillation or variation we are trying to predict? Using simple but effective nudging techniques, we are able to address this question in a clean and incisive framework that teases apart the roles of the mean state vs. transient flow dependence in constraining predictability. The role of bias in model fidelity of simulations of the Indian monsoon is investigated in CAM5, and the relationship to predictability in remote regions in the "free" (non-nudged) domain is explored.

Many-objective Groundwater Monitoring Network Design Using Bias-Aware Ensemble Kalman Filtering and Evolutionary Optimization

NASA Astrophysics Data System (ADS)

Kollat, J. B.; Reed, P. M.

2009-12-01

This study contributes the ASSIST (Adaptive Strategies for Sampling in Space and Time) framework for improving long-term groundwater monitoring decisions across space and time while accounting for the influences of systematic model errors (or predictive bias). The ASSIST framework combines contaminant flow-and-transport modeling, bias-aware ensemble Kalman filtering (EnKF) and many-objective evolutionary optimization. Our goal in this work is to provide decision makers with a fuller understanding of the information tradeoffs they must confront when performing long-term groundwater monitoring network design. Our many-objective analysis considers up to 6 design objectives simultaneously and consequently synthesizes prior monitoring network design methodologies into a single, flexible framework. This study demonstrates the ASSIST framework using a tracer study conducted within a physical aquifer transport experimental tank located at the University of Vermont. The tank tracer experiment was extensively sampled to provide high resolution estimates of tracer plume behavior. The simulation component of the ASSIST framework consists of stochastic ensemble flow-and-transport predictions using ParFlow coupled with the Lagrangian SLIM transport model. The ParFlow and SLIM ensemble predictions are conditioned with tracer observations using a bias-aware EnKF. The EnKF allows decision makers to enhance plume transport predictions in space and time in the presence of uncertain and biased model predictions by conditioning them on uncertain measurement data. In this initial demonstration, the position and frequency of sampling were optimized to: (i) minimize monitoring cost, (ii) maximize information provided to the EnKF, (iii) minimize failure to detect the tracer, (iv) maximize the detection of tracer flux, (v) minimize error in quantifying tracer mass, and (vi) minimize error in quantifying the moment of the tracer plume. The results demonstrate that the many-objective problem formulation provides a tremendous amount of information for decision makers. Specifically our many-objective analysis highlights the limitations and potentially negative design consequences of traditional single and two-objective problem formulations. These consequences become apparent through visual exploration of high-dimensional tradeoffs and the identification of regions with interesting compromise solutions. The prediction characteristics of these compromise designs are explored in detail, as well as their implications for subsequent design decisions in both space and time.

A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

NASA Astrophysics Data System (ADS)

Liang, Zhongmin; Li, Yujie; Hu, Yiming; Li, Binquan; Wang, Jun

2017-06-01

Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.

Path-following in model predictive rollover prevention using front steering and braking

NASA Astrophysics Data System (ADS)

Ghazali, Mohammad; Durali, Mohammad; Salarieh, Hassan

2017-01-01

In this paper vehicle path-following in the presence of rollover risk is investigated. Vehicles with high centre of mass are prone to roll instability. Untripped rollover risk is increased in high centre of gravity vehicles and high-friction road condition. Researches introduce strategies to handle the short-duration rollover condition. In these researches, however, trajectory tracking is affected and not thoroughly investigated. This paper puts stress on tracking error from rollover prevention. A lower level model predictive front steering controller is adopted to deal with rollover and tracking error as a priority sequence. A brake control is included in lower level controller which directly obeys an upper level controller (ULC) command. The ULC manages vehicle speed regarding primarily tracking error. Simulation results show that the proposed control framework maintains roll stability while tracking error is confined to predefined error limit.

The Representation of Prediction Error in Auditory Cortex

PubMed Central

Rubin, Jonathan; Ulanovsky, Nachum; Tishby, Naftali

2016-01-01

To survive, organisms must extract information from the past that is relevant for their future. How this process is expressed at the neural level remains unclear. We address this problem by developing a novel approach from first principles. We show here how to generate low-complexity representations of the past that produce optimal predictions of future events. We then illustrate this framework by studying the coding of ‘oddball’ sequences in auditory cortex. We find that for many neurons in primary auditory cortex, trial-by-trial fluctuations of neuronal responses correlate with the theoretical prediction error calculated from the short-term past of the stimulation sequence, under constraints on the complexity of the representation of this past sequence. In some neurons, the effect of prediction error accounted for more than 50% of response variability. Reliable predictions often depended on a representation of the sequence of the last ten or more stimuli, although the representation kept only few details of that sequence. PMID:27490251

Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose

PubMed Central

Sun, Xiyang; Miao, Jiacheng; Wang, You; Luo, Zhiyuan; Li, Guang

2017-01-01

An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of conformal prediction. In offline mode, the conformal predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity. PMID:28805721

Spatial-temporal-covariance-based modeling, analysis, and simulation of aero-optics wavefront aberrations.

PubMed

Vogel, Curtis R; Tyler, Glenn A; Wittich, Donald J

2014-07-01

We introduce a framework for modeling, analysis, and simulation of aero-optics wavefront aberrations that is based on spatial-temporal covariance matrices extracted from wavefront sensor measurements. Within this framework, we present a quasi-homogeneous structure function to analyze nonhomogeneous, mildly anisotropic spatial random processes, and we use this structure function to show that phase aberrations arising in aero-optics are, for an important range of operating parameters, locally Kolmogorov. This strongly suggests that the d5/3 power law for adaptive optics (AO) deformable mirror fitting error, where d denotes actuator separation, holds for certain important aero-optics scenarios. This framework also allows us to compute bounds on AO servo lag error and predictive control error. In addition, it provides us with the means to accurately simulate AO systems for the mitigation of aero-effects, and it may provide insight into underlying physical processes associated with turbulent flow. The techniques introduced here are demonstrated using data obtained from the Airborne Aero-Optics Laboratory.

A Conjoint Analysis Framework for Evaluating User Preferences in Machine Translation

PubMed Central

Kirchhoff, Katrin; Capurro, Daniel; Turner, Anne M.

2013-01-01

Despite much research on machine translation (MT) evaluation, there is surprisingly little work that directly measures users’ intuitive or emotional preferences regarding different types of MT errors. However, the elicitation and modeling of user preferences is an important prerequisite for research on user adaptation and customization of MT engines. In this paper we explore the use of conjoint analysis as a formal quantitative framework to assess users’ relative preferences for different types of translation errors. We apply our approach to the analysis of MT output from translating public health documents from English into Spanish. Our results indicate that word order errors are clearly the most dispreferred error type, followed by word sense, morphological, and function word errors. The conjoint analysis-based model is able to predict user preferences more accurately than a baseline model that chooses the translation with the fewest errors overall. Additionally we analyze the effect of using a crowd-sourced respondent population versus a sample of domain experts and observe that main preference effects are remarkably stable across the two samples. PMID:24683295

Linking models and data on vegetation structure

NASA Astrophysics Data System (ADS)

Hurtt, G. C.; Fisk, J.; Thomas, R. Q.; Dubayah, R.; Moorcroft, P. R.; Shugart, H. H.

2010-06-01

For more than a century, scientists have recognized the importance of vegetation structure in understanding forest dynamics. Now future satellite missions such as Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI) hold the potential to provide unprecedented global data on vegetation structure needed to reduce uncertainties in terrestrial carbon dynamics. Here, we briefly review the uses of data on vegetation structure in ecosystem models, develop and analyze theoretical models to quantify model-data requirements, and describe recent progress using a mechanistic modeling approach utilizing a formal scaling method and data on vegetation structure to improve model predictions. Generally, both limited sampling and coarse resolution averaging lead to model initialization error, which in turn is propagated in subsequent model prediction uncertainty and error. In cases with representative sampling, sufficient resolution, and linear dynamics, errors in initialization tend to compensate at larger spatial scales. However, with inadequate sampling, overly coarse resolution data or models, and nonlinear dynamics, errors in initialization lead to prediction error. A robust model-data framework will require both models and data on vegetation structure sufficient to resolve important environmental gradients and tree-level heterogeneity in forest structure globally.

Cost–Effective Prediction of Gender-Labeling Errors and Estimation of Gender-Labeling Error Rates in Candidate-Gene Association Studies

PubMed Central

Qu, Conghui; Schuetz, Johanna M.; Min, Jeong Eun; Leach, Stephen; Daley, Denise; Spinelli, John J.; Brooks-Wilson, Angela; Graham, Jinko

2011-01-01

We describe a statistical approach to predict gender-labeling errors in candidate-gene association studies, when Y-chromosome markers have not been included in the genotyping set. The approach adds value to methods that consider only the heterozygosity of X-chromosome SNPs, by incorporating available information about the intensity of X-chromosome SNPs in candidate genes relative to autosomal SNPs from the same individual. To our knowledge, no published methods formalize a framework in which heterozygosity and relative intensity are simultaneously taken into account. Our method offers the advantage that, in the genotyping set, no additional space is required beyond that already assigned to X-chromosome SNPs in the candidate genes. We also show how the predictions can be used in a two-phase sampling design to estimate the gender-labeling error rates for an entire study, at a fraction of the cost of a conventional design. PMID:22303327

The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, Nikki C.; Errico, Ronald M.

2013-01-01

A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models

PubMed Central

Wong, Stephen; Gardner, Andrew B.; Krieger, Abba M.; Litt, Brian

2007-01-01

Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they deliver therapy before seizure onset. Despite years of effort, prospective seizure prediction, which could improve device performance, remains elusive. In large part, this is explained by lack of agreement on a statistical framework for modeling seizure generation and a method for validating algorithm performance. We present a novel stochastic framework based on a three-state hidden Markov model (HMM) (representing interictal, preictal, and seizure states) with the feature that periods of increased seizure probability can transition back to the interictal state. This notion reflects clinical experience and may enhance interpretation of published seizure prediction studies. Our model accommodates clipped EEG segments and formalizes intuitive notions regarding statistical validation. We derive equations for type I and type II errors as a function of the number of seizures, duration of interictal data, and prediction horizon length and we demonstrate the model’s utility with a novel seizure detection algorithm that appeared to predicted seizure onset. We propose this framework as a vital tool for designing and validating prediction algorithms and for facilitating collaborative research in this area. PMID:17021032

The P-chain: relating sentence production and its disorders to comprehension and acquisition

PubMed Central

Dell, Gary S.; Chang, Franklin

2014-01-01

This article introduces the P-chain, an emerging framework for theory in psycholinguistics that unifies research on comprehension, production and acquisition. The framework proposes that language processing involves incremental prediction, which is carried out by the production system. Prediction necessarily leads to prediction error, which drives learning, including both adaptive adjustment to the mature language processing system as well as language acquisition. To illustrate the P-chain, we review the Dual-path model of sentence production, a connectionist model that explains structural priming in production and a number of facts about language acquisition. The potential of this and related models for explaining acquired and developmental disorders of sentence production is discussed. PMID:24324238

The P-chain: relating sentence production and its disorders to comprehension and acquisition.

PubMed

Dell, Gary S; Chang, Franklin

2014-01-01

This article introduces the P-chain, an emerging framework for theory in psycholinguistics that unifies research on comprehension, production and acquisition. The framework proposes that language processing involves incremental prediction, which is carried out by the production system. Prediction necessarily leads to prediction error, which drives learning, including both adaptive adjustment to the mature language processing system as well as language acquisition. To illustrate the P-chain, we review the Dual-path model of sentence production, a connectionist model that explains structural priming in production and a number of facts about language acquisition. The potential of this and related models for explaining acquired and developmental disorders of sentence production is discussed.

The fate of memory: Reconsolidation and the case of Prediction Error.

PubMed

Fernández, Rodrigo S; Boccia, Mariano M; Pedreira, María E

2016-09-01

The ability to make predictions based on stored information is a general coding strategy. A Prediction-Error (PE) is a mismatch between expected and current events. It was proposed as the process by which memories are acquired. But, our memories like ourselves are subject to change. Thus, an acquired memory can become active and update its content or strength by a labilization-reconsolidation process. Within the reconsolidation framework, PE drives the updating of consolidated memories. Moreover, memory features, such as strength and age, are crucial boundary conditions that limit the initiation of the reconsolidation process. In order to disentangle these boundary conditions, we review the role of surprise, classical models of conditioning, and their neural correlates. Several forms of PE were found to be capable of inducing memory labilization-reconsolidation. Notably, many of the PE findings mirror those of memory-reconsolidation, suggesting a strong link between these signals and memory process. Altogether, the aim of the present work is to integrate a psychological and neuroscientific analysis of PE into a general framework for memory-reconsolidation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Seeing the Errors You Feel Enhances Locomotor Performance but Not Learning.

PubMed

Roemmich, Ryan T; Long, Andrew W; Bastian, Amy J

2016-10-24

In human motor learning, it is thought that the more information we have about our errors, the faster we learn. Here, we show that additional error information can lead to improved motor performance without any concomitant improvement in learning. We studied split-belt treadmill walking that drives people to learn a new gait pattern using sensory prediction errors detected by proprioceptive feedback. When we also provided visual error feedback, participants acquired the new walking pattern far more rapidly and showed accelerated restoration of the normal walking pattern during washout. However, when the visual error feedback was removed during either learning or washout, errors reappeared with performance immediately returning to the level expected based on proprioceptive learning alone. These findings support a model with two mechanisms: a dual-rate adaptation process that learns invariantly from sensory prediction error detected by proprioception and a visual-feedback-dependent process that monitors learning and corrects residual errors but shows no learning itself. We show that our voluntary correction model accurately predicted behavior in multiple situations where visual feedback was used to change acquisition of new walking patterns while the underlying learning was unaffected. The computational and behavioral framework proposed here suggests that parallel learning and error correction systems allow us to rapidly satisfy task demands without necessarily committing to learning, as the relative permanence of learning may be inappropriate or inefficient when facing environments that are liable to change. Copyright © 2016 Elsevier Ltd. All rights reserved.

Threat and error management for anesthesiologists: a predictive risk taxonomy

PubMed Central

Ruskin, Keith J.; Stiegler, Marjorie P.; Park, Kellie; Guffey, Patrick; Kurup, Viji; Chidester, Thomas

2015-01-01

Purpose of review Patient care in the operating room is a dynamic interaction that requires cooperation among team members and reliance upon sophisticated technology. Most human factors research in medicine has been focused on analyzing errors and implementing system-wide changes to prevent them from recurring. We describe a set of techniques that has been used successfully by the aviation industry to analyze errors and adverse events and explain how these techniques can be applied to patient care. Recent findings Threat and error management (TEM) describes adverse events in terms of risks or challenges that are present in an operational environment (threats) and the actions of specific personnel that potentiate or exacerbate those threats (errors). TEM is a technique widely used in aviation, and can be adapted for the use in a medical setting to predict high-risk situations and prevent errors in the perioperative period. A threat taxonomy is a novel way of classifying and predicting the hazards that can occur in the operating room. TEM can be used to identify error-producing situations, analyze adverse events, and design training scenarios. Summary TEM offers a multifaceted strategy for identifying hazards, reducing errors, and training physicians. A threat taxonomy may improve analysis of critical events with subsequent development of specific interventions, and may also serve as a framework for training programs in risk mitigation. PMID:24113268

Applications of integrated human error identification techniques on the chemical cylinder change task.

PubMed

Cheng, Ching-Min; Hwang, Sheue-Ling

2015-03-01

This paper outlines the human error identification (HEI) techniques that currently exist to assess latent human errors. Many formal error identification techniques have existed for years, but few have been validated to cover latent human error analysis in different domains. This study considers many possible error modes and influential factors, including external error modes, internal error modes, psychological error mechanisms, and performance shaping factors, and integrates several execution procedures and frameworks of HEI techniques. The case study in this research was the operational process of changing chemical cylinders in a factory. In addition, the integrated HEI method was used to assess the operational processes and the system's reliability. It was concluded that the integrated method is a valuable aid to develop much safer operational processes and can be used to predict human error rates on critical tasks in the plant. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.

On the joys of perceiving: Affect as feedback for perceptual predictions.

PubMed

Chetverikov, Andrey; Kristjánsson, Árni

2016-09-01

How we perceive, attend to, or remember the stimuli in our environment depends on our preferences for them. Here we argue that this dependence is reciprocal: pleasures and displeasures are heavily dependent on cognitive processing, namely, on our ability to predict the world correctly. We propose that prediction errors, inversely weighted with prior probabilities of predictions, yield subjective experiences of positive or negative affect. In this way, we link affect to predictions within a predictive coding framework. We discuss how three key factors - uncertainty, expectations, and conflict - influence prediction accuracy and show how they shape our affective response. We demonstrate that predictable stimuli are, in general, preferred to unpredictable ones, though too much predictability may decrease this liking effect. Furthermore, the account successfully overcomes the "dark-room" problem, explaining why we do not avoid stimulation to minimize prediction error. We further discuss the implications of our approach for art perception and the utility of affect as feedback for predictions within a prediction-testing architecture of cognition. Copyright © 2016 Elsevier B.V. All rights reserved.

Attention and Conscious Perception in the Hypothesis Testing Brain

PubMed Central

Hohwy, Jakob

2012-01-01

Conscious perception and attention are difficult to study, partly because their relation to each other is not fully understood. Rather than conceiving and studying them in isolation from each other it may be useful to locate them in an independently motivated, general framework, from which a principled account of how they relate can then emerge. Accordingly, these mental phenomena are here reviewed through the prism of the increasingly influential predictive coding framework. On this framework, conscious perception can be seen as the upshot of prediction error minimization and attention as the optimization of precision expectations during such perceptual inference. This approach maps on well to a range of standard characteristics of conscious perception and attention, and can be used to interpret a range of empirical findings on their relation to each other. PMID:22485102

Evaluating Snow Data Assimilation Framework for Streamflow Forecasting Applications Using Hindcast Verification

NASA Astrophysics Data System (ADS)

Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

2012-12-01

Snow water equivalent (SWE) estimation is a key factor in producing reliable streamflow simulations and forecasts in snow dominated areas. However, measuring or predicting SWE has significant uncertainty. Sequential data assimilation, which updates states using both observed and modeled data based on error estimation, has been shown to reduce streamflow simulation errors but has had limited testing for forecasting applications. In the current study, a snow data assimilation framework integrated with the National Weather System River Forecasting System (NWSRFS) is evaluated for use in ensemble streamflow prediction (ESP). Seasonal water supply ESP hindcasts are generated for the North Fork of the American River Basin (NFARB) in northern California. Parameter sets from the California Nevada River Forecast Center (CNRFC), the Differential Evolution Adaptive Metropolis (DREAM) algorithm and the Multistep Automated Calibration Scheme (MACS) are tested both with and without sequential data assimilation. The traditional ESP method considers uncertainty in future climate conditions using historical temperature and precipitation time series to generate future streamflow scenarios conditioned on the current basin state. We include data uncertainty analysis in the forecasting framework through the DREAM-based parameter set which is part of a recently developed Integrated Uncertainty and Ensemble-based data Assimilation framework (ICEA). Extensive verification of all tested approaches is undertaken using traditional forecast verification measures, including root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), volumetric bias, joint distribution, rank probability score (RPS), and discrimination and reliability plots. In comparison to the RFC parameters, the DREAM and MACS sets show significant improvement in volumetric bias in flow. Use of assimilation improves hindcasts of higher flows but does not significantly improve performance in the mid flow and low flow categories.

Chemical library subset selection algorithms: a unified derivation using spatial statistics.

PubMed

Hamprecht, Fred A; Thiel, Walter; van Gunsteren, Wilfred F

2002-01-01

If similar compounds have similar activity, rational subset selection becomes superior to random selection in screening for pharmacological lead discovery programs. Traditional approaches to this experimental design problem fall into two classes: (i) a linear or quadratic response function is assumed (ii) some space filling criterion is optimized. The assumptions underlying the first approach are clear but not always defendable; the second approach yields more intuitive designs but lacks a clear theoretical foundation. We model activity in a bioassay as realization of a stochastic process and use the best linear unbiased estimator to construct spatial sampling designs that optimize the integrated mean square prediction error, the maximum mean square prediction error, or the entropy. We argue that our approach constitutes a unifying framework encompassing most proposed techniques as limiting cases and sheds light on their underlying assumptions. In particular, vector quantization is obtained, in dimensions up to eight, in the limiting case of very smooth response surfaces for the integrated mean square error criterion. Closest packing is obtained for very rough surfaces under the integrated mean square error and entropy criteria. We suggest to use either the integrated mean square prediction error or the entropy as optimization criteria rather than approximations thereof and propose a scheme for direct iterative minimization of the integrated mean square prediction error. Finally, we discuss how the quality of chemical descriptors manifests itself and clarify the assumptions underlying the selection of diverse or representative subsets.

Combining Traditional Cyber Security Audit Data with Psychosocial Data: Towards Predictive Modeling for Insider Threat Mitigation

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

Greitzer, Frank L.; Frincke, Deborah A.

2010-09-01

The purpose of this chapter is to motivate the combination of traditional cyber security audit data with psychosocial data, so as to move from an insider threat detection stance to one that enables prediction of potential insider presence. Two distinctive aspects of the approach are the objective of predicting or anticipating potential risks and the use of organizational data in addition to cyber data to support the analysis. The chapter describes the challenges of this endeavor and progress in defining a usable set of predictive indicators, developing a framework for integrating the analysis of organizational and cyber security data tomore » yield predictions about possible insider exploits, and developing the knowledge base and reasoning capability of the system. We also outline the types of errors that one expects in a predictive system versus a detection system and discuss how those errors can affect the usefulness of the results.« less

Printer model for dot-on-dot halftone screens

NASA Astrophysics Data System (ADS)

Balasubramanian, Raja

1995-04-01

A printer model is described for dot-on-dot halftone screens. For a given input CMYK signal, the model predicts the resulting spectral reflectance of the printed patch. The model is derived in two steps. First, the C, M, Y, K dot growth functions are determined which relate the input digital value to the actual dot area coverages of the colorants. Next, the reflectance of a patch is predicted as a weighted combination of the reflectances of the four solid C, M, Y, K patches and their various overlays. This approach is analogous to the Neugebauer model, with the random mixing equations being replaced by dot-on-dot mixing equations. A Yule-Neilsen correction factor is incorporated to account for light scattering within the paper. The dot area functions and Yule-Neilsen parameter are chosen to optimize the fit to a set of training data. The model is also extended to a cellular framework, requiring additional measurements. The model is tested with a four color xerographic printer employing a line-on-line halftone screen. CIE L*a*b* errors are obtained between measurements and model predictions. The Yule-Neilsen factor significantly decreases the model error. Accuracy is also increased with the use of a cellular framework.

Bayesian energy landscape tilting: towards concordant models of molecular ensembles.

PubMed

Beauchamp, Kyle A; Pande, Vijay S; Das, Rhiju

2014-03-18

Predicting biological structure has remained challenging for systems such as disordered proteins that take on myriad conformations. Hybrid simulation/experiment strategies have been undermined by difficulties in evaluating errors from computational model inaccuracies and data uncertainties. Building on recent proposals from maximum entropy theory and nonequilibrium thermodynamics, we address these issues through a Bayesian energy landscape tilting (BELT) scheme for computing Bayesian hyperensembles over conformational ensembles. BELT uses Markov chain Monte Carlo to directly sample maximum-entropy conformational ensembles consistent with a set of input experimental observables. To test this framework, we apply BELT to model trialanine, starting from disagreeing simulations with the force fields ff96, ff99, ff99sbnmr-ildn, CHARMM27, and OPLS-AA. BELT incorporation of limited chemical shift and (3)J measurements gives convergent values of the peptide's α, β, and PPII conformational populations in all cases. As a test of predictive power, all five BELT hyperensembles recover set-aside measurements not used in the fitting and report accurate errors, even when starting from highly inaccurate simulations. BELT's principled framework thus enables practical predictions for complex biomolecular systems from discordant simulations and sparse data. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Predictability Experiments With the Navy Operational Global Atmospheric Prediction System

NASA Astrophysics Data System (ADS)

Reynolds, C. A.; Gelaro, R.; Rosmond, T. E.

2003-12-01

There are several areas of research in numerical weather prediction and atmospheric predictability, such as targeted observations and ensemble perturbation generation, where it is desirable to combine information about the uncertainty of the initial state with information about potential rapid perturbation growth. Singular vectors (SVs) provide a framework to accomplish this task in a mathematically rigorous and computationally feasible manner. In this study, SVs are calculated using the tangent and adjoint models of the Navy Operational Global Atmospheric Prediction System (NOGAPS). The analysis error variance information produced by the NRL Atmospheric Variational Data Assimilation System is used as the initial-time SV norm. These VAR SVs are compared to SVs for which total energy is both the initial and final time norms (TE SVs). The incorporation of analysis error variance information has a significant impact on the structure and location of the SVs. This in turn has a significant impact on targeted observing applications. The utility and implications of such experiments in assessing the analysis error variance estimates will be explored. Computing support has been provided by the Department of Defense High Performance Computing Center at the Naval Oceanographic Office Major Shared Resource Center at Stennis, Mississippi.

Protein loop modeling using a new hybrid energy function and its application to modeling in inaccurate structural environments.

PubMed

Park, Hahnbeom; Lee, Gyu Rie; Heo, Lim; Seok, Chaok

2014-01-01

Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and de novo protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at http://galaxy.seoklab.org/loop with the PS2 option for the scoring function.

Real-time prediction and gating of respiratory motion using an extended Kalman filter and Gaussian process regression

NASA Astrophysics Data System (ADS)

Bukhari, W.; Hong, S.-M.

2015-01-01

Motion-adaptive radiotherapy aims to deliver a conformal dose to the target tumour with minimal normal tissue exposure by compensating for tumour motion in real time. The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting and gating respiratory motion that utilizes a model-based and a model-free Bayesian framework by combining them in a cascade structure. The algorithm, named EKF-GPR+, implements a gating function without pre-specifying a particular region of the patient’s breathing cycle. The algorithm first employs an extended Kalman filter (LCM-EKF) to predict the respiratory motion and then uses a model-free Gaussian process regression (GPR) to correct the error of the LCM-EKF prediction. The GPR is a non-parametric Bayesian algorithm that yields predictive variance under Gaussian assumptions. The EKF-GPR+ algorithm utilizes the predictive variance from the GPR component to capture the uncertainty in the LCM-EKF prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification allows us to pause the treatment beam over such instances. EKF-GPR+ implements the gating function by using simple calculations based on the predictive variance with no additional detection mechanism. A sparse approximation of the GPR algorithm is employed to realize EKF-GPR+ in real time. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPR+. The experimental results show that the EKF-GPR+ algorithm effectively reduces the prediction error in a root-mean-square (RMS) sense by employing the gating function, albeit at the cost of a reduced duty cycle. As an example, EKF-GPR+ reduces the patient-wise RMS error to 37%, 39% and 42% in percent ratios relative to no prediction for a duty cycle of 80% at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The experiments also confirm that EKF-GPR+ controls the duty cycle with reasonable accuracy.

Human operator response to error-likely situations in complex engineering systems

NASA Technical Reports Server (NTRS)

Morris, Nancy M.; Rouse, William B.

1988-01-01

The causes of human error in complex systems are examined. First, a conceptual framework is provided in which two broad categories of error are discussed: errors of action, or slips, and errors of intention, or mistakes. Conditions in which slips and mistakes might be expected to occur are identified, based on existing theories of human error. Regarding the role of workload, it is hypothesized that workload may act as a catalyst for error. Two experiments are presented in which humans' response to error-likely situations were examined. Subjects controlled PLANT under a variety of conditions and periodically provided subjective ratings of mental effort. A complex pattern of results was obtained, which was not consistent with predictions. Generally, the results of this research indicate that: (1) humans respond to conditions in which errors might be expected by attempting to reduce the possibility of error, and (2) adaptation to conditions is a potent influence on human behavior in discretionary situations. Subjects' explanations for changes in effort ratings are also explored.

Ensemble Kalman filters for dynamical systems with unresolved turbulence

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

Grooms, Ian, E-mail: grooms@cims.nyu.edu; Lee, Yoonsang; Majda, Andrew J.

Ensemble Kalman filters are developed for turbulent dynamical systems where the forecast model does not resolve all the active scales of motion. Coarse-resolution models are intended to predict the large-scale part of the true dynamics, but observations invariably include contributions from both the resolved large scales and the unresolved small scales. The error due to the contribution of unresolved scales to the observations, called ‘representation’ or ‘representativeness’ error, is often included as part of the observation error, in addition to the raw measurement error, when estimating the large-scale part of the system. It is here shown how stochastic superparameterization (amore » multiscale method for subgridscale parameterization) can be used to provide estimates of the statistics of the unresolved scales. In addition, a new framework is developed wherein small-scale statistics can be used to estimate both the resolved and unresolved components of the solution. The one-dimensional test problem from dispersive wave turbulence used here is computationally tractable yet is particularly difficult for filtering because of the non-Gaussian extreme event statistics and substantial small scale turbulence: a shallow energy spectrum proportional to k{sup −5/6} (where k is the wavenumber) results in two-thirds of the climatological variance being carried by the unresolved small scales. Because the unresolved scales contain so much energy, filters that ignore the representation error fail utterly to provide meaningful estimates of the system state. Inclusion of a time-independent climatological estimate of the representation error in a standard framework leads to inaccurate estimates of the large-scale part of the signal; accurate estimates of the large scales are only achieved by using stochastic superparameterization to provide evolving, large-scale dependent predictions of the small-scale statistics. Again, because the unresolved scales contain so much energy, even an accurate estimate of the large-scale part of the system does not provide an accurate estimate of the true state. By providing simultaneous estimates of both the large- and small-scale parts of the solution, the new framework is able to provide accurate estimates of the true system state.« less

A Bayesian framework for infrasound location

NASA Astrophysics Data System (ADS)

Modrak, Ryan T.; Arrowsmith, Stephen J.; Anderson, Dale N.

2010-04-01

We develop a framework for location of infrasound events using backazimuth and infrasonic arrival times from multiple arrays. Bayesian infrasonic source location (BISL) developed here estimates event location and associated credibility regions. BISL accounts for unknown source-to-array path or phase by formulating infrasonic group velocity as random. Differences between observed and predicted source-to-array traveltimes are partitioned into two additive Gaussian sources, measurement error and model error, the second of which accounts for the unknown influence of wind and temperature on path. By applying the technique to both synthetic tests and ground-truth events, we highlight the complementary nature of back azimuths and arrival times for estimating well-constrained event locations. BISL is an extension to methods developed earlier by Arrowsmith et al. that provided simple bounds on location using a grid-search technique.

An Overview of Atmospheric Composition OSSE Activities at NASA's Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

daSilva, Arlinda

2012-01-01

A model-based Observing System Simulation Experiment (OSSE) is a framework for numerical experimentation in which observables are simulated from fields generated by an earth system model, including a parameterized description of observational error characteristics. Simulated observations can be used for sampling studies, quantifying errors in analysis or retrieval algorithms, and ultimately being a planning tool for designing new observing missions. While this framework has traditionally been used to assess the impact of observations on numerical weather prediction, it has a much broader applicability, in particular to aerosols and chemical constituents. In this talk we will give a general overview of Observing System Simulation Experiments (OSSE) activities at NASA's Global Modeling and Assimilation Office, with focus on its emerging atmospheric composition component.

Model based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach

PubMed Central

Rasmussen, Peter M.; Smith, Amy F.; Sakadžić, Sava; Boas, David A.; Pries, Axel R.; Secomb, Timothy W.; Østergaard, Leif

2017-01-01

Objective In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors. Methods We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty. Results We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than five hundred unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration. Conclusion Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large datasets acquired as part of microvascular imaging studies. PMID:27987383

We favor formal models of heuristics rather than lists of loose dichotomies: a reply to Evans and Over

PubMed Central

Gigerenzer, Gerd

2009-01-01

In their comment on Marewski et al. (good judgments do not require complex cognition, 2009) Evans and Over (heuristic thinking and human intelligence: a commentary on Marewski, Gaissmaier and Gigerenzer, 2009) conjectured that heuristics can often lead to biases and are not error free. This is a most surprising critique. The computational models of heuristics we have tested allow for quantitative predictions of how many errors a given heuristic will make, and we and others have measured the amount of error by analysis, computer simulation, and experiment. This is clear progress over simply giving heuristics labels, such as availability, that do not allow for quantitative comparisons of errors. Evans and Over argue that the reason people rely on heuristics is the accuracy-effort trade-off. However, the comparison between heuristics and more effortful strategies, such as multiple regression, has shown that there are many situations in which a heuristic is more accurate with less effort. Finally, we do not see how the fast and frugal heuristics program could benefit from a dual-process framework unless the dual-process framework is made more precise. Instead, the dual-process framework could benefit if its two “black boxes” (Type 1 and Type 2 processes) were substituted by computational models of both heuristics and other processes. PMID:19784854

Benchmarking global land surface models in CMIP5: analysis of ecosystem water use efficiency (WUE) and Budyko framework

NASA Astrophysics Data System (ADS)

Li, Longhui

2015-04-01

Twelve Earth System Models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are evaluated in terms of ecosystem water use efficiency (WUE) and Budyko framework. Simulated values of GPP and ET from ESMs were validated against with FLUXNET measurements, and the slope of linear regression between the measurement and the model ranged from 0.24 in CanESM2 to 0.8 in GISS-E2 for GPP, and from 0.51 to 0.86 for ET. The performances of 12 ESMs in simulating ET are generally better than GPP. Compared with flux-tower-based estimates by Jung et al. [Journal of Geophysical Research 116 (2011) G00J07] (JU11), all ESMs could capture the latitudinal variations of GPP and ET, but the majority of models extremely overestimated GPP and ET, particularly around the equator. The 12 ESMs showed much larger variations in latitudinal WUE. 4 of 12 ESMs predicted global annual GPP of higher than 150 Pg C year-1, and the other 8 ESMs predicted global GPP with ±15% error of the JU11 GPP. In contrast, all EMSs predicted moderate bias for global ET. The coefficient of variation (CV) of ET (0.11) is significantly less than that of GPP (0.25). More than half of 12 ESMs generally comply with the Budyko framework but some models deviated much. Spatial analysis of error in GPP and ET indicated that model results largely differ among models at different regions. This study suggested that the estimate of ET was much better than GPP. Incorporating the convergence of WUE and the Budyko framework into ESMs as constraints in the next round of CMIP scheme is expected to decrease the uncertainties of carbon and water fluxes estimates.

Normalized Rotational Multiple Yield Surface Framework (NRMYSF) stress-strain curve prediction method based on small strain triaxial test data on undisturbed Auckland residual clay soils

NASA Astrophysics Data System (ADS)

Noor, M. J. Md; Ibrahim, A.; Rahman, A. S. A.

2018-04-01

Small strain triaxial test measurement is considered to be significantly accurate compared to the external strain measurement using conventional method due to systematic errors normally associated with the test. Three submersible miniature linear variable differential transducer (LVDT) mounted on yokes which clamped directly onto the soil sample at equally 120° from the others. The device setup using 0.4 N resolution load cell and 16 bit AD converter was capable of consistently resolving displacement of less than 1µm and measuring axial strains ranging from less than 0.001% to 2.5%. Further analysis of small strain local measurement data was performed using new Normalized Multiple Yield Surface Framework (NRMYSF) method and compared with existing Rotational Multiple Yield Surface Framework (RMYSF) prediction method. The prediction of shear strength based on combined intrinsic curvilinear shear strength envelope using small strain triaxial test data confirmed the significant improvement and reliability of the measurement and analysis methods. Moreover, the NRMYSF method shows an excellent data prediction and significant improvement toward more reliable prediction of soil strength that can reduce the cost and time of experimental laboratory test.

Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles

PubMed Central

2010-01-01

Changes to the glycosylation profile on HIV gp120 can influence viral pathogenesis and alter AIDS disease progression. The characterization of glycosylation differences at the sequence level is inadequate as the placement of carbohydrates is structurally complex. However, no structural framework is available to date for the study of HIV disease progression. In this study, we propose a novel machine-learning based framework for the prediction of AIDS disease progression in three stages (RP, SP, and LTNP) using the HIV structural gp120 profile. This new intelligent framework proves to be accurate and provides an important benchmark for predicting AIDS disease progression computationally. The model is trained using a novel HIV gp120 glycosylation structural profile to detect possible stages of AIDS disease progression for the target sequences of HIV+ individuals. The performance of the proposed model was compared to seven existing different machine-learning models on newly proposed gp120-Benchmark_1 dataset in terms of error-rate (MSE), accuracy (CCI), stability (STD), and complexity (TBM). The novel framework showed better predictive performance with 67.82% CCI, 30.21 MSE, 0.8 STD, and 2.62 TBM on the three stages of AIDS disease progression of 50 HIV+ individuals. This framework is an invaluable bioinformatics tool that will be useful to the clinical assessment of viral pathogenesis. PMID:21143806

The Mismeasure of Morals: Antisocial Personality Traits Predict Utilitarian Responses to Moral Dilemmas

ERIC Educational Resources Information Center

Bartels, Daniel M.; Pizarro, David A.

2011-01-01

Researchers have recently argued that utilitarianism is the appropriate framework by which to evaluate moral judgment, and that individuals who endorse non-utilitarian solutions to moral dilemmas (involving active vs. passive harm) are committing an error. We report a study in which participants responded to a battery of personality assessments…

Combining empirical approaches and error modelling to enhance predictive uncertainty estimation in extrapolation for operational flood forecasting. Tests on flood events on the Loire basin, France.

NASA Astrophysics Data System (ADS)

Berthet, Lionel; Marty, Renaud; Bourgin, François; Viatgé, Julie; Piotte, Olivier; Perrin, Charles

2017-04-01

An increasing number of operational flood forecasting centres assess the predictive uncertainty associated with their forecasts and communicate it to the end users. This information can match the end-users needs (i.e. prove to be useful for an efficient crisis management) only if it is reliable: reliability is therefore a key quality for operational flood forecasts. In 2015, the French flood forecasting national and regional services (Vigicrues network; www.vigicrues.gouv.fr) implemented a framework to compute quantitative discharge and water level forecasts and to assess the predictive uncertainty. Among the possible technical options to achieve this goal, a statistical analysis of past forecasting errors of deterministic models has been selected (QUOIQUE method, Bourgin, 2014). It is a data-based and non-parametric approach based on as few assumptions as possible about the forecasting error mathematical structure. In particular, a very simple assumption is made regarding the predictive uncertainty distributions for large events outside the range of the calibration data: the multiplicative error distribution is assumed to be constant, whatever the magnitude of the flood. Indeed, the predictive distributions may not be reliable in extrapolation. However, estimating the predictive uncertainty for these rare events is crucial when major floods are of concern. In order to improve the forecasts reliability for major floods, an attempt at combining the operational strength of the empirical statistical analysis and a simple error modelling is done. Since the heteroscedasticity of forecast errors can considerably weaken the predictive reliability for large floods, this error modelling is based on the log-sinh transformation which proved to reduce significantly the heteroscedasticity of the transformed error in a simulation context, even for flood peaks (Wang et al., 2012). Exploratory tests on some operational forecasts issued during the recent floods experienced in France (major spring floods in June 2016 on the Loire river tributaries and flash floods in fall 2016) will be shown and discussed. References Bourgin, F. (2014). How to assess the predictive uncertainty in hydrological modelling? An exploratory work on a large sample of watersheds, AgroParisTech Wang, Q. J., Shrestha, D. L., Robertson, D. E. and Pokhrel, P (2012). A log-sinh transformation for data normalization and variance stabilization. Water Resources Research, , W05514, doi:10.1029/2011WR010973

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

Zhang, Yao; Balaprakash, Prasanna; Meng, Jiayuan

We present Raexplore, a performance modeling framework for architecture exploration. Raexplore enables rapid, automated, and systematic search of architecture design space by combining hardware counter-based performance characterization and analytical performance modeling. We demonstrate Raexplore for two recent manycore processors IBM Blue- Gene/Q compute chip and Intel Xeon Phi, targeting a set of scientific applications. Our framework is able to capture complex interactions between architectural components including instruction pipeline, cache, and memory, and to achieve a 3–22% error for same-architecture and cross-architecture performance predictions. Furthermore, we apply our framework to assess the two processors, and discover and evaluate a list ofmore » architectural scaling options for future processor designs.« less

Brain mechanisms in religion and spirituality: An integrative predictive processing framework.

PubMed

van Elk, Michiel; Aleman, André

2017-02-01

We present the theory of predictive processing as a unifying framework to account for the neurocognitive basis of religion and spirituality. Our model is substantiated by discussing four different brain mechanisms that play a key role in religion and spirituality: temporal brain areas are associated with religious visions and ecstatic experiences; multisensory brain areas and the default mode network are involved in self-transcendent experiences; the Theory of Mind-network is associated with prayer experiences and over attribution of intentionality; top-down mechanisms instantiated in the anterior cingulate cortex and the medial prefrontal cortex could be involved in acquiring and maintaining intuitive supernatural beliefs. We compare the predictive processing model with two-systems accounts of religion and spirituality, by highlighting the central role of prediction error monitoring. We conclude by presenting novel predictions for future research and by discussing the philosophical and theological implications of neuroscientific research on religion and spirituality. Copyright © 2016 Elsevier Ltd. All rights reserved.

An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning

PubMed Central

Balasubramani, Pragathi P.; Chakravarthy, V. Srinivasa; Ravindran, Balaraman; Moustafa, Ahmed A.

2014-01-01

Although empirical and neural studies show that serotonin (5HT) plays many functional roles in the brain, prior computational models mostly focus on its role in behavioral inhibition. In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL)-framework. The model depicts the roles of dopamine (DA) and serotonin (5HT) in Basal Ganglia (BG). In this model, the DA signal is represented by the temporal difference error (δ), while the 5HT signal is represented by a parameter (α) that controls risk prediction error. This formulation that accommodates both 5HT and DA reconciles some of the diverse roles of 5HT particularly in connection with the BG system. We apply the model to different experimental paradigms used to study the role of 5HT: (1) Risk-sensitive decision making, where 5HT controls risk assessment, (2) Temporal reward prediction, where 5HT controls time-scale of reward prediction, and (3) Reward/Punishment sensitivity, in which the punishment prediction error depends on 5HT levels. Thus the proposed integrated RL model reconciles several existing theories of 5HT and DA in the BG. PMID:24795614

Impact of Data Assimilation on Cost-Accuracy Tradeoff in Multi-Fidelity Models at the Example of an Infiltration Problem

NASA Astrophysics Data System (ADS)

Sinsbeck, Michael; Tartakovsky, Daniel

2015-04-01

Infiltration into top soil can be described by alternative models with different degrees of fidelity: Richards equation and the Green-Ampt model. These models typically contain uncertain parameters and forcings, rendering predictions of the state variables uncertain as well. Within the probabilistic framework, solutions of these models are given in terms of their probability density functions (PDFs) that, in the presence of data, can be treated as prior distributions. The assimilation of soil moisture data into model predictions, e.g., via a Bayesian updating of solution PDFs, poses a question of model selection: Given a significant difference in computational cost, is a lower-fidelity model preferable to its higher-fidelity counter-part? We investigate this question in the context of heterogeneous porous media, whose hydraulic properties are uncertain. While low-fidelity (reduced-complexity) models introduce a model error, their moderate computational cost makes it possible to generate more realizations, which reduces the (e.g., Monte Carlo) sampling or stochastic error. The ratio between these two errors determines the model with the smallest total error. We found assimilation of measurements of a quantity of interest (the soil moisture content, in our example) to decrease the model error, increasing the probability that the predictive accuracy of a reduced-complexity model does not fall below that of its higher-fidelity counterpart.

Model identification using stochastic differential equation grey-box models in diabetes.

PubMed

Duun-Henriksen, Anne Katrine; Schmidt, Signe; Røge, Rikke Meldgaard; Møller, Jonas Bech; Nørgaard, Kirsten; Jørgensen, John Bagterp; Madsen, Henrik

2013-03-01

The acceptance of virtual preclinical testing of control algorithms is growing and thus also the need for robust and reliable models. Models based on ordinary differential equations (ODEs) can rarely be validated with standard statistical tools. Stochastic differential equations (SDEs) offer the possibility of building models that can be validated statistically and that are capable of predicting not only a realistic trajectory, but also the uncertainty of the prediction. In an SDE, the prediction error is split into two noise terms. This separation ensures that the errors are uncorrelated and provides the possibility to pinpoint model deficiencies. An identifiable model of the glucoregulatory system in a type 1 diabetes mellitus (T1DM) patient is used as the basis for development of a stochastic-differential-equation-based grey-box model (SDE-GB). The parameters are estimated on clinical data from four T1DM patients. The optimal SDE-GB is determined from likelihood-ratio tests. Finally, parameter tracking is used to track the variation in the "time to peak of meal response" parameter. We found that the transformation of the ODE model into an SDE-GB resulted in a significant improvement in the prediction and uncorrelated errors. Tracking of the "peak time of meal absorption" parameter showed that the absorption rate varied according to meal type. This study shows the potential of using SDE-GBs in diabetes modeling. Improved model predictions were obtained due to the separation of the prediction error. SDE-GBs offer a solid framework for using statistical tools for model validation and model development. © 2013 Diabetes Technology Society.

Curiosity and reward: Valence predicts choice and information prediction errors enhance learning.

PubMed

Marvin, Caroline B; Shohamy, Daphna

2016-03-01

Curiosity drives many of our daily pursuits and interactions; yet, we know surprisingly little about how it works. Here, we harness an idea implied in many conceptualizations of curiosity: that information has value in and of itself. Reframing curiosity as the motivation to obtain reward-where the reward is information-allows one to leverage major advances in theoretical and computational mechanisms of reward-motivated learning. We provide new evidence supporting 2 predictions that emerge from this framework. First, we find an asymmetric effect of positive versus negative information, with positive information enhancing both curiosity and long-term memory for information. Second, we find that it is not the absolute value of information that drives learning but, rather, the gap between the reward expected and reward received, an "information prediction error." These results support the idea that information functions as a reward, much like money or food, guiding choices and driving learning in systematic ways. (c) 2016 APA, all rights reserved).

Shape Optimization by Bayesian-Validated Computer-Simulation Surrogates

NASA Technical Reports Server (NTRS)

Patera, Anthony T.

1997-01-01

A nonparametric-validated, surrogate approach to optimization has been applied to the computational optimization of eddy-promoter heat exchangers and to the experimental optimization of a multielement airfoil. In addition to the baseline surrogate framework, a surrogate-Pareto framework has been applied to the two-criteria, eddy-promoter design problem. The Pareto analysis improves the predictability of the surrogate results, preserves generality, and provides a means to rapidly determine design trade-offs. Significant contributions have been made in the geometric description used for the eddy-promoter inclusions as well as to the surrogate framework itself. A level-set based, geometric description has been developed to define the shape of the eddy-promoter inclusions. The level-set technique allows for topology changes (from single-body,eddy-promoter configurations to two-body configurations) without requiring any additional logic. The continuity of the output responses for input variations that cross the boundary between topologies has been demonstrated. Input-output continuity is required for the straightforward application of surrogate techniques in which simplified, interpolative models are fitted through a construction set of data. The surrogate framework developed previously has been extended in a number of ways. First, the formulation for a general, two-output, two-performance metric problem is presented. Surrogates are constructed and validated for the outputs. The performance metrics can be functions of both outputs, as well as explicitly of the inputs, and serve to characterize the design preferences. By segregating the outputs and the performance metrics, an additional level of flexibility is provided to the designer. The validated outputs can be used in future design studies and the error estimates provided by the output validation step still apply, and require no additional appeals to the expensive analysis. Second, a candidate-based a posteriori error analysis capability has been developed which provides probabilistic error estimates on the true performance for a design randomly selected near the surrogate-predicted optimal design.

Uncertainty estimation of predictions of peptides' chromatographic retention times in shotgun proteomics.

PubMed

Maboudi Afkham, Heydar; Qiu, Xuanbin; The, Matthew; Käll, Lukas

2017-02-15

Liquid chromatography is frequently used as a means to reduce the complexity of peptide-mixtures in shotgun proteomics. For such systems, the time when a peptide is released from a chromatography column and registered in the mass spectrometer is referred to as the peptide's retention time . Using heuristics or machine learning techniques, previous studies have demonstrated that it is possible to predict the retention time of a peptide from its amino acid sequence. In this paper, we are applying Gaussian Process Regression to the feature representation of a previously described predictor E lude . Using this framework, we demonstrate that it is possible to estimate the uncertainty of the prediction made by the model. Here we show how this uncertainty relates to the actual error of the prediction. In our experiments, we observe a strong correlation between the estimated uncertainty provided by Gaussian Process Regression and the actual prediction error. This relation provides us with new means for assessment of the predictions. We demonstrate how a subset of the peptides can be selected with lower prediction error compared to the whole set. We also demonstrate how such predicted standard deviations can be used for designing adaptive windowing strategies. lukas.kall@scilifelab.se. Our software and the data used in our experiments is publicly available and can be downloaded from https://github.com/statisticalbiotechnology/GPTime . © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Estimating Prediction Uncertainty from Geographical Information System Raster Processing: A User's Manual for the Raster Error Propagation Tool (REPTool)

USGS Publications Warehouse

Gurdak, Jason J.; Qi, Sharon L.; Geisler, Michael L.

2009-01-01

The U.S. Geological Survey Raster Error Propagation Tool (REPTool) is a custom tool for use with the Environmental System Research Institute (ESRI) ArcGIS Desktop application to estimate error propagation and prediction uncertainty in raster processing operations and geospatial modeling. REPTool is designed to introduce concepts of error and uncertainty in geospatial data and modeling and provide users of ArcGIS Desktop a geoprocessing tool and methodology to consider how error affects geospatial model output. Similar to other geoprocessing tools available in ArcGIS Desktop, REPTool can be run from a dialog window, from the ArcMap command line, or from a Python script. REPTool consists of public-domain, Python-based packages that implement Latin Hypercube Sampling within a probabilistic framework to track error propagation in geospatial models and quantitatively estimate the uncertainty of the model output. Users may specify error for each input raster or model coefficient represented in the geospatial model. The error for the input rasters may be specified as either spatially invariant or spatially variable across the spatial domain. Users may specify model output as a distribution of uncertainty for each raster cell. REPTool uses the Relative Variance Contribution method to quantify the relative error contribution from the two primary components in the geospatial model - errors in the model input data and coefficients of the model variables. REPTool is appropriate for many types of geospatial processing operations, modeling applications, and related research questions, including applications that consider spatially invariant or spatially variable error in geospatial data.

Visual anticipation biases conscious decision making but not bottom-up visual processing.

PubMed

Mathews, Zenon; Cetnarski, Ryszard; Verschure, Paul F M J

2014-01-01

Prediction plays a key role in control of attention but it is not clear which aspects of prediction are most prominent in conscious experience. An evolving view on the brain is that it can be seen as a prediction machine that optimizes its ability to predict states of the world and the self through the top-down propagation of predictions and the bottom-up presentation of prediction errors. There are competing views though on whether prediction or prediction errors dominate the formation of conscious experience. Yet, the dynamic effects of prediction on perception, decision making and consciousness have been difficult to assess and to model. We propose a novel mathematical framework and a psychophysical paradigm that allows us to assess both the hierarchical structuring of perceptual consciousness, its content and the impact of predictions and/or errors on conscious experience, attention and decision-making. Using a displacement detection task combined with reverse correlation, we reveal signatures of the usage of prediction at three different levels of perceptual processing: bottom-up fast saccades, top-down driven slow saccades and consciousnes decisions. Our results suggest that the brain employs multiple parallel mechanism at different levels of perceptual processing in order to shape effective sensory consciousness within a predicted perceptual scene. We further observe that bottom-up sensory and top-down predictive processes can be dissociated through cognitive load. We propose a probabilistic data association model from dynamical systems theory to model the predictive multi-scale bias in perceptual processing that we observe and its role in the formation of conscious experience. We propose that these results support the hypothesis that consciousness provides a time-delayed description of a task that is used to prospectively optimize real time control structures, rather than being engaged in the real-time control of behavior itself.

Error estimation for CFD aeroheating prediction under rarefied flow condition

NASA Astrophysics Data System (ADS)

Jiang, Yazhong; Gao, Zhenxun; Jiang, Chongwen; Lee, Chunhian

2014-12-01

Both direct simulation Monte Carlo (DSMC) and Computational Fluid Dynamics (CFD) methods have become widely used for aerodynamic prediction when reentry vehicles experience different flow regimes during flight. The implementation of slip boundary conditions in the traditional CFD method under Navier-Stokes-Fourier (NSF) framework can extend the validity of this approach further into transitional regime, with the benefit that much less computational cost is demanded compared to DSMC simulation. Correspondingly, an increasing error arises in aeroheating calculation as the flow becomes more rarefied. To estimate the relative error of heat flux when applying this method for a rarefied flow in transitional regime, theoretical derivation is conducted and a dimensionless parameter ɛ is proposed by approximately analyzing the ratio of the second order term to first order term in the heat flux expression in Burnett equation. DSMC simulation for hypersonic flow over a cylinder in transitional regime is performed to test the performance of parameter ɛ, compared with two other parameters, Knρ and MaṡKnρ.

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning

PubMed Central

Zhu, Lusha; Mathewson, Kyle E.; Hsu, Ming

2012-01-01

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents’ beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs. PMID:22307594

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning.

PubMed

Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming

2012-01-31

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

Possibilities: A framework for modeling students' deductive reasoning in physics

NASA Astrophysics Data System (ADS)

Gaffney, Jonathan David Housley

Students often make errors when trying to solve qualitative or conceptual physics problems, and while many successful instructional interventions have been generated to prevent such errors, the process of deduction that students use when solving physics problems has not been thoroughly studied. In an effort to better understand that reasoning process, I have developed a new framework, which is based on the mental models framework in psychology championed by P. N. Johnson-Laird. My new framework models how students search possibility space when thinking about conceptual physics problems and suggests that errors arise from failing to flesh out all possibilities. It further suggests that instructional interventions should focus on making apparent those possibilities, as well as all physical consequences those possibilities would incur. The possibilities framework emerged from the analysis of data from a unique research project specifically invented for the purpose of understanding how students use deductive reasoning. In the selection task, participants were given a physics problem along with three written possible solutions with the goal of identifying which one of the three possible solutions was correct. Each participant was also asked to identify the errors in the incorrect solutions. For the study presented in this dissertation, participants not only performed the selection task individually on four problems, but they were also placed into groups of two or three and asked to discuss with each other the reasoning they used in making their choices and attempt to reach a consensus about which solution was correct. Finally, those groups were asked to work together to perform the selection task on three new problems. The possibilities framework appropriately models the reasoning that students use, and it makes useful predictions about potentially helpful instructional interventions. The study reported in this dissertation emphasizes the useful insight the possibilities framework provides. For example, this framework allows us to detect subtle differences in students' reasoning errors, even when those errors result in the same final answer. It also illuminates how simply mentioning overlooked quantities can instigate new lines of student reasoning. It allows us to better understand how well-known psychological biases, such as the belief bias, affect the reasoning process by preventing reasoners from fleshing out all of the possibilities. The possibilities framework also allows us to track student discussions about physics, revealing the need for all parties in communication to use the same set of possibilities in the conversations to facilitate successful understanding. The framework also suggests some of the influences that affect how reasoners choose between possible solutions to a given problem. This new framework for understanding how students reason when solving conceptual physics problems opens the door to a significant field of research. The framework itself needs to be further tested and developed, but it provides substantial suggestions for instructional interventions. If we hope to improve student reasoning in physics, the possibilities framework suggests that we are perhaps best served by teaching students how to fully flesh out the possibilities in every situation. This implies that we need to ensure students have a deep understanding of all of the implied possibilities afforded by the fundamental principles that are the cornerstones of the models we teach in physics classes.

Active inference and cognitive-emotional interactions in the brain.

PubMed

Pezzulo, Giovanni; Barca, Laura; Friston, Karl J

2015-01-01

All organisms must integrate cognition, emotion, and motivation to guide action toward valuable (goal) states, as described by active inference. Within this framework, cognition, emotion, and motivation interact through the (Bayesian) fusion of exteroceptive, proprioceptive, and interoceptive signals, the precision-weighting of prediction errors, and the "affective tuning" of neuronal representations. Crucially, misregulation of these processes may have profound psychopathological consequences.

Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer.

PubMed

Castelli, Mauro; Trujillo, Leonardo; Vanneschi, Leonardo

2015-01-01

Energy consumption forecasting (ECF) is an important policy issue in today's economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.

On how to avoid input and structural uncertainties corrupt the inference of hydrological parameters using a Bayesian framework

NASA Astrophysics Data System (ADS)

Hernández, Mario R.; Francés, Félix

2015-04-01

One phase of the hydrological models implementation process, significantly contributing to the hydrological predictions uncertainty, is the calibration phase in which values of the unknown model parameters are tuned by optimizing an objective function. An unsuitable error model (e.g. Standard Least Squares or SLS) introduces noise into the estimation of the parameters. The main sources of this noise are the input errors and the hydrological model structural deficiencies. Thus, the biased calibrated parameters cause the divergence model phenomenon, where the errors variance of the (spatially and temporally) forecasted flows far exceeds the errors variance in the fitting period, and provoke the loss of part or all of the physical meaning of the modeled processes. In other words, yielding a calibrated hydrological model which works well, but not for the right reasons. Besides, an unsuitable error model yields a non-reliable predictive uncertainty assessment. Hence, with the aim of prevent all these undesirable effects, this research focuses on the Bayesian joint inference (BJI) of both the hydrological and error model parameters, considering a general additive (GA) error model that allows for correlation, non-stationarity (in variance and bias) and non-normality of model residuals. As hydrological model, it has been used a conceptual distributed model called TETIS, with a particular split structure of the effective model parameters. Bayesian inference has been performed with the aid of a Markov Chain Monte Carlo (MCMC) algorithm called Dream-ZS. MCMC algorithm quantifies the uncertainty of the hydrological and error model parameters by getting the joint posterior probability distribution, conditioned on the observed flows. The BJI methodology is a very powerful and reliable tool, but it must be used correctly this is, if non-stationarity in errors variance and bias is modeled, the Total Laws must be taken into account. The results of this research show that the application of BJI with a GA error model outperforms the hydrological parameters robustness (diminishing the divergence model phenomenon) and improves the reliability of the streamflow predictive distribution, in respect of the results of a bad error model as SLS. Finally, the most likely prediction in a validation period, for both BJI+GA and SLS error models shows a similar performance.

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity

PubMed Central

Whittington, James C. R.; Bogacz, Rafal

2017-01-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output. PMID:28333583

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.

PubMed

Whittington, James C R; Bogacz, Rafal

2017-05-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.

TU-AB-202-03: Prediction of PET Transfer Uncertainty by DIR Error Estimating Software, AUTODIRECT

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

Kim, H; Chen, J; Phillips, J

2016-06-15

Purpose: Deformable image registration (DIR) is a powerful tool, but DIR errors can adversely affect its clinical applications. To estimate voxel-specific DIR uncertainty, a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), has been developed and validated. This work tests the ability of this software to predict uncertainty for the transfer of standard uptake values (SUV) from positron-emission tomography (PET) with DIR. Methods: Virtual phantoms are used for this study. Each phantom has a planning computed tomography (CT) image and a diagnostic PET-CT image set. A deformation was digitally applied to the diagnostic CT to create the planningmore » CT image and establish a known deformation between the images. One lung and three rectum patient datasets were employed to create the virtual phantoms. Both of these sites have difficult deformation scenarios associated with them, which can affect DIR accuracy (lung tissue sliding and changes in rectal filling). The virtual phantoms were created to simulate these scenarios by introducing discontinuities in the deformation field at the lung rectum border. The DIR algorithm from Plastimatch software was applied to these phantoms. The SUV mapping errors from the DIR were then compared to that predicted by AUTODIRECT. Results: The SUV error distributions closely followed the AUTODIRECT predicted error distribution for the 4 test cases. The minimum and maximum PET SUVs were produced from AUTODIRECT at 95% confidence interval before applying gradient-based SUV segmentation for each of these volumes. Notably, 93.5% of the target volume warped by the true deformation was included within the AUTODIRECT-predicted maximum SUV volume after the segmentation, while 78.9% of the target volume was within the target volume warped by Plastimatch. Conclusion: The AUTODIRECT framework is able to predict PET transfer uncertainty caused by DIR, which enables an understanding of the associated target volume uncertainty.« less

Prediction and typicality in multiverse cosmology

NASA Astrophysics Data System (ADS)

Azhar, Feraz

2014-02-01

In the absence of a fundamental theory that precisely predicts values for observable parameters, anthropic reasoning attempts to constrain probability distributions over those parameters in order to facilitate the extraction of testable predictions. The utility of this approach has been vigorously debated of late, particularly in light of theories that claim we live in a multiverse, where parameters may take differing values in regions lying outside our observable horizon. Within this cosmological framework, we investigate the efficacy of top-down anthropic reasoning based on the weak anthropic principle. We argue contrary to recent claims that it is not clear one can either dispense with notions of typicality altogether or presume typicality, in comparing resulting probability distributions with observations. We show in a concrete, top-down setting related to dark matter, that assumptions about typicality can dramatically affect predictions, thereby providing a guide to how errors in reasoning regarding typicality translate to errors in the assessment of predictive power. We conjecture that this dependence on typicality is an integral feature of anthropic reasoning in broader cosmological contexts, and argue in favour of the explicit inclusion of measures of typicality in schemes invoking anthropic reasoning, with a view to extracting predictions from multiverse scenarios.

A Surrogate Approach to the Experimental Optimization of Multielement Airfoils

NASA Technical Reports Server (NTRS)

Otto, John C.; Landman, Drew; Patera, Anthony T.

1996-01-01

The incorporation of experimental test data into the optimization process is accomplished through the use of Bayesian-validated surrogates. In the surrogate approach, a surrogate for the experiment (e.g., a response surface) serves in the optimization process. The validation step of the framework provides a qualitative assessment of the surrogate quality, and bounds the surrogate-for-experiment error on designs "near" surrogate-predicted optimal designs. The utility of the framework is demonstrated through its application to the experimental selection of the trailing edge ap position to achieve a design lift coefficient for a three-element airfoil.

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation

PubMed Central

Scellier, Benjamin; Bengio, Yoshua

2017-01-01

We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function. Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point or stationary distribution) toward a configuration that reduces prediction error. In the case of a recurrent multi-layer supervised network, the output units are slightly nudged toward their target in the second phase, and the perturbation introduced at the output layer propagates backward in the hidden layers. We show that the signal “back-propagated” during this second phase corresponds to the propagation of error derivatives and encodes the gradient of the objective function, when the synaptic update corresponds to a standard form of spike-timing dependent plasticity. This work makes it more plausible that a mechanism similar to Backpropagation could be implemented by brains, since leaky integrator neural computation performs both inference and error back-propagation in our model. The only local difference between the two phases is whether synaptic changes are allowed or not. We also show experimentally that multi-layer recurrently connected networks with 1, 2, and 3 hidden layers can be trained by Equilibrium Propagation on the permutation-invariant MNIST task. PMID:28522969

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

PubMed Central

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-01-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity. PMID:23592970

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

PubMed

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-04-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

A Framework for Identifying and Classifying Undergraduate Student Proof Errors

ERIC Educational Resources Information Center

Strickland, S.; Rand, B.

2016-01-01

This paper describes a framework for identifying, classifying, and coding student proofs, modified from existing proof-grading rubrics. The framework includes 20 common errors, as well as categories for interpreting the severity of the error. The coding scheme is intended for use in a classroom context, for providing effective student feedback. In…

Toward a Neurobiology of Delusions

PubMed Central

Corlett, P.R.; Taylor, J.R.; Wang, X.-J.; Fletcher, P.C.; Krystal, J.H.

2013-01-01

Delusions are the false and often incorrigible beliefs that can cause severe suffering in mental illness. We cannot yet explain them in terms of underlying neurobiological abnormalities. However, by drawing on recent advances in the biological, computational and psychological processes of reinforcement learning, memory, and perception it may be feasible to account for delusions in terms of cognition and brain function. The account focuses on a particular parameter, prediction error – the mismatch between expectation and experience – that provides a computational mechanism common to cortical hierarchies, frontostriatal circuits and the amygdala as well as parietal cortices. We suggest that delusions result from aberrations in how brain circuits specify hierarchical predictions, and how they compute and respond to prediction errors. Defects in these fundamental brain mechanisms can vitiate perception, memory, bodily agency and social learning such that individuals with delusions experience an internal and external world that healthy individuals would find difficult to comprehend. The present model attempts to provide a framework through which we can build a mechanistic and translational understanding of these puzzling symptoms. PMID:20558235

A Formal Approach to Empirical Dynamic Model Optimization and Validation

NASA Technical Reports Server (NTRS)

Crespo, Luis G; Morelli, Eugene A.; Kenny, Sean P.; Giesy, Daniel P.

2014-01-01

A framework was developed for the optimization and validation of empirical dynamic models subject to an arbitrary set of validation criteria. The validation requirements imposed upon the model, which may involve several sets of input-output data and arbitrary specifications in time and frequency domains, are used to determine if model predictions are within admissible error limits. The parameters of the empirical model are estimated by finding the parameter realization for which the smallest of the margins of requirement compliance is as large as possible. The uncertainty in the value of this estimate is characterized by studying the set of model parameters yielding predictions that comply with all the requirements. Strategies are presented for bounding this set, studying its dependence on admissible prediction error set by the analyst, and evaluating the sensitivity of the model predictions to parameter variations. This information is instrumental in characterizing uncertainty models used for evaluating the dynamic model at operating conditions differing from those used for its identification and validation. A practical example based on the short period dynamics of the F-16 is used for illustration.

A Symbiotic Framework for coupling Machine Learning and Geosciences in Prediction and Predictability

NASA Astrophysics Data System (ADS)

Ravela, S.

2017-12-01

In this presentation we review the two directions of a symbiotic relationship between machine learning and the geosciences in relation to prediction and predictability. In the first direction, we develop ensemble, information theoretic and manifold learning framework to adaptively improve state and parameter estimates in nonlinear high-dimensional non-Gaussian problems, showing in particular that tractable variational approaches can be produced. We demonstrate these applications in the context of autonomous mapping of environmental coherent structures and other idealized problems. In the reverse direction, we show that data assimilation, particularly probabilistic approaches for filtering and smoothing offer a novel and useful way to train neural networks, and serve as a better basis than gradient based approaches when we must quantify uncertainty in association with nonlinear, chaotic processes. In many inference problems in geosciences we seek to build reduced models to characterize local sensitivies, adjoints or other mechanisms that propagate innovations and errors. Here, the particular use of neural approaches for such propagation trained using ensemble data assimilation provides a novel framework. Through these two examples of inference problems in the earth sciences, we show that not only is learning useful to broaden existing methodology, but in reverse, geophysical methodology can be used to influence paradigms in learning.

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

Vrugt, Jasper A; Robinson, Bruce A; Ter Braak, Cajo J F

In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented usingmore » the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchments.« less

A Novel Approach of Understanding and Incorporating Error of Chemical Transport Models into a Geostatistical Framework

NASA Astrophysics Data System (ADS)

Reyes, J.; Vizuete, W.; Serre, M. L.; Xu, Y.

2015-12-01

The EPA employs a vast monitoring network to measure ambient PM2.5 concentrations across the United States with one of its goals being to quantify exposure within the population. However, there are several areas of the country with sparse monitoring spatially and temporally. One means to fill in these monitoring gaps is to use PM2.5 modeled estimates from Chemical Transport Models (CTMs) specifically the Community Multi-scale Air Quality (CMAQ) model. CMAQ is able to provide complete spatial coverage but is subject to systematic and random error due to model uncertainty. Due to the deterministic nature of CMAQ, often these uncertainties are not quantified. Much effort is employed to quantify the efficacy of these models through different metrics of model performance. Currently evaluation is specific to only locations with observed data. Multiyear studies across the United States are challenging because the error and model performance of CMAQ are not uniform over such large space/time domains. Error changes regionally and temporally. Because of the complex mix of species that constitute PM2.5, CMAQ error is also a function of increasing PM2.5 concentration. To address this issue we introduce a model performance evaluation for PM2.5 CMAQ that is regionalized and non-linear. This model performance evaluation leads to error quantification for each CMAQ grid. Areas and time periods of error being better qualified. The regionalized error correction approach is non-linear and is therefore more flexible at characterizing model performance than approaches that rely on linearity assumptions and assume homoscedasticity of CMAQ predictions errors. Corrected CMAQ data are then incorporated into the modern geostatistical framework of Bayesian Maximum Entropy (BME). Through cross validation it is shown that incorporating error-corrected CMAQ data leads to more accurate estimates than just using observed data by themselves.

Error Analysis and Selection of Optimal Excitation Parameters for the Sensing of CO2 and O2 from Space for ASCENDS Applications

NASA Technical Reports Server (NTRS)

Pliutau, Denis; Prasad, Narasimha S.

2012-01-01

Simulation studies to optimize sensing of CO2 and O2 from space are described. Uncertainties in line-by-line calculations unaccounted for in previous studies identified. Multivariate methods are employed for measurement wavelengths selection. The Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) recommended by NRC Decadal Survey has a stringent accuracy requirements of 0.5% or better in XCO2 retrievals. NASA LaRC and its partners are investigating the use of the 1.57 m band of CO2 and the 1.26-1.27 m band of oxygen for XCO2 measurements. As part of these efforts, we are carrying out simulation studies using a lidar modeling framework being developed at NASA LaRC to predict the performance of our proposed ASCENDS mission implementation [1]. Our study is aimed at predicting the sources and magnitudes of errors anticipated in XCO2 retrievals for further error minimization through the selection of optimum excitation parameters and development of better retrieval methods.

Uncertainty Quantification for Polynomial Systems via Bernstein Expansions

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2012-01-01

This paper presents a unifying framework to uncertainty quantification for systems having polynomial response metrics that depend on both aleatory and epistemic uncertainties. The approach proposed, which is based on the Bernstein expansions of polynomials, enables bounding the range of moments and failure probabilities of response metrics as well as finding supersets of the extreme epistemic realizations where the limits of such ranges occur. These bounds and supersets, whose analytical structure renders them free of approximation error, can be made arbitrarily tight with additional computational effort. Furthermore, this framework enables determining the importance of particular uncertain parameters according to the extent to which they affect the first two moments of response metrics and failure probabilities. This analysis enables determining the parameters that should be considered uncertain as well as those that can be assumed to be constants without incurring significant error. The analytical nature of the approach eliminates the numerical error that characterizes the sampling-based techniques commonly used to propagate aleatory uncertainties as well as the possibility of under predicting the range of the statistic of interest that may result from searching for the best- and worstcase epistemic values via nonlinear optimization or sampling.

Visual anticipation biases conscious decision making but not bottom-up visual processing

PubMed Central

Mathews, Zenon; Cetnarski, Ryszard; Verschure, Paul F. M. J.

2015-01-01

Prediction plays a key role in control of attention but it is not clear which aspects of prediction are most prominent in conscious experience. An evolving view on the brain is that it can be seen as a prediction machine that optimizes its ability to predict states of the world and the self through the top-down propagation of predictions and the bottom-up presentation of prediction errors. There are competing views though on whether prediction or prediction errors dominate the formation of conscious experience. Yet, the dynamic effects of prediction on perception, decision making and consciousness have been difficult to assess and to model. We propose a novel mathematical framework and a psychophysical paradigm that allows us to assess both the hierarchical structuring of perceptual consciousness, its content and the impact of predictions and/or errors on conscious experience, attention and decision-making. Using a displacement detection task combined with reverse correlation, we reveal signatures of the usage of prediction at three different levels of perceptual processing: bottom-up fast saccades, top-down driven slow saccades and consciousnes decisions. Our results suggest that the brain employs multiple parallel mechanism at different levels of perceptual processing in order to shape effective sensory consciousness within a predicted perceptual scene. We further observe that bottom-up sensory and top-down predictive processes can be dissociated through cognitive load. We propose a probabilistic data association model from dynamical systems theory to model the predictive multi-scale bias in perceptual processing that we observe and its role in the formation of conscious experience. We propose that these results support the hypothesis that consciousness provides a time-delayed description of a task that is used to prospectively optimize real time control structures, rather than being engaged in the real-time control of behavior itself. PMID:25741290

Towards a Unified Framework in Hydroclimate Extremes Prediction in Changing Climate

NASA Astrophysics Data System (ADS)

Moradkhani, H.; Yan, H.; Zarekarizi, M.; Bracken, C.

2016-12-01

Spatio-temporal analysis and prediction of hydroclimate extremes are of paramount importance in disaster mitigation and emergency management. The IPCC special report on managing the risks of extreme events and disasters emphasizes that the global warming would change the frequency, severity, and spatial pattern of extremes. In addition to climate change, land use and land cover changes also influence the extreme characteristics at regional scale. Therefore, natural variability and anthropogenic changes to the hydroclimate system result in nonstationarity in hydroclimate variables. In this presentation recent advancements in developing and using Bayesian approaches to account for non-stationarity in hydroclimate extremes are discussed. Also, implications of these approaches in flood frequency analysis, treatment of spatial dependence, the impact of large-scale climate variability, the selection of cause-effect covariates, with quantification of model errors in extreme prediction is explained. Within this framework, the applicability and usefulness of the ensemble data assimilation for extreme flood predictions is also introduced. Finally, a practical and easy to use approach for better communication with decision-makers and emergency managers is presented.

Dopamine reward prediction errors reflect hidden state inference across time

PubMed Central

Starkweather, Clara Kwon; Babayan, Benedicte M.; Uchida, Naoshige; Gershman, Samuel J.

2017-01-01

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a ‘belief state’). In this work, we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling exhibited a striking difference between two tasks that differed only with respect to whether reward was delivered deterministically. Our results favor an associative learning rule that combines cached values with hidden state inference. PMID:28263301

Remembering a criminal conversation: beyond eyewitness testimony.

PubMed

Campos, Laura; Alonso-Quecuty, María L

2006-01-01

Unlike the important body of work on eyewitness memory, little research has been done on the accuracy and completeness of "earwitness" memory for conversations. The present research examined the effects of mode of presentation (audiovisual/ auditory-only) on witnesses' free recall for utterances in a criminal conversation at different retention intervals (immediate/delayed) within a single experiment. Different forms of correct recall (verbatim/gist) of the verbal information as well as different types of errors (distortions/fabrications) were also examined. It was predicted that participants in the audiovisual modality would provide more correct information, and fewer errors than participants in the auditory-only modality. Participants' recall was predicted to be impaired over time, dropping to a greater extent after a delay in the auditory-only modality. Results confirmed these hypotheses. Interpretations of the overall findings are offered within the context of dual-coding theory, and within the theoretical frameworks of source monitoring and fuzzy-trace theory.

Dopamine reward prediction errors reflect hidden-state inference across time.

PubMed

Starkweather, Clara Kwon; Babayan, Benedicte M; Uchida, Naoshige; Gershman, Samuel J

2017-04-01

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a 'belief state'). Here we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling showed a notable difference between two tasks that differed only with respect to whether reward was delivered in a deterministic manner. Our results favor an associative learning rule that combines cached values with hidden-state inference.

Towards a Framework for Managing Risk Associated with Technology-Induced Error.

PubMed

Borycki, Elizabeth M; Kushniruk, Andre W

2017-01-01

Health information technologies (HIT) promised to streamline and modernize healthcare processes. However, a growing body of research has indicated that if such technologies are not designed, implemented or maintained properly this may lead to an increased incidence of new types of errors which the authors have referred to as "technology-induced errors". In this paper, framework is presented that can be used to manage HIT risk. The framework considers the reduction of technology-induced errors at different stages by managing risks associated with the implementation of HIT. Frameworks that allow health information technology managers to employ proactive and preventative approaches that can be used to manage the risks associated with technology-induced errors are critical to improving HIT safety and managing risk associated with implementing new technologies.

Renormalized coupled cluster approaches in the cluster-in-molecule framework: predicting vertical electron binding energies of the anionic water clusters (H2O)(n)(-).

PubMed

Xu, Peng; Gordon, Mark S

2014-09-04

Anionic water clusters are generally considered to be extremely challenging to model using fragmentation approaches due to the diffuse nature of the excess electron distribution. The local correlation coupled cluster (CC) framework cluster-in-molecule (CIM) approach combined with the completely renormalized CR-CC(2,3) method [abbreviated CIM/CR-CC(2,3)] is shown to be a viable alternative for computing the vertical electron binding energies (VEBE). CIM/CR-CC(2,3) with the threshold parameter ζ set to 0.001, as a trade-off between accuracy and computational cost, demonstrates the reliability of predicting the VEBE, with an average percentage error of ∼15% compared to the full ab initio calculation at the same level of theory. The errors are predominantly from the electron correlation energy. The CIM/CR-CC(2,3) approach provides the ease of a black-box type calculation with few threshold parameters to manipulate. The cluster sizes that can be studied by high-level ab initio methods are significantly increased in comparison with full CC calculations. Therefore, the VEBE computed by the CIM/CR-CC(2,3) method can be used as benchmarks for testing model potential approaches in small-to-intermediate-sized water clusters.

Patient specific computerized phantoms to estimate dose in pediatric CT

NASA Astrophysics Data System (ADS)

Segars, W. P.; Sturgeon, G.; Li, X.; Cheng, L.; Ceritoglu, C.; Ratnanather, J. T.; Miller, M. I.; Tsui, B. M. W.; Frush, D.; Samei, E.

2009-02-01

We create a series of detailed computerized phantoms to estimate patient organ and effective dose in pediatric CT and investigate techniques for efficiently creating patient-specific phantoms based on imaging data. The initial anatomy of each phantom was previously developed based on manual segmentation of pediatric CT data. Each phantom was extended to include a more detailed anatomy based on morphing an existing adult phantom in our laboratory to match the framework (based on segmentation) defined for the target pediatric model. By morphing a template anatomy to match the patient data in the LDDMM framework, it was possible to create a patient specific phantom with many anatomical structures, some not visible in the CT data. The adult models contain thousands of defined structures that were transformed to define them in each pediatric anatomy. The accuracy of this method, under different conditions, was tested using a known voxelized phantom as the target. Errors were measured in terms of a distance map between the predicted organ surfaces and the known ones. We also compared calculated dose measurements to see the effect of different magnitudes of errors in morphing. Despite some variations in organ geometry, dose measurements from morphing predictions were found to agree with those calculated from the voxelized phantom thus demonstrating the feasibility of our methods.

The error-related negativity (ERN) is an electrophysiological marker of motor impulsiveness on the Barratt Impulsiveness Scale (BIS-11) during adolescence.

PubMed

Taylor, Jasmine B; Visser, Troy A W; Fueggle, Simone N; Bellgrove, Mark A; Fox, Allison M

2018-04-01

Previous studies have postulated that the error-related negativity (ERN) may reflect individual differences in impulsivity; however, none have used a longitudinal framework or evaluated impulsivity as a multidimensional construct. The current study evaluated whether ERN amplitude, measured in childhood and adolescence, is predictive of impulsiveness during adolescence. Seventy-five children participated in this study, initially at ages 7-9 years and again at 12-18 years. The interval between testing sessions ranged from 5 to 9 years. The ERN was extracted in response to behavioural errors produced during a modified visual flanker task at both time points (i.e. childhood and adolescence). Participants also completed the Barratt Impulsiveness Scale - a measure that considers impulsiveness to comprise three core sub-traits - during adolescence. At adolescence, the ERN amplitude was significantly larger than during childhood. Additionally, ERN amplitude during adolescence significantly predicted motor impulsiveness at that time point, after controlling for age, gender, and the number of trials included in the ERN. In contrast, ERN amplitude during childhood did not uniquely predict impulsiveness during adolescence. These findings provide preliminary evidence that ERN amplitude is an electrophysiological marker of self-reported motor impulsiveness (i.e. acting without thinking) during adolescence. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Review of Auditory Prediction and Its Potential Role in Tinnitus Perception.

PubMed

Durai, Mithila; O'Keeffe, Mary G; Searchfield, Grant D

2018-06-01

The precise mechanisms underlying tinnitus perception and distress are still not fully understood. A recent proposition is that auditory prediction errors and related memory representations may play a role in driving tinnitus perception. It is of interest to further explore this. To obtain a comprehensive narrative synthesis of current research in relation to auditory prediction and its potential role in tinnitus perception and severity. A narrative review methodological framework was followed. The key words Prediction Auditory, Memory Prediction Auditory, Tinnitus AND Memory, Tinnitus AND Prediction in Article Title, Abstract, and Keywords were extensively searched on four databases: PubMed, Scopus, SpringerLink, and PsychINFO. All study types were selected from 2000-2016 (end of 2016) and had the following exclusion criteria applied: minimum age of participants <18, nonhuman participants, and article not available in English. Reference lists of articles were reviewed to identify any further relevant studies. Articles were short listed based on title relevance. After reading the abstracts and with consensus made between coauthors, a total of 114 studies were selected for charting data. The hierarchical predictive coding model based on the Bayesian brain hypothesis, attentional modulation and top-down feedback serves as the fundamental framework in current literature for how auditory prediction may occur. Predictions are integral to speech and music processing, as well as in sequential processing and identification of auditory objects during auditory streaming. Although deviant responses are observable from middle latency time ranges, the mismatch negativity (MMN) waveform is the most commonly studied electrophysiological index of auditory irregularity detection. However, limitations may apply when interpreting findings because of the debatable origin of the MMN and its restricted ability to model real-life, more complex auditory phenomenon. Cortical oscillatory band activity may act as neurophysiological substrates for auditory prediction. Tinnitus has been modeled as an auditory object which may demonstrate incomplete processing during auditory scene analysis resulting in tinnitus salience and therefore difficulty in habituation. Within the electrophysiological domain, there is currently mixed evidence regarding oscillatory band changes in tinnitus. There are theoretical proposals for a relationship between prediction error and tinnitus but few published empirical studies. American Academy of Audiology.

Adaptive framework to better characterize errors of apriori fluxes and observational residuals in a Bayesian setup for the urban flux inversions.

NASA Astrophysics Data System (ADS)

Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Karion, A.; Mueller, K.; Gourdji, S.; Martin, C.; Whetstone, J. R.

2017-12-01

The National Institute of Standards and Technology (NIST) supports the North-East Corridor Baltimore Washington (NEC-B/W) project and Indianapolis Flux Experiment (INFLUX) aiming to quantify sources of Greenhouse Gas (GHG) emissions as well as their uncertainties. These projects employ different flux estimation methods including top-down inversion approaches. The traditional Bayesian inversion method estimates emission distributions by updating prior information using atmospheric observations of Green House Gases (GHG) coupled to an atmospheric and dispersion model. The magnitude of the update is dependent upon the observed enhancement along with the assumed errors such as those associated with prior information and the atmospheric transport and dispersion model. These errors are specified within the inversion covariance matrices. The assumed structure and magnitude of the specified errors can have large impact on the emission estimates from the inversion. The main objective of this work is to build a data-adaptive model for these covariances matrices. We construct a synthetic data experiment using a Kalman Filter inversion framework (Lopez et al., 2017) employing different configurations of transport and dispersion model and an assumed prior. Unlike previous traditional Bayesian approaches, we estimate posterior emissions using regularized sample covariance matrices associated with prior errors to investigate whether the structure of the matrices help to better recover our hypothetical true emissions. To incorporate transport model error, we use ensemble of transport models combined with space-time analytical covariance to construct a covariance that accounts for errors in space and time. A Kalman Filter is then run using these covariances along with Maximum Likelihood Estimates (MLE) of the involved parameters. Preliminary results indicate that specifying sptio-temporally varying errors in the error covariances can improve the flux estimates and uncertainties. We also demonstrate that differences between the modeled and observed meteorology can be used to predict uncertainties associated with atmospheric transport and dispersion modeling which can help improve the skill of an inversion at urban scales.

Motion prediction in MRI-guided radiotherapy based on interleaved orthogonal cine-MRI

NASA Astrophysics Data System (ADS)

Seregni, M.; Paganelli, C.; Lee, D.; Greer, P. B.; Baroni, G.; Keall, P. J.; Riboldi, M.

2016-01-01

In-room cine-MRI guidance can provide non-invasive target localization during radiotherapy treatment. However, in order to cope with finite imaging frequency and system latencies between target localization and dose delivery, tumour motion prediction is required. This work proposes a framework for motion prediction dedicated to cine-MRI guidance, aiming at quantifying the geometric uncertainties introduced by this process for both tumour tracking and beam gating. The tumour position, identified through scale invariant features detected in cine-MRI slices, is estimated at high-frequency (25 Hz) using three independent predictors, one for each anatomical coordinate. Linear extrapolation, auto-regressive and support vector machine algorithms are compared against systems that use no prediction or surrogate-based motion estimation. Geometric uncertainties are reported as a function of image acquisition period and system latency. Average results show that the tracking error RMS can be decreased down to a [0.2; 1.2] mm range, for acquisition periods between 250 and 750 ms and system latencies between 50 and 300 ms. Except for the linear extrapolator, tracking and gating prediction errors were, on average, lower than those measured for surrogate-based motion estimation. This finding suggests that cine-MRI guidance, combined with appropriate prediction algorithms, could relevantly decrease geometric uncertainties in motion compensated treatments.

Progressive Dictionary Learning with Hierarchical Predictive Structure for Scalable Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Xiong, Hongkai; Jiang, Xiaoqian; Zou, Junni; Taubman, David

2017-04-12

Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers (ELs) with a guarantee of reconstruction performance. The overcomplete dictionary is trained to adaptively capture local structures along motion trajectories as well as exploit the correlations between neighboring layers of resolutions. Furthermore, progressive dictionary learning is developed to enable the scalability in temporal domain and restrict the error propagation in a close-loop predictor. Under the hierarchical predictive structure, online learning is leveraged to guarantee the training and prediction performance with an improved convergence rate. To accommodate with the stateof- the-art scalable extension of H.264/AVC and latest HEVC, standardized codec cores are utilized to encode the base and enhancement layers. Experimental results show that the proposed method outperforms the latest SHVC and HEVC simulcast over extensive test sequences with various resolutions.

Decision-making in schizophrenia: A predictive-coding perspective.

PubMed

Sterzer, Philipp; Voss, Martin; Schlagenhauf, Florian; Heinz, Andreas

2018-05-31

Dysfunctional decision-making has been implicated in the positive and negative symptoms of schizophrenia. Decision-making can be conceptualized within the framework of hierarchical predictive coding as the result of a Bayesian inference process that uses prior beliefs to infer states of the world. According to this idea, prior beliefs encoded at higher levels in the brain are fed back as predictive signals to lower levels. Whenever these predictions are violated by the incoming sensory data, a prediction error is generated and fed forward to update beliefs encoded at higher levels. Well-documented impairments in cognitive decision-making support the view that these neural inference mechanisms are altered in schizophrenia. There is also extensive evidence relating the symptoms of schizophrenia to aberrant signaling of prediction errors, especially in the domain of reward and value-based decision-making. Moreover, the idea of altered predictive coding is supported by evidence for impaired low-level sensory mechanisms and motor processes. We review behavioral and neural findings from these research areas and provide an integrated view suggesting that schizophrenia may be related to a pervasive alteration in predictive coding at multiple hierarchical levels, including cognitive and value-based decision-making processes as well as sensory and motor systems. We relate these findings to decision-making processes and propose that varying degrees of impairment in the implicated brain areas contribute to the variety of psychotic experiences. Copyright © 2018 Elsevier Inc. All rights reserved.

Toward accurate prediction of pKa values for internal protein residues: the importance of conformational relaxation and desolvation energy.

PubMed

Wallace, Jason A; Wang, Yuhang; Shi, Chuanyin; Pastoor, Kevin J; Nguyen, Bao-Linh; Xia, Kai; Shen, Jana K

2011-12-01

Proton uptake or release controls many important biological processes, such as energy transduction, virus replication, and catalysis. Accurate pK(a) prediction informs about proton pathways, thereby revealing detailed acid-base mechanisms. Physics-based methods in the framework of molecular dynamics simulations not only offer pK(a) predictions but also inform about the physical origins of pK(a) shifts and provide details of ionization-induced conformational relaxation and large-scale transitions. One such method is the recently developed continuous constant pH molecular dynamics (CPHMD) method, which has been shown to be an accurate and robust pK(a) prediction tool for naturally occurring titratable residues. To further examine the accuracy and limitations of CPHMD, we blindly predicted the pK(a) values for 87 titratable residues introduced in various hydrophobic regions of staphylococcal nuclease and variants. The predictions gave a root-mean-square deviation of 1.69 pK units from experiment, and there were only two pK(a)'s with errors greater than 3.5 pK units. Analysis of the conformational fluctuation of titrating side-chains in the context of the errors of calculated pK(a) values indicate that explicit treatment of conformational flexibility and the associated dielectric relaxation gives CPHMD a distinct advantage. Analysis of the sources of errors suggests that more accurate pK(a) predictions can be obtained for the most deeply buried residues by improving the accuracy in calculating desolvation energies. Furthermore, it is found that the generalized Born implicit-solvent model underlying the current CPHMD implementation slightly distorts the local conformational environment such that the inclusion of an explicit-solvent representation may offer improvement of accuracy. Copyright © 2011 Wiley-Liss, Inc.

Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation.

PubMed

Saha, Monjoy; Chakraborty, Chandan

2018-05-01

We present an efficient deep learning framework for identifying, segmenting, and classifying cell membranes and nuclei from human epidermal growth factor receptor-2 (HER2)-stained breast cancer images with minimal user intervention. This is a long-standing issue for pathologists because the manual quantification of HER2 is error-prone, costly, and time-consuming. Hence, we propose a deep learning-based HER2 deep neural network (Her2Net) to solve this issue. The convolutional and deconvolutional parts of the proposed Her2Net framework consisted mainly of multiple convolution layers, max-pooling layers, spatial pyramid pooling layers, deconvolution layers, up-sampling layers, and trapezoidal long short-term memory (TLSTM). A fully connected layer and a softmax layer were also used for classification and error estimation. Finally, HER2 scores were calculated based on the classification results. The main contribution of our proposed Her2Net framework includes the implementation of TLSTM and a deep learning framework for cell membrane and nucleus detection, segmentation, and classification and HER2 scoring. Our proposed Her2Net achieved 96.64% precision, 96.79% recall, 96.71% F-score, 93.08% negative predictive value, 98.33% accuracy, and a 6.84% false-positive rate. Our results demonstrate the high accuracy and wide applicability of the proposed Her2Net in the context of HER2 scoring for breast cancer evaluation.

An optimization-based framework for anisotropic simplex mesh adaptation

NASA Astrophysics Data System (ADS)

Yano, Masayuki; Darmofal, David L.

2012-09-01

We present a general framework for anisotropic h-adaptation of simplex meshes. Given a discretization and any element-wise, localizable error estimate, our adaptive method iterates toward a mesh that minimizes error for a given degrees of freedom. Utilizing mesh-metric duality, we consider a continuous optimization problem of the Riemannian metric tensor field that provides an anisotropic description of element sizes. First, our method performs a series of local solves to survey the behavior of the local error function. This information is then synthesized using an affine-invariant tensor manipulation framework to reconstruct an approximate gradient of the error function with respect to the metric tensor field. Finally, we perform gradient descent in the metric space to drive the mesh toward optimality. The method is first demonstrated to produce optimal anisotropic meshes minimizing the L2 projection error for a pair of canonical problems containing a singularity and a singular perturbation. The effectiveness of the framework is then demonstrated in the context of output-based adaptation for the advection-diffusion equation using a high-order discontinuous Galerkin discretization and the dual-weighted residual (DWR) error estimate. The method presented provides a unified framework for optimizing both the element size and anisotropy distribution using an a posteriori error estimate and enables efficient adaptation of anisotropic simplex meshes for high-order discretizations.

Spectral Analysis of Forecast Error Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, N. C.; Errico, Ronald M.

2015-01-01

The spectra of analysis and forecast error are examined using the observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASAGMAO). A global numerical weather prediction model, the Global Earth Observing System version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation, is cycled for two months with once-daily forecasts to 336 hours to generate a control case. Verification of forecast errors using the Nature Run as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self analysis verification significantly overestimates the error growth rates of the early forecast, as well as mischaracterizing the spatial scales at which the strongest growth occurs. The Nature Run-verified error variances exhibit a complicated progression of growth, particularly for low wave number errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realization of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

Biologically driven neural platform invoking parallel electrophoretic separation and urinary metabolite screening.

PubMed

Page, Tessa; Nguyen, Huong Thi Huynh; Hilts, Lindsey; Ramos, Lorena; Hanrahan, Grady

2012-06-01

This work reveals a computational framework for parallel electrophoretic separation of complex biological macromolecules and model urinary metabolites. More specifically, the implementation of a particle swarm optimization (PSO) algorithm on a neural network platform for multiparameter optimization of multiplexed 24-capillary electrophoresis technology with UV detection is highlighted. Two experimental systems were examined: (1) separation of purified rabbit metallothioneins and (2) separation of model toluene urinary metabolites and selected organic acids. Results proved superior to the use of neural networks employing standard back propagation when examining training error, fitting response, and predictive abilities. Simulation runs were obtained as a result of metaheuristic examination of the global search space with experimental responses in good agreement with predicted values. Full separation of selected analytes was realized after employing optimal model conditions. This framework provides guidance for the application of metaheuristic computational tools to aid in future studies involving parallel chemical separation and screening. Adaptable pseudo-code is provided to enable users of varied software packages and modeling framework to implement the PSO algorithm for their desired use.

A robust nonparametric framework for reconstruction of stochastic differential equation models

NASA Astrophysics Data System (ADS)

Rajabzadeh, Yalda; Rezaie, Amir Hossein; Amindavar, Hamidreza

2016-05-01

In this paper, we employ a nonparametric framework to robustly estimate the functional forms of drift and diffusion terms from discrete stationary time series. The proposed method significantly improves the accuracy of the parameter estimation. In this framework, drift and diffusion coefficients are modeled through orthogonal Legendre polynomials. We employ the least squares regression approach along with the Euler-Maruyama approximation method to learn coefficients of stochastic model. Next, a numerical discrete construction of mean squared prediction error (MSPE) is established to calculate the order of Legendre polynomials in drift and diffusion terms. We show numerically that the new method is robust against the variation in sample size and sampling rate. The performance of our method in comparison with the kernel-based regression (KBR) method is demonstrated through simulation and real data. In case of real dataset, we test our method for discriminating healthy electroencephalogram (EEG) signals from epilepsy ones. We also demonstrate the efficiency of the method through prediction in the financial data. In both simulation and real data, our algorithm outperforms the KBR method.

Center-TRACON Automation System (CTAS) En Route Trajectory Predictor Requirements and Capabilities

NASA Technical Reports Server (NTRS)

Vivona, Robert; Cate, Karen Tung

2013-01-01

This requirements framework document is designed to support the capture of requirements and capabilities for state-of-the-art trajectory predictors (TPs). This framework has been developed to assist TP experts in capturing a clear, consistent, and cross-comparable set of requirements and capabilities. The goal is to capture capabilities (types of trajectories that can be built), functional requirements (including inputs and outputs), non-functional requirements (including prediction accuracy and computational performance), approaches for constraint relaxation, and input uncertainties. The sections of this framework are based on the Common Trajectory Predictor structure developed by the FAA/Eurocontrol Cooperative R&D Action Plan 16 Committee on Common Trajectory Prediction. It is assumed that the reader is familiar with the Common TP Structure.1 This initial draft is intended as a first cut capture of the En Route TS Capabilities and Requirements. As such, it contains many annotations indicating possible logic errors in the CTAS code or in the description provided. It is intended to work out the details of the annotations with NASA and to update this document at a later time.

On The Usage Of Fire Smoke Emissions In An Air Quality Forecasting System To Reduce Particular Matter Forecasting Error

NASA Astrophysics Data System (ADS)

Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; DiMego, G.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2016-12-01

Wildfires contribute to air quality problems not only towards primary emissions of particular matters (PM) but also emitted ozone precursor gases that can lead to elevated ozone concentration. Wildfires are unpredictable and can be ignited by natural causes such as lightning or accidently by human negligent behavior such as live cigarette. Although wildfire impacts on the air quality can be studied by collecting fire information after events, it is extremely difficult to predict future occurrence and behavior of wildfires for real-time air quality forecasts. Because of the time constraints of operational air quality forecasting, assumption of future day's fire behavior often have to be made based on observed fire information in the past. The United States (U.S.) NOAA/NWS built the National Air Quality Forecast Capability (NAQFC) based on the U.S. EPA CMAQ to provide air quality forecast guidance (prediction) publicly. State and local forecasters use the forecast guidance to issue air quality alerts in their area. The NAQFC fine particulates (PM2.5) prediction includes emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and fires. The fire emission input to the NAQFC is derived from the NOAA NESDIS HMS fire and smoke detection product and the emission module of the US Forest Service BlueSky Smoke Modeling Framework. This study focuses on the error estimation of NAQFC PM2.5 predictions resulting from fire emissions. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that present operational NAQFC fire emissions assumption can lead to a huge error in PM2.5 prediction as fire emissions are sometimes placed at wrong location and time. This PM2.5 prediction error can be propagated from the fire source in the Northwest U.S. to downstream areas as far as the Southeast U.S. From this study, a new procedure has been identified to minimize the aforementioned error. An additional 24 hours reanalysis-run of NAQFC using same-day observed fire emission are being tested. Preliminary results have shown that this procedure greatly improves the PM2.5 predictions at both nearby and downstream areas from fire sources. The 24 hours reanalysis-run is critical and necessary especially during extreme fire events to provide better PM2.5 predictions.

Physiologically grounded metrics of model skill: a case study estimating heat stress in intertidal populations

PubMed Central

Kish, Nicole E.; Helmuth, Brian; Wethey, David S.

2016-01-01

Models of ecological responses to climate change fundamentally assume that predictor variables, which are often measured at large scales, are to some degree diagnostic of the smaller-scale biological processes that ultimately drive patterns of abundance and distribution. Given that organisms respond physiologically to stressors, such as temperature, in highly non-linear ways, small modelling errors in predictor variables can potentially result in failures to predict mortality or severe stress, especially if an organism exists near its physiological limits. As a result, a central challenge facing ecologists, particularly those attempting to forecast future responses to environmental change, is how to develop metrics of forecast model skill (the ability of a model to predict defined events) that are biologically meaningful and reflective of underlying processes. We quantified the skill of four simple models of body temperature (a primary determinant of physiological stress) of an intertidal mussel, Mytilus californianus, using common metrics of model performance, such as root mean square error, as well as forecast verification skill scores developed by the meteorological community. We used a physiologically grounded framework to assess each model's ability to predict optimal, sub-optimal, sub-lethal and lethal physiological responses. Models diverged in their ability to predict different levels of physiological stress when evaluated using skill scores, even though common metrics, such as root mean square error, indicated similar accuracy overall. Results from this study emphasize the importance of grounding assessments of model skill in the context of an organism's physiology and, especially, of considering the implications of false-positive and false-negative errors when forecasting the ecological effects of environmental change. PMID:27729979

Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint

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

Smith, Kandler A; Saxon, Aron R; Keyser, Matthew A

Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity andmore » resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4 percent RMS error and resistance growth with 15 percent RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.« less

Predictable and reliable ECG monitoring over IEEE 802.11 WLANs within a hospital.

PubMed

Park, Juyoung; Kang, Kyungtae

2014-09-01

Telecardiology provides mobility for patients who require constant electrocardiogram (ECG) monitoring. However, its safety is dependent on the predictability and robustness of data delivery, which must overcome errors in the wireless channel through which the ECG data are transmitted. We report here a framework that can be used to gauge the applicability of IEEE 802.11 wireless local area network (WLAN) technology to ECG monitoring systems in terms of delay constraints and transmission reliability. For this purpose, a medical-grade WLAN architecture achieved predictable delay through the combination of a medium access control mechanism based on the point coordination function provided by IEEE 802.11 and an error control scheme based on Reed-Solomon coding and block interleaving. The size of the jitter buffer needed was determined by this architecture to avoid service dropout caused by buffer underrun, through analysis of variations in transmission delay. Finally, we assessed this architecture in terms of service latency and reliability by modeling the transmission of uncompressed two-lead electrocardiogram data from the MIT-BIH Arrhythmia Database and highlight the applicability of this wireless technology to telecardiology.

From conflict management to reward-based decision making: actors and critics in primate medial frontal cortex.

PubMed

Silvetti, Massimo; Alexander, William; Verguts, Tom; Brown, Joshua W

2014-10-01

The role of the medial prefrontal cortex (mPFC) and especially the anterior cingulate cortex has been the subject of intense debate for the last decade. A number of theories have been proposed to account for its function. Broadly speaking, some emphasize cognitive control, whereas others emphasize value processing; specific theories concern reward processing, conflict detection, error monitoring, and volatility detection, among others. Here we survey and evaluate them relative to experimental results from neurophysiological, anatomical, and cognitive studies. We argue for a new conceptualization of mPFC, arising from recent computational modeling work. Based on reinforcement learning theory, these new models propose that mPFC is an Actor-Critic system. This system is aimed to predict future events including rewards, to evaluate errors in those predictions, and finally, to implement optimal skeletal-motor and visceromotor commands to obtain reward. This framework provides a comprehensive account of mPFC function, accounting for and predicting empirical results across different levels of analysis, including monkey neurophysiology, human ERP, human neuroimaging, and human behavior. Copyright © 2013 Elsevier Ltd. All rights reserved.

The effect of meteorological and chemical factors on the agreement between observations and predictions of fine aerosol composition in southwestern Ontario during BAQS-Met

NASA Astrophysics Data System (ADS)

Markovic, M. Z.; Hayden, K. L.; Murphy, J. G.; Makar, P. A.; Ellis, R. A.; Chang, R. Y.-W.; Slowik, J. G.; Mihele, C.; Brook, J.

2011-04-01

The Border Air Quality and Meteorology Study (BAQS-Met) was an intensive, collaborative field campaign during the summer of 2007 that investigated the effects of transboundary pollution, local pollution, and local meteorology on air quality in southwestern Ontario. This analysis focuses on the measurements of the inorganic constituents of particulate matter with diameter of less than 1 μm (PM1), with a specific emphasis on nitrate. We evaluate the ability of AURAMS, Environment Canada's chemical transport model, to represent regional air pollution in SW Ontario by comparing modelled aerosol inorganic chemical composition with measurements from Aerosol Mass Spectrometers (AMS) onboard the National Research Council (NRC) of Canada Twin Otter aircraft and at a ground site in Harrow, ON. The agreement between modelled and measured pNO3- at the ground site (observed mean (Mobs) = 0.50 μg m-3; modelled mean (Mmod) = 0.58 μg m-3; root mean square error (RSME) = 1.27 μg m-3) was better than aloft (Mobs = 0.32 μg m-3; Mmod = 0.09 μg m-3; RSME = 0.48 μg m-3). Possible reasons for discrepancies include errors in (i) emission inventories, (ii) atmospheric chemistry, (iii) predicted meteorological parameters, or (iv) gas/particle thermodynamics in the model framework. Using the inorganic thermodynamics model, ISORROPIA, in an offline mode, we find that the assumption of thermodynamic equilibrium is consistent with observations of gas and particle composition at Harrow. We develop a framework to assess the sensitivity of PM1 nitrate to meteorological and chemical parameters and find that errors in both the predictions of relative humidity and free ammonia (FA ≡ NH3(g) + pNH4+ - 2 · pSO42-) are responsible for the poor agreement between modelled and measured values.

The effect of meteorological and chemical factors on the agreement between observations and predictions of fine aerosol composition in Southwestern Ontario during BAQS-Met

NASA Astrophysics Data System (ADS)

Markovic, M. Z.; Hayden, K. L.; Murphy, J. G.; Makar, P. A.; Ellis, R. A.; Chang, R. Y.-W.; Slowik, J. G.; Mihele, C.; Brook, J.

2010-10-01

The Border Air Quality and Meteorology Study (BAQS-Met) was an intensive, collaborative field campaign during the summer of 2007 that investigated the effects of transboundary pollution, local pollution, and local meteorology on regional air quality in Southwestern Ontario. This analysis focuses on the measurements of the inorganic constituents of particulate matter with diameter of less than 1 μm (PM1), with a specific emphasis on nitrate. We evaluate the ability of AURAMS, the Environment Canada's chemical transport model, to represent regional air pollution in SW Ontario by comparing modelled aerosol inorganic chemical composition with measurements from Aerosol Mass Spectrometers (AMS) onboard the National Research Council (NRC) of Canada Twin Otter aircraft and at a ground site in Harrow, ON. The agreement between modelled and measured pNO3- at the ground site (observed mean (M_obs) = 0.50 μg m-3; modelled mean (M_mod) = 0.58 μg m-3; root mean square error (RSME) = 1.27 μg m-3) was better than aloft (M_obs = 0.32 μg m-3; M_mod = 0.09 μg m-3; RSME = 0.48 μg m-3). Possible reasons for discrepancies include errors in (i) emission inventories, (ii) atmospheric chemistry, (iii) predicted meteorological parameters, or (iv) gas/particle thermodynamics in the model framework. Using the inorganic thermodynamics model, ISORROPIA, in an offline mode, we find that the assumption of thermodynamic equilibrium is consistent with observations of gas and particle composition at Harrow. We develop a framework to assess the sensitivity of PM1 nitrate to meteorological and chemical parameters and find that errors in both the predictions of relative humidity and free ammonia (FA ≡ NH3(g) + NH4+ - SO42-) are responsible for the poor agreement between modelled and measured values.

Incorporating a prediction of postgrazing herbage mass into a whole-farm model for pasture-based dairy systems.

PubMed

Gregorini, P; Galli, J; Romera, A J; Levy, G; Macdonald, K A; Fernandez, H H; Beukes, P C

2014-07-01

The DairyNZ whole-farm model (WFM; DairyNZ, Hamilton, New Zealand) consists of a framework that links component models for animal, pastures, crops, and soils. The model was developed to assist with analysis and design of pasture-based farm systems. New (this work) and revised (e.g., cow, pasture, crops) component models can be added to the WFM, keeping the model flexible and up to date. Nevertheless, the WFM does not account for plant-animal relationships determining herbage-depletion dynamics. The user has to preset the maximum allowable level of herbage depletion [i.e., postgrazing herbage mass (residuals)] throughout the year. Because residuals have a direct effect on herbage regrowth, the WFM in its current form does not dynamically simulate the effect of grazing pressure on herbage depletion and consequent effect on herbage regrowth. The management of grazing pressure is a key component of pasture-based dairy systems. Thus, the main objective of the present work was to develop a new version of the WFM able to predict residuals, and thereby simulate related effects of grazing pressure dynamically at the farm scale. This objective was accomplished by incorporating a new component model into the WFM. This model represents plant-animal relationships, for example sward structure and herbage intake rate, and resulting level of herbage depletion. The sensitivity of the new version of the WFM was evaluated and then the new WFM was tested against an experimental data set previously used to evaluate the WFM and to illustrate the adequacy and improvement of the model development. Key outputs variables of the new version pertinent to this work (milk production, herbage dry matter intake, intake rate, harvesting efficiency, and residuals) responded acceptably to a range of input variables. The relative prediction errors for monthly and mean annual residual predictions were 20 and 5%, respectively. Monthly predictions of residuals had a line bias (1.5%), with a proportion of square root of mean square prediction error (RMSPE) due to random error of 97.5%. Predicted monthly herbage growth rates had a line bias of 2%, a proportion of RMSPE due to random error of 96%, and a concordance correlation coefficient of 0.87. Annual herbage production was predicted with an RMSPE of 531 (kg of herbage dry matter/ha per year), a line bias of 11%, a proportion of RMSPE due to random error of 80%, and relative prediction errors of 2%. Annual herbage dry matter intake per cow and hectare, both per year, were predicted with RMSPE, relative prediction error, and concordance correlation coefficient of 169 and 692kg of dry matter, 3 and 4%, and 0.91 and 0.87, respectively. These results indicate that predictions of the new WFM are relatively accurate and precise, with a conclusion that incorporating a plant-animal relationship model into the WFM allows for dynamic predictions of residuals and more realistic simulations of the effect of grazing pressure on herbage production and intake at the farm level without the intervention from the user. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

An analytical framework to assist decision makers in the use of forest ecosystem model predictions

USGS Publications Warehouse

Larocque, Guy R.; Bhatti, Jagtar S.; Ascough, J.C.; Liu, J.; Luckai, N.; Mailly, D.; Archambault, L.; Gordon, Andrew M.

2011-01-01

The predictions from most forest ecosystem models originate from deterministic simulations. However, few evaluation exercises for model outputs are performed by either model developers or users. This issue has important consequences for decision makers using these models to develop natural resource management policies, as they cannot evaluate the extent to which predictions stemming from the simulation of alternative management scenarios may result in significant environmental or economic differences. Various numerical methods, such as sensitivity/uncertainty analyses, or bootstrap methods, may be used to evaluate models and the errors associated with their outputs. However, the application of each of these methods carries unique challenges which decision makers do not necessarily understand; guidance is required when interpreting the output generated from each model. This paper proposes a decision flow chart in the form of an analytical framework to help decision makers apply, in an orderly fashion, different steps involved in examining the model outputs. The analytical framework is discussed with regard to the definition of problems and objectives and includes the following topics: model selection, identification of alternatives, modelling tasks and selecting alternatives for developing policy or implementing management scenarios. Its application is illustrated using an on-going exercise in developing silvicultural guidelines for a forest management enterprise in Ontario, Canada.

Student Errors in Dynamic Mathematical Environments

ERIC Educational Resources Information Center

Brown, Molly; Bossé, Michael J.; Chandler, Kayla

2016-01-01

This study investigates the nature of student errors in the context of problem solving and Dynamic Math Environments. This led to the development of the Problem Solving Action Identification Framework; this framework captures and defines all activities and errors associated with problem solving in a dynamic math environment. Found are three…

Development of a coupled hydrological - hydrodynamic model for probabilistic catchment flood inundation modelling

NASA Astrophysics Data System (ADS)

Quinn, Niall; Freer, Jim; Coxon, Gemma; Dunne, Toby; Neal, Jeff; Bates, Paul; Sampson, Chris; Smith, Andy; Parkin, Geoff

2017-04-01

Computationally efficient flood inundation modelling systems capable of representing important hydrological and hydrodynamic flood generating processes over relatively large regions are vital for those interested in flood preparation, response, and real time forecasting. However, such systems are currently not readily available. This can be particularly important where flood predictions from intense rainfall are considered as the processes leading to flooding often involve localised, non-linear spatially connected hillslope-catchment responses. Therefore, this research introduces a novel hydrological-hydraulic modelling framework for the provision of probabilistic flood inundation predictions across catchment to regional scales that explicitly account for spatial variability in rainfall-runoff and routing processes. Approaches have been developed to automate the provision of required input datasets and estimate essential catchment characteristics from freely available, national datasets. This is an essential component of the framework as when making predictions over multiple catchments or at relatively large scales, and where data is often scarce, obtaining local information and manually incorporating it into the model quickly becomes infeasible. An extreme flooding event in the town of Morpeth, NE England, in 2008 was used as a first case study evaluation of the modelling framework introduced. The results demonstrated a high degree of prediction accuracy when comparing modelled and reconstructed event characteristics for the event, while the efficiency of the modelling approach used enabled the generation of relatively large ensembles of realisations from which uncertainty within the prediction may be represented. This research supports previous literature highlighting the importance of probabilistic forecasting, particularly during extreme events, which can be often be poorly characterised or even missed by deterministic predictions due to the inherent uncertainty in any model application. Future research will aim to further evaluate the robustness of the approaches introduced by applying the modelling framework to a variety of historical flood events across UK catchments. Furthermore, the flexibility and efficiency of the framework is ideally suited to the examination of the propagation of errors through the model which will help gain a better understanding of the dominant sources of uncertainty currently impacting flood inundation predictions.

A study on the theoretical and practical accuracy of conoscopic holography-based surface measurements: toward image registration in minimally invasive surgery†

PubMed Central

Burgner, J.; Simpson, A. L.; Fitzpatrick, J. M.; Lathrop, R. A.; Herrell, S. D.; Miga, M. I.; Webster, R. J.

2013-01-01

Background Registered medical images can assist with surgical navigation and enable image-guided therapy delivery. In soft tissues, surface-based registration is often used and can be facilitated by laser surface scanning. Tracked conoscopic holography (which provides distance measurements) has been recently proposed as a minimally invasive way to obtain surface scans. Moving this technique from concept to clinical use requires a rigorous accuracy evaluation, which is the purpose of our paper. Methods We adapt recent non-homogeneous and anisotropic point-based registration results to provide a theoretical framework for predicting the accuracy of tracked distance measurement systems. Experiments are conducted a complex objects of defined geometry, an anthropomorphic kidney phantom and a human cadaver kidney. Results Experiments agree with model predictions, producing point RMS errors consistently < 1 mm, surface-based registration with mean closest point error < 1 mm in the phantom and a RMS target registration error of 0.8 mm in the human cadaver kidney. Conclusions Tracked conoscopic holography is clinically viable; it enables minimally invasive surface scan accuracy comparable to current clinical methods that require open surgery. PMID:22761086

Electric train energy consumption modeling

DOE PAGES

Wang, Jinghui; Rakha, Hesham A.

2017-05-01

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error ofmore » 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.« less

Electric train energy consumption modeling

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

Wang, Jinghui; Rakha, Hesham A.

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error ofmore » 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.« less

Calibration of Predictor Models Using Multiple Validation Experiments

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This paper presents a framework for calibrating computational models using data from several and possibly dissimilar validation experiments. The offset between model predictions and observations, which might be caused by measurement noise, model-form uncertainty, and numerical error, drives the process by which uncertainty in the models parameters is characterized. The resulting description of uncertainty along with the computational model constitute a predictor model. Two types of predictor models are studied: Interval Predictor Models (IPMs) and Random Predictor Models (RPMs). IPMs use sets to characterize uncertainty, whereas RPMs use random vectors. The propagation of a set through a model makes the response an interval valued function of the state, whereas the propagation of a random vector yields a random process. Optimization-based strategies for calculating both types of predictor models are proposed. Whereas the formulations used to calculate IPMs target solutions leading to the interval value function of minimal spread containing all observations, those for RPMs seek to maximize the models' ability to reproduce the distribution of observations. Regarding RPMs, we choose a structure for the random vector (i.e., the assignment of probability to points in the parameter space) solely dependent on the prediction error. As such, the probabilistic description of uncertainty is not a subjective assignment of belief, nor is it expected to asymptotically converge to a fixed value, but instead it casts the model's ability to reproduce the experimental data. This framework enables evaluating the spread and distribution of the predicted response of target applications depending on the same parameters beyond the validation domain.

Genomic Prediction Accounting for Residual Heteroskedasticity

PubMed Central

Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.

2015-01-01

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950

Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System

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

Smith, Kandler A; Saxon, Aron R; Keyser, Matthew A

Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity and resistance as the battery degrades. Across 9 aging test conditions frommore » 0oC to 55oC, the model predicts capacity fade with 1.4% RMS error and resistance growth with 15% RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.« less

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

Dall'Anese, Emiliano; Baker, Kyri; Summers, Tyler

The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative boundsmore » that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.« less

Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm's law

PubMed Central

Carbonell-Ballestero, M.; Garcia-Ramallo, E.; Montañez, R.; Rodriguez-Caso, C.; Macía, J.

2016-01-01

Synthetic biology seeks to envision living cells as a matter of engineering. However, increasing evidence suggests that the genetic load imposed by the incorporation of synthetic devices in a living organism introduces a sort of unpredictability in the design process. As a result, individual part characterization is not enough to predict the behavior of designed circuits and thus, a costly trial-error process is eventually required. In this work, we provide a new theoretical framework for the predictive treatment of the genetic load. We mathematically and experimentally demonstrate that dependences among genes follow a quantitatively predictable behavior. Our theory predicts the observed reduction of the expression of a given synthetic gene when an extra genetic load is introduced in the circuit. The theory also explains that such dependence qualitatively differs when the extra load is added either by transcriptional or translational modifications. We finally show that the limitation of the cellular resources for gene expression leads to a mathematical formulation that converges to an expression analogous to the Ohm's law for electric circuits. Similitudes and divergences with this law are outlined. Our work provides a suitable framework with predictive character for the design process of complex genetic devices in synthetic biology. PMID:26656950

The Robustness of Acoustic Analogies

NASA Technical Reports Server (NTRS)

Freund, J. B.; Lele, S. K.; Wei, M.

2004-01-01

Acoustic analogies for the prediction of flow noise are exact rearrangements of the flow equations N(right arrow q) = 0 into a nominal sound source S(right arrow q) and sound propagation operator L such that L(right arrow q) = S(right arrow q). In practice, the sound source is typically modeled and the propagation operator inverted to make predictions. Since the rearrangement is exact, any sufficiently accurate model of the source will yield the correct sound, so other factors must determine the merits of any particular formulation. Using data from a two-dimensional mixing layer direct numerical simulation (DNS), we evaluate the robustness of two analogy formulations to different errors intentionally introduced into the source. The motivation is that since S can not be perfectly modeled, analogies that are less sensitive to errors in S are preferable. Our assessment is made within the framework of Goldstein's generalized acoustic analogy, in which different choices of a base flow used in constructing L give different sources S and thus different analogies. A uniform base flow yields a Lighthill-like analogy, which we evaluate against a formulation in which the base flow is the actual mean flow of the DNS. The more complex mean flow formulation is found to be significantly more robust to errors in the energetic turbulent fluctuations, but its advantage is less pronounced when errors are made in the smaller scales.

Human Error and Commercial Aviation Accidents: A Comprehensive, Fine-Grained Analysis Using HFACS

DTIC Science & Technology

2006-07-01

Factors Figure 2. The HFACS framework. 3 practiced and seemingly automatic behaviors is that they are particularly susceptible to attention and/or memory...been included in most error frameworks, the third and final error form, perceptual errors, has received comparatively less attention . No less...operate safely. After all, just as not everyone can play linebacker for their favorite professional football team or be a concert pianist , not

A predictive model of nuclear power plant crew decision-making and performance in a dynamic simulation environment

NASA Astrophysics Data System (ADS)

Coyne, Kevin Anthony

The safe operation of complex systems such as nuclear power plants requires close coordination between the human operators and plant systems. In order to maintain an adequate level of safety following an accident or other off-normal event, the operators often are called upon to perform complex tasks during dynamic situations with incomplete information. The safety of such complex systems can be greatly improved if the conditions that could lead operators to make poor decisions and commit erroneous actions during these situations can be predicted and mitigated. The primary goal of this research project was the development and validation of a cognitive model capable of simulating nuclear plant operator decision-making during accident conditions. Dynamic probabilistic risk assessment methods can improve the prediction of human error events by providing rich contextual information and an explicit consideration of feedback arising from man-machine interactions. The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) shows promise for predicting situational contexts that might lead to human error events, particularly knowledge driven errors of commission. ADS-IDAC generates a discrete dynamic event tree (DDET) by applying simple branching rules that reflect variations in crew responses to plant events and system status changes. Branches can be generated to simulate slow or fast procedure execution speed, skipping of procedure steps, reliance on memorized information, activation of mental beliefs, variations in control inputs, and equipment failures. Complex operator mental models of plant behavior that guide crew actions can be represented within the ADS-IDAC mental belief framework and used to identify situational contexts that may lead to human error events. This research increased the capabilities of ADS-IDAC in several key areas. The ADS-IDAC computer code was improved to support additional branching events and provide a better representation of the IDAC cognitive model. An operator decision-making engine capable of responding to dynamic changes in situational context was implemented. The IDAC human performance model was fully integrated with a detailed nuclear plant model in order to realistically simulate plant accident scenarios. Finally, the improved ADS-IDAC model was calibrated, validated, and updated using actual nuclear plant crew performance data. This research led to the following general conclusions: (1) A relatively small number of branching rules are capable of efficiently capturing a wide spectrum of crew-to-crew variabilities. (2) Compared to traditional static risk assessment methods, ADS-IDAC can provide a more realistic and integrated assessment of human error events by directly determining the effect of operator behaviors on plant thermal hydraulic parameters. (3) The ADS-IDAC approach provides an efficient framework for capturing actual operator performance data such as timing of operator actions, mental models, and decision-making activities.

Analog ensemble and Bayesian regression techniques to improve the wind speed prediction during extreme storms in the NE U.S.

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Delle Monache, L.; Alessandrini, S.

2016-12-01

Accuracy of weather forecasts in Northeast U.S. has become very important in recent years, given the serious and devastating effects of extreme weather events. Despite the use of evolved forecasting tools and techniques strengthened by increased super-computing resources, the weather forecasting systems still have their limitations in predicting extreme events. In this study, we examine the combination of analog ensemble and Bayesian regression techniques to improve the prediction of storms that have impacted NE U.S., mostly defined by the occurrence of high wind speeds (i.e. blizzards, winter storms, hurricanes and thunderstorms). The predicted wind speed, wind direction and temperature by two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) are combined using the mentioned techniques, exploring various ways that those variables influence the minimization of the prediction error (systematic and random). This study is focused on retrospective simulations of 146 storms that affected the NE U.S. in the period 2005-2016. In order to evaluate the techniques, leave-one-out cross validation procedure was implemented regarding 145 storms as the training dataset. The analog ensemble method selects a set of past observations that corresponded to the best analogs of the numerical weather prediction and provides a set of ensemble members of the selected observation dataset. The set of ensemble members can then be used in a deterministic or probabilistic way. In the Bayesian regression framework, optimal variances are estimated for the training partition by minimizing the root mean square error and are applied to the out-of-sample storm. The preliminary results indicate a significant improvement in the statistical metrics of 10-m wind speed for 146 storms using both techniques (20-30% bias and error reduction in all observation-model pairs). In this presentation, we discuss the various combinations of atmospheric predictors and techniques and illustrate how the long record of predicted storms is valuable in the improvement of wind speed prediction.

Verifiable fault tolerance in measurement-based quantum computation

NASA Astrophysics Data System (ADS)

Fujii, Keisuke; Hayashi, Masahito

2017-09-01

Quantum systems, in general, cannot be simulated efficiently by a classical computer, and hence are useful for solving certain mathematical problems and simulating quantum many-body systems. This also implies, unfortunately, that verification of the output of the quantum systems is not so trivial, since predicting the output is exponentially hard. As another problem, the quantum system is very delicate for noise and thus needs an error correction. Here, we propose a framework for verification of the output of fault-tolerant quantum computation in a measurement-based model. In contrast to existing analyses on fault tolerance, we do not assume any noise model on the resource state, but an arbitrary resource state is tested by using only single-qubit measurements to verify whether or not the output of measurement-based quantum computation on it is correct. Verifiability is equipped by a constant time repetition of the original measurement-based quantum computation in appropriate measurement bases. Since full characterization of quantum noise is exponentially hard for large-scale quantum computing systems, our framework provides an efficient way to practically verify the experimental quantum error correction.

A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

NASA Astrophysics Data System (ADS)

Saleh, Firas; Ramaswamy, Venkatsundar; Georgas, Nickitas; Blumberg, Alan F.; Pullen, Julie

2016-07-01

This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ˜ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

Technology-induced errors. The current use of frameworks and models from the biomedical and life sciences literatures.

PubMed

Borycki, E M; Kushniruk, A W; Bellwood, P; Brender, J

2012-01-01

The objective of this paper is to examine the extent, range and scope to which frameworks, models and theories dealing with technology-induced error have arisen in the biomedical and life sciences literature as indexed by Medline®. To better understand the state of work in the area of technology-induced error involving frameworks, models and theories, the authors conducted a search of Medline® using selected key words identified from seminal articles in this research area. Articles were reviewed and those pertaining to frameworks, models or theories dealing with technology-induced error were further reviewed by two researchers. All articles from Medline® from its inception to April of 2011 were searched using the above outlined strategy. 239 citations were returned. Each of the abstracts for the 239 citations were reviewed by two researchers. Eleven articles met the criteria based on abstract review. These 11 articles were downloaded for further in-depth review. The majority of the articles obtained describe frameworks and models with reference to theories developed in other literatures outside of healthcare. The papers were grouped into several areas. It was found that articles drew mainly from three literatures: 1) the human factors literature (including human-computer interaction and cognition), 2) the organizational behavior/sociotechnical literature, and 3) the software engineering literature. A variety of frameworks and models were found in the biomedical and life sciences literatures. These frameworks and models drew upon and extended frameworks, models and theoretical perspectives that have emerged in other literatures. These frameworks and models are informing an emerging line of research in health and biomedical informatics involving technology-induced errors in healthcare.

A Conceptual Framework for Error Remediation with Multiple External Representations Applied to Learning Objects

ERIC Educational Resources Information Center

Leite, Maici Duarte; Marczal, Diego; Pimentel, Andrey Ricardo; Direne, Alexandre Ibrahim

2014-01-01

This paper presents the application of some concepts of Intelligent Tutoring Systems (ITS) to elaborate a conceptual framework that uses the remediation of errors with Multiple External Representations (MERs) in Learning Objects (LO). To this is demonstrated a development of LO for teaching the Pythagorean Theorem through this framework. This…

Time series analysis of temporal networks

NASA Astrophysics Data System (ADS)

Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

2016-01-01

A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.

Adaptive plasticity in speech perception: Effects of external information and internal predictions.

PubMed

Guediche, Sara; Fiez, Julie A; Holt, Lori L

2016-07-01

When listeners encounter speech under adverse listening conditions, adaptive adjustments in perception can improve comprehension over time. In some cases, these adaptive changes require the presence of external information that disambiguates the distorted speech signals, whereas in other cases mere exposure is sufficient. Both external (e.g., written feedback) and internal (e.g., prior word knowledge) sources of information can be used to generate predictions about the correct mapping of a distorted speech signal. We hypothesize that these predictions provide a basis for determining the discrepancy between the expected and actual speech signal that can be used to guide adaptive changes in perception. This study provides the first empirical investigation that manipulates external and internal factors through (a) the availability of explicit external disambiguating information via the presence or absence of postresponse orthographic information paired with a repetition of the degraded stimulus, and (b) the accuracy of internally generated predictions; an acoustic distortion is introduced either abruptly or incrementally. The results demonstrate that the impact of external information on adaptive plasticity is contingent upon whether the intelligibility of the stimuli permits accurate internally generated predictions during exposure. External information sources enhance adaptive plasticity only when input signals are severely degraded and cannot reliably access internal predictions. This is consistent with a computational framework for adaptive plasticity in which error-driven supervised learning relies on the ability to compute sensory prediction error signals from both internal and external sources of information. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Adaptive plasticity in speech perception: effects of external information and internal predictions

PubMed Central

Guediche, Sara; Fiez, Julie A.; Holt, Lori L.

2016-01-01

When listeners encounter speech under adverse listening conditions, adaptive adjustments in perception can improve comprehension over time. In some cases, these adaptive changes require the presence of external information that disambiguates the distorted speech signals, whereas in other cases mere exposure is sufficient. Both external (e.g. written feedback) and internal (e.g., prior word knowledge) sources of information can be used to generate predictions about the correct mapping of a distorted speech signal. We hypothesize that these predictions provide a basis for determining the discrepancy between the expected and actual speech signal that can be used to guide adaptive changes in perception. This study provides the first empirical investigation that manipulates external and internal factors through 1) the availability of explicit external disambiguating information via the presence or absence of post-response orthographic information paired with a repetition of the degraded stimulus, and 2) the accuracy of internally-generated predictions; an acoustic distortion is introduced either abruptly or incrementally. The results demonstrate that the impact of external information on adaptive plasticity is contingent upon whether the intelligibility of the stimuli permits accurate internally-generated predictions during exposure. External information sources enhance adaptive plasticity only when input signals are severely degraded and cannot reliably access internal predictions. This is consistent with a computational framework for adaptive plasticity in which error-driven supervised learning relies on the ability to compute sensory prediction error signals from both internal and external sources of information. PMID:26854531

A Hybrid Physics-Based Data-Driven Approach for Point-Particle Force Modeling

NASA Astrophysics Data System (ADS)

Moore, Chandler; Akiki, Georges; Balachandar, S.

2017-11-01

This study improves upon the physics-based pairwise interaction extended point-particle (PIEP) model. The PIEP model leverages a physical framework to predict fluid mediated interactions between solid particles. While the PIEP model is a powerful tool, its pairwise assumption leads to increased error in flows with high particle volume fractions. To reduce this error, a regression algorithm is used to model the differences between the current PIEP model's predictions and the results of direct numerical simulations (DNS) for an array of monodisperse solid particles subjected to various flow conditions. The resulting statistical model and the physical PIEP model are superimposed to construct a hybrid, physics-based data-driven PIEP model. It must be noted that the performance of a pure data-driven approach without the model-form provided by the physical PIEP model is substantially inferior. The hybrid model's predictive capabilities are analyzed using more DNS. In every case tested, the hybrid PIEP model's prediction are more accurate than those of physical PIEP model. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1315138 and the U.S. DOE, NNSA, ASC Program, as a Cooperative Agreement under Contract No. DE-NA0002378.

Adaptive Kalman filter based on variance component estimation for the prediction of ionospheric delay in aiding the cycle slip repair of GNSS triple-frequency signals

NASA Astrophysics Data System (ADS)

Chang, Guobin; Xu, Tianhe; Yao, Yifei; Wang, Qianxin

2018-01-01

In order to incorporate the time smoothness of ionospheric delay to aid the cycle slip detection, an adaptive Kalman filter is developed based on variance component estimation. The correlations between measurements at neighboring epochs are fully considered in developing a filtering algorithm for colored measurement noise. Within this filtering framework, epoch-differenced ionospheric delays are predicted. Using this prediction, the potential cycle slips are repaired for triple-frequency signals of global navigation satellite systems. Cycle slips are repaired in a stepwise manner; i.e., for two extra wide lane combinations firstly and then for the third frequency. In the estimation for the third frequency, a stochastic model is followed in which the correlations between the ionospheric delay prediction errors and the errors in the epoch-differenced phase measurements are considered. The implementing details of the proposed method are tabulated. A real BeiDou Navigation Satellite System data set is used to check the performance of the proposed method. Most cycle slips, no matter trivial or nontrivial, can be estimated in float values with satisfactorily high accuracy and their integer values can hence be correctly obtained by simple rounding. To be more specific, all manually introduced nontrivial cycle slips are correctly repaired.

A Framework of Covariance Projection on Constraint Manifold for Data Fusion.

PubMed

Bakr, Muhammad Abu; Lee, Sukhan

2018-05-17

A general framework of data fusion is presented based on projecting the probability distribution of true states and measurements around the predicted states and actual measurements onto the constraint manifold. The constraint manifold represents the constraints to be satisfied among true states and measurements, which is defined in the extended space with all the redundant sources of data such as state predictions and measurements considered as independent variables. By the general framework, we mean that it is able to fuse any correlated data sources while directly incorporating constraints and identifying inconsistent data without any prior information. The proposed method, referred to here as the Covariance Projection (CP) method, provides an unbiased and optimal solution in the sense of minimum mean square error (MMSE), if the projection is based on the minimum weighted distance on the constraint manifold. The proposed method not only offers a generalization of the conventional formula for handling constraints and data inconsistency, but also provides a new insight into data fusion in terms of a geometric-algebraic point of view. Simulation results are provided to show the effectiveness of the proposed method in handling constraints and data inconsistency.

Quantifying drivers of wild pig movement across multiple spatial and temporal scales

USGS Publications Warehouse

Kay, Shannon L.; Fischer, Justin W.; Monaghan, Andrew J.; Beasley, James C; Boughton, Raoul; Campbell, Tyler A; Cooper, Susan M; Ditchkoff, Stephen S.; Hartley, Stephen B.; Kilgo, John C; Wisely, Samantha M; Wyckoff, A Christy; Vercauteren, Kurt C.; Pipen, Kim M

2017-01-01

The analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.

Comparative analysis on the selection of number of clusters in community detection

NASA Astrophysics Data System (ADS)

Kawamoto, Tatsuro; Kabashima, Yoshiyuki

2018-02-01

We conduct a comparative analysis on various estimates of the number of clusters in community detection. An exhaustive comparison requires testing of all possible combinations of frameworks, algorithms, and assessment criteria. In this paper we focus on the framework based on a stochastic block model, and investigate the performance of greedy algorithms, statistical inference, and spectral methods. For the assessment criteria, we consider modularity, map equation, Bethe free energy, prediction errors, and isolated eigenvalues. From the analysis, the tendency of overfit and underfit that the assessment criteria and algorithms have becomes apparent. In addition, we propose that the alluvial diagram is a suitable tool to visualize statistical inference results and can be useful to determine the number of clusters.

Rules and mechanisms for efficient two-stage learning in neural circuits.

PubMed

Teşileanu, Tiberiu; Ölveczky, Bence; Balasubramanian, Vijay

2017-04-04

Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in 'tutor' circuits ( e.g., LMAN) should match plasticity mechanisms in 'student' circuits ( e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.

Minimization of model representativity errors in identification of point source emission from atmospheric concentration measurements

NASA Astrophysics Data System (ADS)

Sharan, Maithili; Singh, Amit Kumar; Singh, Sarvesh Kumar

2017-11-01

Estimation of an unknown atmospheric release from a finite set of concentration measurements is considered an ill-posed inverse problem. Besides ill-posedness, the estimation process is influenced by the instrumental errors in the measured concentrations and model representativity errors. The study highlights the effect of minimizing model representativity errors on the source estimation. This is described in an adjoint modelling framework and followed in three steps. First, an estimation of point source parameters (location and intensity) is carried out using an inversion technique. Second, a linear regression relationship is established between the measured concentrations and corresponding predicted using the retrieved source parameters. Third, this relationship is utilized to modify the adjoint functions. Further, source estimation is carried out using these modified adjoint functions to analyse the effect of such modifications. The process is tested for two well known inversion techniques, called renormalization and least-square. The proposed methodology and inversion techniques are evaluated for a real scenario by using concentrations measurements from the Idaho diffusion experiment in low wind stable conditions. With both the inversion techniques, a significant improvement is observed in the retrieval of source estimation after minimizing the representativity errors.

Fraction of a dose absorbed estimation for structurally diverse low solubility compounds.

PubMed

Sugano, Kiyohiko

2011-02-28

The purpose of the present study was to investigate the prediction accuracy of the fully mechanistic gastrointestinal unified theoretical (GUT) framework for in vivo oral absorption of low solubility drugs. Solubility in biorelevant media, molecular weight, logP(oct), pK(a), Caco-2 permeability, dose and particle size were used as the input parameters. To neglect the effect of the low stomach pH on dissolution of a drug, the fraction of a dose absorbed (Fa%) of undissociable and free acids were used. In addition, Fa% of free base drugs with the high pH stomach was also included to increase the number of model drugs. In total twenty nine structurally diverse compounds were used as the model drugs. Fa% data at several doses and particle sizes in humans and dogs were collated from the literature (total 110 Fa% data). In approximately 80% cases, the prediction error was within 2 fold, suggesting that the GUT framework has practical predictability for drug discovery, but not for drug development. The GUT framework appropriately captured the dose and particle size dependency of Fa% as the particle drifting effect was taken into account. It should be noted that the present validation results cannot be applied for salt form cases and other special formulations such as solid dispersions and emulsion formulations. Copyright © 2010 Elsevier B.V. All rights reserved.

Dopamine reward prediction error coding.

PubMed

Schultz, Wolfram

2016-03-01

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards-an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.

Dopamine reward prediction error coding

PubMed Central

Schultz, Wolfram

2016-01-01

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards—an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware. PMID:27069377

Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model

DOE PAGES

Safta, C.; Ricciuto, Daniel M.; Sargsyan, Khachik; ...

2015-07-01

In this paper we propose a probabilistic framework for an uncertainty quantification (UQ) study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. A global sensitivity analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and a statistical model error term to calibrate the parameters of DALEC using net ecosystem exchange (NEE) observations at the Harvard Forest site. The calibration results are employedmore » in the second part of the paper to assess the predictive skill of the model via posterior predictive checks.« less

Ensemble Kalman Filter Data Assimilation in a Solar Dynamo Model

NASA Astrophysics Data System (ADS)

Dikpati, M.

2017-12-01

Despite great advancement in solar dynamo models since the first model by Parker in 1955, there remain many challenges in the quest to build a dynamo-based prediction scheme that can accurately predict the solar cycle features. One of these challenges is to implement modern data assimilation techniques, which have been used in the oceanic and atmospheric prediction models. Development of data assimilation in solar models are in the early stages. Recently, observing system simulation experiments (OSSE's) have been performed using Ensemble Kalman Filter data assimilation, in the framework of Data Assimilation Research Testbed of NCAR (NCAR-DART), for estimating parameters in a solar dynamo model. I will demonstrate how the selection of ensemble size, number of observations, amount of error in observations and the choice of assimilation interval play important role in parameter estimation. I will also show how the results of parameter reconstruction improve when accuracy in low-latitude observations is increased, despite large error in polar region data. I will then describe how implementation of data assimilation in a solar dynamo model can bring more accuracy in the prediction of polar fields in North and South hemispheres during the declining phase of cycle 24. Recent evidence indicates that the strength of the Sun's polar field during the cycle minima might be a reliable predictor for the next sunspot cycle's amplitude; therefore it is crucial to accurately predict the polar field strength and pattern.

Towards Automated Structure-Based NMR Resonance Assignment

NASA Astrophysics Data System (ADS)

Jang, Richard; Gao, Xin; Li, Ming

We propose a general framework for solving the structure-based NMR backbone resonance assignment problem. The core is a novel 0-1 integer programming model that can start from a complete or partial assignment, generate multiple assignments, and model not only the assignment of spins to residues, but also pairwise dependencies consisting of pairs of spins to pairs of residues. It is still a challenge for automated resonance assignment systems to perform the assignment directly from spectra without any manual intervention. To test the feasibility of this for structure-based assignment, we integrated our system with our automated peak picking and sequence-based resonance assignment system to obtain an assignment for the protein TM1112 with 91% recall and 99% precision without manual intervention. Since using a known structure has the potential to allow one to use only N-labeled NMR data and avoid the added expense of using C-labeled data, we work towards the goal of automated structure-based assignment using only such labeled data. Our system reduced the assignment error of Xiong-Pandurangan-Bailey-Kellogg's contact replacement (CR) method, which to our knowledge is the most error-tolerant method for this problem, by 5 folds on average. By using an iterative algorithm, our system has the added capability of using the NOESY data to correct assignment errors due to errors in predicting the amino acid and secondary structure type of each spin system. On a publicly available data set for Ubiquitin, where the type prediction accuracy is 83%, we achieved 91% assignment accuracy, compared to the 59% accuracy that was obtained without correcting for typing errors.

Predicting Classifier Performance with Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer

PubMed Central

Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant

2015-01-01

Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets. PMID:25993029

Robust model predictive control for multi-step short range spacecraft rendezvous

NASA Astrophysics Data System (ADS)

Zhu, Shuyi; Sun, Ran; Wang, Jiaolong; Wang, Jihe; Shao, Xiaowei

2018-07-01

This work presents a robust model predictive control (MPC) approach for the multi-step short range spacecraft rendezvous problem. During the specific short range phase concerned, the chaser is supposed to be initially outside the line-of-sight (LOS) cone. Therefore, the rendezvous process naturally includes two steps: the first step is to transfer the chaser into the LOS cone and the second step is to transfer the chaser into the aimed region with its motion confined within the LOS cone. A novel MPC framework named after Mixed MPC (M-MPC) is proposed, which is the combination of the Variable-Horizon MPC (VH-MPC) framework and the Fixed-Instant MPC (FI-MPC) framework. The M-MPC framework enables the optimization for the two steps to be implemented jointly rather than to be separated factitiously, and its computation workload is acceptable for the usually low-power processors onboard spacecraft. Then considering that disturbances including modeling error, sensor noise and thrust uncertainty may induce undesired constraint violations, a robust technique is developed and it is attached to the above M-MPC framework to form a robust M-MPC approach. The robust technique is based on the chance-constrained idea, which ensures that constraints can be satisfied with a prescribed probability. It improves the robust technique proposed by Gavilan et al., because it eliminates the unnecessary conservativeness by explicitly incorporating known statistical properties of the navigation uncertainty. The efficacy of the robust M-MPC approach is shown in a simulation study.

A circadian rhythm in skill-based errors in aviation maintenance.

PubMed

Hobbs, Alan; Williamson, Ann; Van Dongen, Hans P A

2010-07-01

In workplaces where activity continues around the clock, human error has been observed to exhibit a circadian rhythm, with a characteristic peak in the early hours of the morning. Errors are commonly distinguished by the nature of the underlying cognitive failure, particularly the level of intentionality involved in the erroneous action. The Skill-Rule-Knowledge (SRK) framework of Rasmussen is used widely in the study of industrial errors and accidents. The SRK framework describes three fundamental types of error, according to whether behavior is under the control of practiced sensori-motor skill routines with minimal conscious awareness; is guided by implicit or explicit rules or expertise; or where the planning of actions requires the conscious application of domain knowledge. Up to now, examinations of circadian patterns of industrial errors have not distinguished between different types of error. Consequently, it is not clear whether all types of error exhibit the same circadian rhythm. A survey was distributed to aircraft maintenance personnel in Australia. Personnel were invited to anonymously report a safety incident and were prompted to describe, in detail, the human involvement (if any) that contributed to it. A total of 402 airline maintenance personnel reported an incident, providing 369 descriptions of human error in which the time of the incident was reported and sufficient detail was available to analyze the error. Errors were categorized using a modified version of the SRK framework, in which errors are categorized as skill-based, rule-based, or knowledge-based, or as procedure violations. An independent check confirmed that the SRK framework had been applied with sufficient consistency and reliability. Skill-based errors were the most common form of error, followed by procedure violations, rule-based errors, and knowledge-based errors. The frequency of errors was adjusted for the estimated proportion of workers present at work/each hour of the day, and the 24 h pattern of each error type was examined. Skill-based errors exhibited a significant circadian rhythm, being most prevalent in the early hours of the morning. Variation in the frequency of rule-based errors, knowledge-based errors, and procedure violations over the 24 h did not reach statistical significance. The results suggest that during the early hours of the morning, maintenance technicians are at heightened risk of "absent minded" errors involving failures to execute action plans as intended.

Human errors and violations in computer and information security: the viewpoint of network administrators and security specialists.

PubMed

Kraemer, Sara; Carayon, Pascale

2007-03-01

This paper describes human errors and violations of end users and network administration in computer and information security. This information is summarized in a conceptual framework for examining the human and organizational factors contributing to computer and information security. This framework includes human error taxonomies to describe the work conditions that contribute adversely to computer and information security, i.e. to security vulnerabilities and breaches. The issue of human error and violation in computer and information security was explored through a series of 16 interviews with network administrators and security specialists. The interviews were audio taped, transcribed, and analyzed by coding specific themes in a node structure. The result is an expanded framework that classifies types of human error and identifies specific human and organizational factors that contribute to computer and information security. Network administrators tended to view errors created by end users as more intentional than unintentional, while errors created by network administrators as more unintentional than intentional. Organizational factors, such as communication, security culture, policy, and organizational structure, were the most frequently cited factors associated with computer and information security.

Flexible Modeling of Epidemics with an Empirical Bayes Framework

PubMed Central

Brooks, Logan C.; Farrow, David C.; Hyun, Sangwon; Tibshirani, Ryan J.; Rosenfeld, Roni

2015-01-01

Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic’s behavior, policy makers can design and implement more effective countermeasures. This past year, the Centers for Disease Control and Prevention hosted the “Predict the Influenza Season Challenge”, with the task of predicting key epidemiological measures for the 2013–2014 U.S. influenza season with the help of digital surveillance data. We developed a framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework, and applied it to predict the weekly percentage of outpatient doctors visits for influenza-like illness, and the season onset, duration, peak time, and peak height, with and without using Google Flu Trends data. Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, tailoring these models to certain types of surveillance data can be challenging, and overly complex models with many parameters can compromise forecasting ability. Our approach instead produces possibilities for the epidemic curve of the season of interest using modified versions of data from previous seasons, allowing for reasonable variations in the timing, pace, and intensity of the seasonal epidemics, as well as noise in observations. Since the framework does not make strict domain-specific assumptions, it can easily be applied to some other diseases with seasonal epidemics. This method produces a complete posterior distribution over epidemic curves, rather than, for example, solely point predictions of forecasting targets. We report prospective influenza-like-illness forecasts made for the 2013–2014 U.S. influenza season, and compare the framework’s cross-validated prediction error on historical data to that of a variety of simpler baseline predictors. PMID:26317693

Machine Learning for Discriminating Quantum Measurement Trajectories and Improving Readout.

PubMed

Magesan, Easwar; Gambetta, Jay M; Córcoles, A D; Chow, Jerry M

2015-05-22

Current methods for classifying measurement trajectories in superconducting qubit systems produce fidelities systematically lower than those predicted by experimental parameters. Here, we place current classification methods within the framework of machine learning (ML) algorithms and improve on them by investigating more sophisticated ML approaches. We find that nonlinear algorithms and clustering methods produce significantly higher assignment fidelities that help close the gap to the fidelity possible under ideal noise conditions. Clustering methods group trajectories into natural subsets within the data, which allows for the diagnosis of systematic errors. We find large clusters in the data associated with T1 processes and show these are the main source of discrepancy between our experimental and ideal fidelities. These error diagnosis techniques help provide a path forward to improve qubit measurements.

Governing Laws of Complex System Predictability under Co-evolving Uncertainty Sources: Theory and Nonlinear Geophysical Applications

NASA Astrophysics Data System (ADS)

Perdigão, R. A. P.

2017-12-01

Predictability assessments are traditionally made on a case-by-case basis, often by running the particular model of interest with randomly perturbed initial/boundary conditions and parameters, producing computationally expensive ensembles. These approaches provide a lumped statistical view of uncertainty evolution, without eliciting the fundamental processes and interactions at play in the uncertainty dynamics. In order to address these limitations, we introduce a systematic dynamical framework for predictability assessment and forecast, by analytically deriving governing equations of predictability in terms of the fundamental architecture of dynamical systems, independent of any particular problem under consideration. The framework further relates multiple uncertainty sources along with their coevolutionary interplay, enabling a comprehensive and explicit treatment of uncertainty dynamics along time, without requiring the actual model to be run. In doing so, computational resources are freed and a quick and effective a-priori systematic dynamic evaluation is made of predictability evolution and its challenges, including aspects in the model architecture and intervening variables that may require optimization ahead of initiating any model runs. It further brings out universal dynamic features in the error dynamics elusive to any case specific treatment, ultimately shedding fundamental light on the challenging issue of predictability. The formulated approach, framed with broad mathematical physics generality in mind, is then implemented in dynamic models of nonlinear geophysical systems with various degrees of complexity, in order to evaluate their limitations and provide informed assistance on how to optimize their design and improve their predictability in fundamental dynamical terms.

Genomic Prediction Accounting for Residual Heteroskedasticity.

PubMed

Ou, Zhining; Tempelman, Robert J; Steibel, Juan P; Ernst, Catherine W; Bates, Ronald O; Bello, Nora M

2015-11-12

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. Copyright © 2016 Ou et al.

Intercomparison of Streamflow Simulations between WRF-Hydro and Hydrology Laboratory-Research Distributed Hydrologic Model Frameworks

NASA Astrophysics Data System (ADS)

KIM, J.; Smith, M. B.; Koren, V.; Salas, F.; Cui, Z.; Johnson, D.

2017-12-01

The National Oceanic and Atmospheric Administration (NOAA)-National Weather Service (NWS) developed the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) framework as an initial step towards spatially distributed modeling at River Forecast Centers (RFCs). Recently, the NOAA/NWS worked with the National Center for Atmospheric Research (NCAR) to implement the National Water Model (NWM) for nationally-consistent water resources prediction. The NWM is based on the WRF-Hydro framework and is run at a 1km spatial resolution and 1-hour time step over the contiguous United States (CONUS) and contributing areas in Canada and Mexico. In this study, we compare streamflow simulations from HL-RDHM and WRF-Hydro to observations from 279 USGS stations. For streamflow simulations, HL-RDHM is run on 4km grids with the temporal resolution of 1 hour for a 5-year period (Water Years 2008-2012), using a priori parameters provided by NOAA-NWS. The WRF-Hydro streamflow simulations for the same time period are extracted from NCAR's 23 retrospective run of the NWM (version 1.0) over CONUS based on 1km grids. We choose 279 USGS stations which are relatively less affected by dams or reservoirs, in the domains of six different RFCs. We use the daily average values of simulations and observations for the convenience of comparison. The main purpose of this research is to evaluate how HL-RDHM and WRF-Hydro perform at USGS gauge stations. We compare daily time-series of observations and both simulations, and calculate the error values using a variety of error functions. Using these plots and error values, we evaluate the performances of HL-RDHM and WRF-Hydro models. Our results show a mix of model performance across geographic regions.

Financial errors in dementia: Testing a neuroeconomic conceptual framework

PubMed Central

Chiong, Winston; Hsu, Ming; Wudka, Danny; Miller, Bruce L.; Rosen, Howard J.

2013-01-01

Financial errors by patients with dementia can have devastating personal and family consequences. We developed and evaluated a neuroeconomic conceptual framework for understanding financial errors across different dementia syndromes, using a systematic, retrospective, blinded chart review of demographically-balanced cohorts of patients with Alzheimer’s disease (AD, n=100) and behavioral variant frontotemporal dementia (bvFTD, n=50). Reviewers recorded specific reports of financial errors according to a conceptual framework identifying patient cognitive and affective characteristics, and contextual influences, conferring susceptibility to each error. Specific financial errors were reported for 49% of AD and 70% of bvFTD patients (p = 0.012). AD patients were more likely than bvFTD patients to make amnestic errors (p< 0.001), while bvFTD patients were more likely to spend excessively (p = 0.004) and to exhibit other behaviors consistent with diminished sensitivity to losses and other negative outcomes (p< 0.001). Exploratory factor analysis identified a social/affective vulnerability factor associated with errors in bvFTD, and a cognitive vulnerability factor associated with errors in AD. Our findings highlight the frequency and functional importance of financial errors as symptoms of AD and bvFTD. A conceptual model derived from neuroeconomic literature identifies factors that influence vulnerability to different types of financial error in different dementia syndromes, with implications for early diagnosis and subsequent risk prevention. PMID:23550884

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

PubMed Central

Xie, Weihong; Yu, Yang

2017-01-01

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly. PMID:29124062

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach.

PubMed

Liang, Fan; Xie, Weihong; Yu, Yang

2017-01-01

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively "switch" from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.

Accounting for uncertain fault geometry in earthquake source inversions - I: theory and simplified application

NASA Astrophysics Data System (ADS)

Ragon, Théa; Sladen, Anthony; Simons, Mark

2018-05-01

The ill-posed nature of earthquake source estimation derives from several factors including the quality and quantity of available observations and the fidelity of our forward theory. Observational errors are usually accounted for in the inversion process. Epistemic errors, which stem from our simplified description of the forward problem, are rarely dealt with despite their potential to bias the estimate of a source model. In this study, we explore the impact of uncertainties related to the choice of a fault geometry in source inversion problems. The geometry of a fault structure is generally reduced to a set of parameters, such as position, strike and dip, for one or a few planar fault segments. While some of these parameters can be solved for, more often they are fixed to an uncertain value. We propose a practical framework to address this limitation by following a previously implemented method exploring the impact of uncertainties on the elastic properties of our models. We develop a sensitivity analysis to small perturbations of fault dip and position. The uncertainties in fault geometry are included in the inverse problem under the formulation of the misfit covariance matrix that combines both prediction and observation uncertainties. We validate this approach with the simplified case of a fault that extends infinitely along strike, using both Bayesian and optimization formulations of a static inversion. If epistemic errors are ignored, predictions are overconfident in the data and source parameters are not reliably estimated. In contrast, inclusion of uncertainties in fault geometry allows us to infer a robust posterior source model. Epistemic uncertainties can be many orders of magnitude larger than observational errors for great earthquakes (Mw > 8). Not accounting for uncertainties in fault geometry may partly explain observed shallow slip deficits for continental earthquakes. Similarly, ignoring the impact of epistemic errors can also bias estimates of near surface slip and predictions of tsunamis induced by megathrust earthquakes. (Mw > 8)

Adaptive invasive species distribution models: A framework for modeling incipient invasions

USGS Publications Warehouse

Uden, Daniel R.; Allen, Craig R.; Angeler, David G.; Corral, Lucia; Fricke, Kent A.

2015-01-01

The utilization of species distribution model(s) (SDM) for approximating, explaining, and predicting changes in species’ geographic locations is increasingly promoted for proactive ecological management. Although frameworks for modeling non-invasive species distributions are relatively well developed, their counterparts for invasive species—which may not be at equilibrium within recipient environments and often exhibit rapid transformations—are lacking. Additionally, adaptive ecological management strategies address the causes and effects of biological invasions and other complex issues in social-ecological systems. We conducted a review of biological invasions, species distribution models, and adaptive practices in ecological management, and developed a framework for adaptive, niche-based, invasive species distribution model (iSDM) development and utilization. This iterative, 10-step framework promotes consistency and transparency in iSDM development, allows for changes in invasive drivers and filters, integrates mechanistic and correlative modeling techniques, balances the avoidance of type 1 and type 2 errors in predictions, encourages the linking of monitoring and management actions, and facilitates incremental improvements in models and management across space, time, and institutional boundaries. These improvements are useful for advancing coordinated invasive species modeling, management and monitoring from local scales to the regional, continental and global scales at which biological invasions occur and harm native ecosystems and economies, as well as for anticipating and responding to biological invasions under continuing global change.

A reverberation-time-aware DNN approach leveraging spatial information for microphone array dereverberation

NASA Astrophysics Data System (ADS)

Wu, Bo; Yang, Minglei; Li, Kehuang; Huang, Zhen; Siniscalchi, Sabato Marco; Wang, Tong; Lee, Chin-Hui

2017-12-01

A reverberation-time-aware deep-neural-network (DNN)-based multi-channel speech dereverberation framework is proposed to handle a wide range of reverberation times (RT60s). There are three key steps in designing a robust system. First, to accomplish simultaneous speech dereverberation and beamforming, we propose a framework, namely DNNSpatial, by selectively concatenating log-power spectral (LPS) input features of reverberant speech from multiple microphones in an array and map them into the expected output LPS features of anechoic reference speech based on a single deep neural network (DNN). Next, the temporal auto-correlation function of received signals at different RT60s is investigated to show that RT60-dependent temporal-spatial contexts in feature selection are needed in the DNNSpatial training stage in order to optimize the system performance in diverse reverberant environments. Finally, the RT60 is estimated to select the proper temporal and spatial contexts before feeding the log-power spectrum features to the trained DNNs for speech dereverberation. The experimental evidence gathered in this study indicates that the proposed framework outperforms the state-of-the-art signal processing dereverberation algorithm weighted prediction error (WPE) and conventional DNNSpatial systems without taking the reverberation time into account, even for extremely weak and severe reverberant conditions. The proposed technique generalizes well to unseen room size, array geometry and loudspeaker position, and is robust to reverberation time estimation error.

Design framework for spherical microphone and loudspeaker arrays in a multiple-input multiple-output system.

PubMed

Morgenstern, Hai; Rafaely, Boaz; Noisternig, Markus

2017-03-01

Spherical microphone arrays (SMAs) and spherical loudspeaker arrays (SLAs) facilitate the study of room acoustics due to the three-dimensional analysis they provide. More recently, systems that combine both arrays, referred to as multiple-input multiple-output (MIMO) systems, have been proposed due to the added spatial diversity they facilitate. The literature provides frameworks for designing SMAs and SLAs separately, including error analysis from which the operating frequency range (OFR) of an array is defined. However, such a framework does not exist for the joint design of a SMA and a SLA that comprise a MIMO system. This paper develops a design framework for MIMO systems based on a model that addresses errors and highlights the importance of a matched design. Expanding on a free-field assumption, errors are incorporated separately for each array and error bounds are defined, facilitating error analysis for the system. The dependency of the error bounds on the SLA and SMA parameters is studied and it is recommended that parameters should be chosen to assure matched OFRs of the arrays in MIMO system design. A design example is provided, demonstrating the superiority of a matched system over an unmatched system in the synthesis of directional room impulse responses.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

Set membership experimental design for biological systems.

PubMed

Marvel, Skylar W; Williams, Cranos M

2012-03-21

Experimental design approaches for biological systems are needed to help conserve the limited resources that are allocated for performing experiments. The assumptions used when assigning probability density functions to characterize uncertainty in biological systems are unwarranted when only a small number of measurements can be obtained. In these situations, the uncertainty in biological systems is more appropriately characterized in a bounded-error context. Additionally, effort must be made to improve the connection between modelers and experimentalists by relating design metrics to biologically relevant information. Bounded-error experimental design approaches that can assess the impact of additional measurements on model uncertainty are needed to identify the most appropriate balance between the collection of data and the availability of resources. In this work we develop a bounded-error experimental design framework for nonlinear continuous-time systems when few data measurements are available. This approach leverages many of the recent advances in bounded-error parameter and state estimation methods that use interval analysis to generate parameter sets and state bounds consistent with uncertain data measurements. We devise a novel approach using set-based uncertainty propagation to estimate measurement ranges at candidate time points. We then use these estimated measurements at the candidate time points to evaluate which candidate measurements furthest reduce model uncertainty. A method for quickly combining multiple candidate time points is presented and allows for determining the effect of adding multiple measurements. Biologically relevant metrics are developed and used to predict when new data measurements should be acquired, which system components should be measured and how many additional measurements should be obtained. The practicability of our approach is illustrated with a case study. This study shows that our approach is able to 1) identify candidate measurement time points that maximize information corresponding to biologically relevant metrics and 2) determine the number at which additional measurements begin to provide insignificant information. This framework can be used to balance the availability of resources with the addition of one or more measurement time points to improve the predictability of resulting models.

Set membership experimental design for biological systems

PubMed Central

2012-01-01

Background Experimental design approaches for biological systems are needed to help conserve the limited resources that are allocated for performing experiments. The assumptions used when assigning probability density functions to characterize uncertainty in biological systems are unwarranted when only a small number of measurements can be obtained. In these situations, the uncertainty in biological systems is more appropriately characterized in a bounded-error context. Additionally, effort must be made to improve the connection between modelers and experimentalists by relating design metrics to biologically relevant information. Bounded-error experimental design approaches that can assess the impact of additional measurements on model uncertainty are needed to identify the most appropriate balance between the collection of data and the availability of resources. Results In this work we develop a bounded-error experimental design framework for nonlinear continuous-time systems when few data measurements are available. This approach leverages many of the recent advances in bounded-error parameter and state estimation methods that use interval analysis to generate parameter sets and state bounds consistent with uncertain data measurements. We devise a novel approach using set-based uncertainty propagation to estimate measurement ranges at candidate time points. We then use these estimated measurements at the candidate time points to evaluate which candidate measurements furthest reduce model uncertainty. A method for quickly combining multiple candidate time points is presented and allows for determining the effect of adding multiple measurements. Biologically relevant metrics are developed and used to predict when new data measurements should be acquired, which system components should be measured and how many additional measurements should be obtained. Conclusions The practicability of our approach is illustrated with a case study. This study shows that our approach is able to 1) identify candidate measurement time points that maximize information corresponding to biologically relevant metrics and 2) determine the number at which additional measurements begin to provide insignificant information. This framework can be used to balance the availability of resources with the addition of one or more measurement time points to improve the predictability of resulting models. PMID:22436240

Reducing epistemic errors in water quality modelling through high-frequency data and stakeholder collaboration: the case of an industrial spill

NASA Astrophysics Data System (ADS)

Krueger, Tobias; Inman, Alex; Paling, Nick

2014-05-01

Catchment management, as driven by legislation such as the EU WFD or grassroots initiatives, requires the apportionment of in-stream pollution to point and diffuse sources so that mitigation measures can be targeted and costs and benefits shared. Source apportionment is typically done via modelling. Given model imperfections and input data errors, it has become state-of-the-art to employ an uncertainty framework. However, what is not easily incorporated in such a framework, and currently much discussed in hydrology, are epistemic uncertainties, i.e. those uncertainties that relate to lack of knowledge about processes and data. For example, what if an otherwise negligible source suddenly matters because of an accidental pollution incident? In this paper we present such a case of epistemic error, an industrial spill ignored in a water quality model, demonstrate the bias of the resulting model simulations, and show how the error was discovered somewhat incidentally by auxiliary high-frequency data and finally corrected through the collective intelligence of a stakeholder network. We suggest that accidental pollution incidents like this are a wide-spread, though largely ignored, problem. Hence our discussion will reflect on the practice of catchment monitoring, modelling and management in general. The case itself occurred as part of ongoing modelling support in the Tamar catchment, one of the priority catchments of the UK government's new approach to managing water resources more decentralised and collaboratively. An Extended Export Coefficient Model (ECM+) had been developed with stakeholders to simulate transfers of nutrients (N & P), sediment and Faecal Coliforms from land to water and down the river network as a function of sewage treatment options, land use, livestock densities and farm management practices. In the process of updating the model for the hydrological years 2008-2012 an over-prediction of the annual average P concentration by the model was found at one sub-catchment outlet compared to high-frequency measurements at this point that had become available through another UK government initiative, the Demonstration Test Catchments. This discrepancy had gone unnoticed when calibrating the model in a probabilistic framework against the statutory monitoring data due to the high uncertainties associated with their low-frequency monitoring regime. According to these data what turned out to be an over-prediction seemed possible, albeit with low probability. It was only through the well-established contacts with the local stakeholders that this anomaly could be connected to an industrial spill elsewhere in the catchment, and the model eventually corrected for this additional source. Failing to account for this source would have resulted in drastic over-estimation of the contributions of other sources, in particular agriculture, and eventually wrong targeting of catchment restoration funds and collateral damage of stakeholder relations. The paper will conclude with a discussion of the following general points: the pretence of uncertainty frameworks in the light of epistemic errors; the value of high-frequency data; the value of stakeholder collaboration, particularly in the light of sharing sensitive information; the (somewhat incidental) synergies of various pieces of information and policy initiatives.

On the Formulation of Anisotropic-Polyaxial Failure Criteria: A Comparative Study

NASA Astrophysics Data System (ADS)

Parisio, Francesco; Laloui, Lyesse

2018-02-01

The correct representation of the failure of geomaterials that feature strength anisotropy and polyaxiality is crucial for many applications. In this contribution, we propose and evaluate through a comparative study a generalized framework that covers both features. Polyaxiality of strength is modeled with a modified Van Eekelen approach, while the anisotropy is modeled using a fabric tensor approach of the Pietruszczak and Mroz type. Both approaches share the same philosophy as they can be applied to simpler failure surfaces, allowing great flexibility in model formulation. The new failure surface is tested against experimental data and its performance compared against classical failure criteria commonly used in geomechanics. Our study finds that the global error between predictions and data is generally smaller for the proposed framework compared to other classical approaches.

Applications of Machine Learning for Radiation Therapy.

PubMed

Arimura, Hidetaka; Nakamoto, Takahiro

2016-01-01

Radiation therapy has been highly advanced as image guided radiation therapy (IGRT) by making advantage of image engineering technologies. Recently, novel frameworks based on image engineering technologies as well as machine learning technologies have been studied for sophisticating the radiation therapy. In this review paper, the author introduces several researches of applications of machine learning for radiation therapy. For examples, a method to determine the threshold values for standardized uptake value (SUV) for estimation of gross tumor volume (GTV) in positron emission tomography (PET) images, an approach to estimate the multileaf collimator (MLC) position errors between treatment plans and radiation delivery time, and prediction frameworks for esophageal stenosis and radiation pneumonitis risk after radiation therapy are described. Finally, the author introduces seven issues that one should consider when applying machine learning models to radiation therapy.

Propagation of hydro-meteorological uncertainty in a model cascade framework to inundation prediction

NASA Astrophysics Data System (ADS)

Rodríguez-Rincón, J. P.; Pedrozo-Acuña, A.; Breña-Naranjo, J. A.

2015-07-01

This investigation aims to study the propagation of meteorological uncertainty within a cascade modelling approach to flood prediction. The methodology was comprised of a numerical weather prediction (NWP) model, a distributed rainfall-runoff model and a 2-D hydrodynamic model. The uncertainty evaluation was carried out at the meteorological and hydrological levels of the model chain, which enabled the investigation of how errors that originated in the rainfall prediction interact at a catchment level and propagate to an estimated inundation area and depth. For this, a hindcast scenario is utilised removing non-behavioural ensemble members at each stage, based on the fit with observed data. At the hydrodynamic level, an uncertainty assessment was not incorporated; instead, the model was setup following guidelines for the best possible representation of the case study. The selected extreme event corresponds to a flood that took place in the southeast of Mexico during November 2009, for which field data (e.g. rain gauges; discharge) and satellite imagery were available. Uncertainty in the meteorological model was estimated by means of a multi-physics ensemble technique, which is designed to represent errors from our limited knowledge of the processes generating precipitation. In the hydrological model, a multi-response validation was implemented through the definition of six sets of plausible parameters from past flood events. Precipitation fields from the meteorological model were employed as input in a distributed hydrological model, and resulting flood hydrographs were used as forcing conditions in the 2-D hydrodynamic model. The evolution of skill within the model cascade shows a complex aggregation of errors between models, suggesting that in valley-filling events hydro-meteorological uncertainty has a larger effect on inundation depths than that observed in estimated flood inundation extents.

Evaluation of the suitability of chromatographic systems to predict human skin permeation of neutral compounds.

PubMed

Hidalgo-Rodríguez, Marta; Soriano-Meseguer, Sara; Fuguet, Elisabet; Ràfols, Clara; Rosés, Martí

2013-12-18

Several chromatographic systems (three systems of high-performance liquid chromatography and two micellar electrokinetic chromatography systems) besides the reference octanol-water partition system are evaluated by a systematic procedure previously proposed in order to know their ability to model human skin permeation. The precision achieved when skin-water permeability coefficients are correlated against chromatographic retention factors is predicted within the framework of the solvation parameter model. It consists in estimating the contribution of error due to the biological and chromatographic data, as well as the error coming from the dissimilarity between the human skin permeation and the chromatographic systems. Both predictions and experimental tests show that all correlations are greatly affected by the considerable uncertainty of the skin permeability data and the error associated to the dissimilarity between the systems. Correlations with much better predictive abilities are achieved when the volume of the solute is used as additional variable, which illustrates the main roles of both lipophilicity and size of the solute to penetrate through the skin. In this way, the considered systems are able to give precise estimations of human skin permeability coefficients. In particular, the HPLC systems with common C18 columns provide the best performances in emulating the permeation of neutral compounds from aqueous solution through the human skin. As a result, a methodology based on easy, fast, and economical HPLC measurements in a common C18 column has been developed. After a validation based on training and test sets, the method has been applied with good results to the estimation of skin permeation of several hormones and pesticides. Copyright © 2013 Elsevier B.V. All rights reserved.

An Experimental Study of Medical Error Explanations: Do Apology, Empathy, Corrective Action, and Compensation Alter Intentions and Attitudes?

PubMed

Nazione, Samantha; Pace, Kristin

2015-01-01

Medical malpractice lawsuits are a growing problem in the United States, and there is much controversy regarding how to best address this problem. The medical error disclosure framework suggests that apologizing, expressing empathy, engaging in corrective action, and offering compensation after a medical error may improve the provider-patient relationship and ultimately help reduce the number of medical malpractice lawsuits patients bring to medical providers. This study provides an experimental examination of the medical error disclosure framework and its effect on amount of money requested in a lawsuit, negative intentions, attitudes, and anger toward the provider after a medical error. Results suggest empathy may play a large role in providing positive outcomes after a medical error.

Nurses' attitude and intention of medication administration error reporting.

PubMed

Hung, Chang-Chiao; Chu, Tsui-Ping; Lee, Bih-O; Hsiao, Chia-Chi

2016-02-01

The Aims of this study were to explore the effects of nurses' attitudes and intentions regarding medication administration error reporting on actual reporting behaviours. Underreporting of medication errors is still a common occurrence. Whether attitude and intention towards medication administration error reporting connect to actual reporting behaviours remain unclear. This study used a cross-sectional design with self-administered questionnaires, and the theory of planned behaviour was used as the framework for this study. A total of 596 staff nurses who worked in general wards and intensive care units in a hospital were invited to participate in this study. The researchers used the instruments measuring nurses' attitude, nurse managers' and co-workers' attitude, report control, and nurses' intention to predict nurses' actual reporting behaviours. Data were collected from September-November 2013. Path analyses were used to examine the hypothesized model. Of the 596 nurses invited to participate, 548 (92%) completed and returned a valid questionnaire. The findings indicated that nurse managers' and co-workers' attitudes are predictors for nurses' attitudes towards medication administration error reporting. Nurses' attitudes also influenced their intention to report medication administration errors; however, no connection was found between intention and actual reporting behaviour. The findings reflected links among colleague perspectives, nurses' attitudes, and intention to report medication administration errors. The researchers suggest that hospitals should increase nurses' awareness and recognition of error occurrence. Regardless of nurse managers' and co-workers' attitudes towards medication administration error reporting, nurses are likely to report medication administration errors if they detect them. Management of medication administration errors should focus on increasing nurses' awareness and recognition of error occurrence. © 2015 John Wiley & Sons Ltd.

Planning for robust reserve networks using uncertainty analysis

USGS Publications Warehouse

Moilanen, A.; Runge, M.C.; Elith, Jane; Tyre, A.; Carmel, Y.; Fegraus, E.; Wintle, B.A.; Burgman, M.; Ben-Haim, Y.

2006-01-01

Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence?absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data?erroneous species presence?absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search.

A Cerebellar Framework for Predictive Coding and Homeostatic Regulation in Depressive Disorder.

PubMed

Schutter, Dennis J L G

2016-02-01

Depressive disorder is associated with abnormalities in the processing of reward and punishment signals and disturbances in homeostatic regulation. These abnormalities are proposed to impair error minimization routines for reducing uncertainty. Several lines of research point towards a role of the cerebellum in reward- and punishment-related predictive coding and homeostatic regulatory function in depressive disorder. Available functional and anatomical evidence suggests that in addition to the cortico-limbic networks, the cerebellum is part of the dysfunctional brain circuit in depressive disorder as well. It is proposed that impaired cerebellar function contributes to abnormalities in predictive coding and homeostatic dysregulation in depressive disorder. Further research on the role of the cerebellum in depressive disorder may further extend our knowledge on the functional and neural mechanisms of depressive disorder and development of novel antidepressant treatments strategies targeting the cerebellum.

Evaluating concentration estimation errors in ELISA microarray experiments

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

Daly, Don S.; White, Amanda M.; Varnum, Susan M.

Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to predict a protein concentration in a sample. Deploying ELISA in a microarray format permits simultaneous prediction of the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Evaluating prediction error is critical to interpreting biological significance and improving the ELISA microarray process. Evaluating prediction error must be automated to realize a reliable high-throughput ELISA microarray system. Methods: In this paper, we present a statistical method based on propagation of error to evaluate prediction errors in the ELISA microarray process. Althoughmore » propagation of error is central to this method, it is effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization and statistical diagnostics when evaluating ELISA microarray prediction errors. We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of prediction errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error.« less

Quantifying Biomass from Point Clouds by Connecting Representations of Ecosystem Structure

NASA Astrophysics Data System (ADS)

Hendryx, S. M.; Barron-Gafford, G.

2017-12-01

Quantifying terrestrial ecosystem biomass is an essential part of monitoring carbon stocks and fluxes within the global carbon cycle and optimizing natural resource management. Point cloud data such as from lidar and structure from motion can be effective for quantifying biomass over large areas, but significant challenges remain in developing effective models that allow for such predictions. Inference models that estimate biomass from point clouds are established in many environments, yet, are often scale-dependent, needing to be fitted and applied at the same spatial scale and grid size at which they were developed. Furthermore, training such models typically requires large in situ datasets that are often prohibitively costly or time-consuming to obtain. We present here a scale- and sensor-invariant framework for efficiently estimating biomass from point clouds. Central to this framework, we present a new algorithm, assignPointsToExistingClusters, that has been developed for finding matches between in situ data and clusters in remotely-sensed point clouds. The algorithm can be used for assessing canopy segmentation accuracy and for training and validating machine learning models for predicting biophysical variables. We demonstrate the algorithm's efficacy by using it to train a random forest model of above ground biomass in a shrubland environment in Southern Arizona. We show that by learning a nonlinear function to estimate biomass from segmented canopy features we can reduce error, especially in the presence of inaccurate clusterings, when compared to a traditional, deterministic technique to estimate biomass from remotely measured canopies. Our random forest on cluster features model extends established methods of training random forest regressions to predict biomass of subplots but requires significantly less training data and is scale invariant. The random forest on cluster features model reduced mean absolute error, when evaluated on all test data in leave one out cross validation, by 40.6% from deterministic mesquite allometry and 35.9% from the inferred ecosystem-state allometric function. Our framework should allow for the inference of biomass more efficiently than common subplot methods and more accurately than individual tree segmentation methods in densely vegetated environments.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

Relationships of Measurement Error and Prediction Error in Observed-Score Regression

ERIC Educational Resources Information Center

Moses, Tim

2012-01-01

The focus of this paper is assessing the impact of measurement errors on the prediction error of an observed-score regression. Measures are presented and described for decomposing the linear regression's prediction error variance into parts attributable to the true score variance and the error variances of the dependent variable and the predictor…

Predictor-corrector framework for the sequential assembly of optical systems based on wavefront sensing.

PubMed

Schindlbeck, Christopher; Pape, Christian; Reithmeier, Eduard

2018-04-16

Alignment of optical components is crucial for the assembly of optical systems to ensure their full functionality. In this paper we present a novel predictor-corrector framework for the sequential assembly of serial optical systems. Therein, we use a hybrid optical simulation model that comprises virtual and identified component positions. The hybrid model is constantly adapted throughout the assembly process with the help of nonlinear identification techniques and wavefront measurements. This enables prediction of the future wavefront at the detector plane and therefore allows for taking corrective measures accordingly during the assembly process if a user-defined tolerance on the wavefront error is violated. We present a novel notation for the so-called hybrid model and outline the work flow of the presented predictor-corrector framework. A beam expander is assembled as demonstrator for experimental verification of the framework. The optical setup consists of a laser, two bi-convex spherical lenses each mounted to a five degree-of-freedom stage to misalign and correct components, and a Shack-Hartmann sensor for wavefront measurements.

Quantifying the impact of material-model error on macroscale quantities-of-interest using multiscale a posteriori error-estimation techniques

DOE PAGES

Brown, Judith A.; Bishop, Joseph E.

2016-07-20

An a posteriori error-estimation framework is introduced to quantify and reduce modeling errors resulting from approximating complex mesoscale material behavior with a simpler macroscale model. Such errors may be prevalent when modeling welds and additively manufactured structures, where spatial variations and material textures may be present in the microstructure. We consider a case where a <100> fiber texture develops in the longitudinal scanning direction of a weld. Transversely isotropic elastic properties are obtained through homogenization of a microstructural model with this texture and are considered the reference weld properties within the error-estimation framework. Conversely, isotropic elastic properties are considered approximatemore » weld properties since they contain no representation of texture. Errors introduced by using isotropic material properties to represent a weld are assessed through a quantified error bound in the elastic regime. Lastly, an adaptive error reduction scheme is used to determine the optimal spatial variation of the isotropic weld properties to reduce the error bound.« less

'Bodily precision': a predictive coding account of individual differences in interoceptive accuracy.

PubMed

Ainley, Vivien; Apps, Matthew A J; Fotopoulou, Aikaterini; Tsakiris, Manos

2016-11-19

Individuals differ in their awareness of afferent information from within their bodies, which is typically assessed by a heartbeat perception measure of 'interoceptive accuracy' (IAcc). Neural and behavioural correlates of this trait have been investigated, but a theoretical explanation has yet to be presented. Building on recent models that describe interoception within the free energy/predictive coding framework, this paper applies similar principles to IAcc, proposing that individual differences in IAcc depend on 'precision' in interoceptive systems, i.e. the relative weight accorded to 'prior' representations and 'prediction errors' (that part of incoming interoceptive sensation not accounted for by priors), at various levels within the cortical hierarchy and between modalities. Attention has the effect of optimizing precision both within and between sensory modalities. Our central assumption is that people with high IAcc are able, with attention, to prioritize interoception over other sensory modalities and can thus adjust the relative precision of their interoceptive priors and prediction errors, where appropriate, given their personal history. This characterization explains key findings within the interoception literature; links results previously seen as unrelated or contradictory; and may have important implications for understanding cognitive, behavioural and psychopathological consequences of both high and low interoceptive awareness.This article is part of the themed issue 'Interoception beyond homeostasis: affect, cognition and mental health'. © 2016 The Author(s).

The role of model errors represented by nonlinear forcing singular vector tendency error in causing the "spring predictability barrier" within ENSO predictions

NASA Astrophysics Data System (ADS)

Duan, Wansuo; Zhao, Peng

2017-04-01

Within the Zebiak-Cane model, the nonlinear forcing singular vector (NFSV) approach is used to investigate the role of model errors in the "Spring Predictability Barrier" (SPB) phenomenon within ENSO predictions. NFSV-related errors have the largest negative effect on the uncertainties of El Niño predictions. NFSV errors can be classified into two types: the first is characterized by a zonal dipolar pattern of SST anomalies (SSTA), with the western poles centered in the equatorial central-western Pacific exhibiting positive anomalies and the eastern poles in the equatorial eastern Pacific exhibiting negative anomalies; and the second is characterized by a pattern almost opposite the first type. The first type of error tends to have the worst effects on El Niño growth-phase predictions, whereas the latter often yields the largest negative effects on decaying-phase predictions. The evolution of prediction errors caused by NFSV-related errors exhibits prominent seasonality, with the fastest error growth in the spring and/or summer seasons; hence, these errors result in a significant SPB related to El Niño events. The linear counterpart of NFSVs, the (linear) forcing singular vector (FSV), induces a less significant SPB because it contains smaller prediction errors. Random errors cannot generate a SPB for El Niño events. These results show that the occurrence of an SPB is related to the spatial patterns of tendency errors. The NFSV tendency errors cause the most significant SPB for El Niño events. In addition, NFSVs often concentrate these large value errors in a few areas within the equatorial eastern and central-western Pacific, which likely represent those areas sensitive to El Niño predictions associated with model errors. Meanwhile, these areas are also exactly consistent with the sensitive areas related to initial errors determined by previous studies. This implies that additional observations in the sensitive areas would not only improve the accuracy of the initial field but also promote the reduction of model errors to greatly improve ENSO forecasts.

Injury profile SIMulator, a Qualitative aggregative modelling framework to predict injury profile as a function of cropping practices, and abiotic and biotic environment. II. Proof of concept: design of IPSIM-wheat-eyespot.

PubMed

Robin, Marie-Hélène; Colbach, Nathalie; Lucas, Philippe; Montfort, Françoise; Cholez, Célia; Debaeke, Philippe; Aubertot, Jean-Noël

2013-01-01

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM's modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM's modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiency = 0.51, Root Mean Square Error of Prediction = 24%; bias = 5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation.

Functional dissociation of stimulus intensity encoding and predictive coding of pain in the insula

PubMed Central

Geuter, Stephan; Boll, Sabrina; Eippert, Falk; Büchel, Christian

2017-01-01

The computational principles by which the brain creates a painful experience from nociception are still unknown. Classic theories suggest that cortical regions either reflect stimulus intensity or additive effects of intensity and expectations, respectively. By contrast, predictive coding theories provide a unified framework explaining how perception is shaped by the integration of beliefs about the world with mismatches resulting from the comparison of these beliefs against sensory input. Using functional magnetic resonance imaging during a probabilistic heat pain paradigm, we investigated which computations underlie pain perception. Skin conductance, pupil dilation, and anterior insula responses to cued pain stimuli strictly followed the response patterns hypothesized by the predictive coding model, whereas posterior insula encoded stimulus intensity. This novel functional dissociation of pain processing within the insula together with previously observed alterations in chronic pain offer a novel interpretation of aberrant pain processing as disturbed weighting of predictions and prediction errors. DOI: http://dx.doi.org/10.7554/eLife.24770.001 PMID:28524817

Maximal Predictability Approach for Identifying the Right Descriptors for Electrocatalytic Reactions.

PubMed

Krishnamurthy, Dilip; Sumaria, Vaidish; Viswanathan, Venkatasubramanian

2018-02-01

Density functional theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations are associated with inherent uncertainty, which limits the ability to delineate materials (distinguishability) that possess high activity. Development of error-estimation capabilities in DFT has enabled uncertainty propagation through activity-prediction models. In this work, we demonstrate an approach to propagating uncertainty through thermodynamic activity models leading to a probability distribution of the computed activity and thereby its expectation value. A new metric, prediction efficiency, is defined, which provides a quantitative measure of the ability to distinguish activity of materials and can be used to identify the optimal descriptor(s) ΔG opt . We demonstrate the framework for four important electrochemical reactions: hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. Future studies could utilize expected activity and prediction efficiency to significantly improve the prediction accuracy of highly active material candidates.

Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint

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

Dall'Anese, Emiliano; Baker, Kyri; Summers, Tyler

2016-12-01

The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative boundsmore » that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.« less

The mismeasure of morals: antisocial personality traits predict utilitarian responses to moral dilemmas.

PubMed

Bartels, Daniel M; Pizarro, David A

2011-10-01

Researchers have recently argued that utilitarianism is the appropriate framework by which to evaluate moral judgment, and that individuals who endorse non-utilitarian solutions to moral dilemmas (involving active vs. passive harm) are committing an error. We report a study in which participants responded to a battery of personality assessments and a set of dilemmas that pit utilitarian and non-utilitarian options against each other. Participants who indicated greater endorsement of utilitarian solutions had higher scores on measures of Psychopathy, machiavellianism, and life meaninglessness. These results question the widely-used methods by which lay moral judgments are evaluated, as these approaches lead to the counterintuitive conclusion that those individuals who are least prone to moral errors also possess a set of psychological characteristics that many would consider prototypically immoral. Copyright © 2011 Elsevier B.V. All rights reserved.

Estimation and Application of Ecological Memory Functions in Time and Space

NASA Astrophysics Data System (ADS)

Itter, M.; Finley, A. O.; Dawson, A.

2017-12-01

A common goal in quantitative ecology is the estimation or prediction of ecological processes as a function of explanatory variables (or covariates). Frequently, the ecological process of interest and associated covariates vary in time, space, or both. Theory indicates many ecological processes exhibit memory to local, past conditions. Despite such theoretical understanding, few methods exist to integrate observations from the recent past or within a local neighborhood as drivers of these processes. We build upon recent methodological advances in ecology and spatial statistics to develop a Bayesian hierarchical framework to estimate so-called ecological memory functions; that is, weight-generating functions that specify the relative importance of local, past covariate observations to ecological processes. Memory functions are estimated using a set of basis functions in time and/or space, allowing for flexible ecological memory based on a reduced set of parameters. Ecological memory functions are entirely data driven under the Bayesian hierarchical framework—no a priori assumptions are made regarding functional forms. Memory function uncertainty follows directly from posterior distributions for model parameters allowing for tractable propagation of error to predictions of ecological processes. We apply the model framework to simulated spatio-temporal datasets generated using memory functions of varying complexity. The framework is also applied to estimate the ecological memory of annual boreal forest growth to local, past water availability. Consistent with ecological understanding of boreal forest growth dynamics, memory to past water availability peaks in the year previous to growth and slowly decays to zero in five to eight years. The Bayesian hierarchical framework has applicability to a broad range of ecosystems and processes allowing for increased understanding of ecosystem responses to local and past conditions and improved prediction of ecological processes.

Implementation and Validation of the Viscoelastic Continuum Damage Theory for Asphalt Mixture and Pavement Analysis in Brazil

NASA Astrophysics Data System (ADS)

Nascimento, Luis Alberto Herrmann do

This dissertation presents the implementation and validation of the viscoelastic continuum damage (VECD) model for asphalt mixture and pavement analysis in Brazil. It proposes a simulated damage-to-fatigue cracked area transfer function for the layered viscoelastic continuum damage (LVECD) program framework and defines the model framework's fatigue cracking prediction error for asphalt pavement reliability-based design solutions in Brazil. The research is divided into three main steps: (i) implementation of the simplified viscoelastic continuum damage (S-VECD) model in Brazil (Petrobras) for asphalt mixture characterization, (ii) validation of the LVECD model approach for pavement analysis based on field performance observations, and defining a local simulated damage-to-cracked area transfer function for the Fundao Project's pavement test sections in Rio de Janeiro, RJ, and (iii) validation of the Fundao project local transfer function to be used throughout Brazil for asphalt pavement fatigue cracking predictions, based on field performance observations of the National MEPDG Project's pavement test sections, thereby validating the proposed framework's prediction capability. For the first step, the S-VECD test protocol, which uses controlled-on-specimen strain mode-of-loading, was successfully implemented at the Petrobras and used to characterize Brazilian asphalt mixtures that are composed of a wide range of asphalt binders. This research verified that the S-VECD model coupled with the GR failure criterion is accurate for fatigue life predictions of Brazilian asphalt mixtures, even when very different asphalt binders are used. Also, the applicability of the load amplitude sweep (LAS) test for the fatigue characterization of the asphalt binders was checked, and the effects of different asphalt binders on the fatigue damage properties of the asphalt mixtures was investigated. The LAS test results, modeled according to VECD theory, presented a strong correlation with the asphalt mixtures' fatigue performance. In the second step, the S-VECD test protocol was used to characterize the asphalt mixtures used in the 27 selected Fundao project test sections and subjected to real traffic loading. Thus, the asphalt mixture properties, pavement structure data, traffic loading, and climate were input into the LVECD program for pavement fatigue cracking performance simulations. The simulation results showed good agreement with the field-observed distresses. Then, a damage shift approach, based on the initial simulated damage growth rate, was introduced in order to obtain a unique relationship between the LVECD-simulated shifted damage and the pavement-observed fatigue cracked areas. This correlation was fitted to a power form function and defined as the averaged reduced damage-to-cracked area transfer function. The last step consisted of using the averaged reduced damage-to-cracked area transfer function that was developed in the Fundao project to predict pavement fatigue cracking in 17 National MEPDG project test sections. The procedures for the material characterization and pavement data gathering adopted in this step are similar to those used for the Fundao project simulations. This research verified that the transfer function defined for the Fundao project sections can be used for the fatigue performance predictions of a wide range of pavements all over Brazil, as the predicted and observed cracked areas for the National MEPDG pavements presented good agreement, following the same trends found for the Fundao project pavement sites. Based on the prediction errors determined for all 44 pavement test sections (Fundao and National MEPDG test sections), the proposed framework's prediction capability was determined so that reliability-based solutions can be applied for flexible pavement design. It was concluded that the proposed LVECD program framework has very good fatigue cracking prediction capability.

Impact of Representing Model Error in a Hybrid Ensemble-Variational Data Assimilation System for Track Forecast of Tropical Cyclones over the Bay of Bengal

NASA Astrophysics Data System (ADS)

Kutty, Govindan; Muraleedharan, Rohit; Kesarkar, Amit P.

2018-03-01

Uncertainties in the numerical weather prediction models are generally not well-represented in ensemble-based data assimilation (DA) systems. The performance of an ensemble-based DA system becomes suboptimal, if the sources of error are undersampled in the forecast system. The present study examines the effect of accounting for model error treatments in the hybrid ensemble transform Kalman filter—three-dimensional variational (3DVAR) DA system (hybrid) in the track forecast of two tropical cyclones viz. Hudhud and Thane, formed over the Bay of Bengal, using Advanced Research Weather Research and Forecasting (ARW-WRF) model. We investigated the effect of two types of model error treatment schemes and their combination on the hybrid DA system; (i) multiphysics approach, which uses different combination of cumulus, microphysics and planetary boundary layer schemes, (ii) stochastic kinetic energy backscatter (SKEB) scheme, which perturbs the horizontal wind and potential temperature tendencies, (iii) a combination of both multiphysics and SKEB scheme. Substantial improvements are noticed in the track positions of both the cyclones, when flow-dependent ensemble covariance is used in 3DVAR framework. Explicit model error representation is found to be beneficial in treating the underdispersive ensembles. Among the model error schemes used in this study, a combination of multiphysics and SKEB schemes has outperformed the other two schemes with improved track forecast for both the tropical cyclones.

Modeling the Violation of Reward Maximization and Invariance in Reinforcement Schedules

PubMed Central

La Camera, Giancarlo; Richmond, Barry J.

2008-01-01

It is often assumed that animals and people adjust their behavior to maximize reward acquisition. In visually cued reinforcement schedules, monkeys make errors in trials that are not immediately rewarded, despite having to repeat error trials. Here we show that error rates are typically smaller in trials equally distant from reward but belonging to longer schedules (referred to as “schedule length effect”). This violates the principles of reward maximization and invariance and cannot be predicted by the standard methods of Reinforcement Learning, such as the method of temporal differences. We develop a heuristic model that accounts for all of the properties of the behavior in the reinforcement schedule task but whose predictions are not different from those of the standard temporal difference model in choice tasks. In the modification of temporal difference learning introduced here, the effect of schedule length emerges spontaneously from the sensitivity to the immediately preceding trial. We also introduce a policy for general Markov Decision Processes, where the decision made at each node is conditioned on the motivation to perform an instrumental action, and show that the application of our model to the reinforcement schedule task and the choice task are special cases of this general theoretical framework. Within this framework, Reinforcement Learning can approach contextual learning with the mixture of empirical findings and principled assumptions that seem to coexist in the best descriptions of animal behavior. As examples, we discuss two phenomena observed in humans that often derive from the violation of the principle of invariance: “framing,” wherein equivalent options are treated differently depending on the context in which they are presented, and the “sunk cost” effect, the greater tendency to continue an endeavor once an investment in money, effort, or time has been made. The schedule length effect might be a manifestation of these phenomena in monkeys. PMID:18688266

Modeling the violation of reward maximization and invariance in reinforcement schedules.

PubMed

La Camera, Giancarlo; Richmond, Barry J

2008-08-08

It is often assumed that animals and people adjust their behavior to maximize reward acquisition. In visually cued reinforcement schedules, monkeys make errors in trials that are not immediately rewarded, despite having to repeat error trials. Here we show that error rates are typically smaller in trials equally distant from reward but belonging to longer schedules (referred to as "schedule length effect"). This violates the principles of reward maximization and invariance and cannot be predicted by the standard methods of Reinforcement Learning, such as the method of temporal differences. We develop a heuristic model that accounts for all of the properties of the behavior in the reinforcement schedule task but whose predictions are not different from those of the standard temporal difference model in choice tasks. In the modification of temporal difference learning introduced here, the effect of schedule length emerges spontaneously from the sensitivity to the immediately preceding trial. We also introduce a policy for general Markov Decision Processes, where the decision made at each node is conditioned on the motivation to perform an instrumental action, and show that the application of our model to the reinforcement schedule task and the choice task are special cases of this general theoretical framework. Within this framework, Reinforcement Learning can approach contextual learning with the mixture of empirical findings and principled assumptions that seem to coexist in the best descriptions of animal behavior. As examples, we discuss two phenomena observed in humans that often derive from the violation of the principle of invariance: "framing," wherein equivalent options are treated differently depending on the context in which they are presented, and the "sunk cost" effect, the greater tendency to continue an endeavor once an investment in money, effort, or time has been made. The schedule length effect might be a manifestation of these phenomena in monkeys.

Concurrent prediction of ground reaction forces and moments and tibiofemoral contact forces during walking using musculoskeletal modelling.

PubMed

Peng, Yinghu; Zhang, Zhifeng; Gao, Yongchang; Chen, Zhenxian; Xin, Hua; Zhang, Qida; Fan, Xunjian; Jin, Zhongmin

2018-02-01

Ground reaction forces and moments (GRFs and GRMs) measured from force plates in a gait laboratory are usually used as the input conditions to predict the knee joint forces and moments via musculoskeletal (MSK) multibody dynamics (MBD) model. However, the measurements of the GRFs and GRMs data rely on force plates and sometimes are limited by the difficulty in some patient's gait patterns (e.g. treadmill gait). In addition, the force plate calibration error may influence the prediction accuracy of the MSK model. In this study, a prediction method of the GRFs and GRMs based on elastic contact element was integrated into a subject-specific MSK MBD modelling framework of total knee arthroplasty (TKA), and the GRFs and GRMs and knee contact forces (KCFs) during walking were predicted simultaneously with reasonable accuracy. The ground reaction forces and moments were predicted with an average root mean square errors (RMSEs) of 0.021 body weight (BW), 0.014 BW and 0.089 BW in the antero-posterior, medio-lateral and vertical directions and 0.005 BW•body height (BH), 0.011 BW•BH, 0.004 BW•BH in the sagittal, frontal and transverse planes, respectively. Meanwhile, the medial, lateral and total tibiofemoral (TF) contact forces were predicted by the developed MSK model with RMSEs of 0.025-0.032 BW, 0.018-0.022 BW, and 0.089-0.132 BW, respectively. The accuracy of the predicted medial TF contact force was improved by 12% using the present method. The proposed method can extend the application of the MSK model of TKA and is valuable for understanding the in vivo knee biomechanics and tribological conditions without the force plate data. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Risk prediction and aversion by anterior cingulate cortex.

PubMed

Brown, Joshua W; Braver, Todd S

2007-12-01

The recently proposed error-likelihood hypothesis suggests that anterior cingulate cortex (ACC) and surrounding areas will become active in proportion to the perceived likelihood of an error. The hypothesis was originally derived from a computational model prediction. The same computational model now makes a further prediction that ACC will be sensitive not only to predicted error likelihood, but also to the predicted magnitude of the consequences, should an error occur. The product of error likelihood and predicted error consequence magnitude collectively defines the general "expected risk" of a given behavior in a manner analogous but orthogonal to subjective expected utility theory. New fMRI results from an incentivechange signal task now replicate the error-likelihood effect, validate the further predictions of the computational model, and suggest why some segments of the population may fail to show an error-likelihood effect. In particular, error-likelihood effects and expected risk effects in general indicate greater sensitivity to earlier predictors of errors and are seen in risk-averse but not risk-tolerant individuals. Taken together, the results are consistent with an expected risk model of ACC and suggest that ACC may generally contribute to cognitive control by recruiting brain activity to avoid risk.

Predicting age from cortical structure across the lifespan.

PubMed

Madan, Christopher R; Kensinger, Elizabeth A

2018-03-01

Despite interindividual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. This study assessed how accurately an individual's age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from one region to 1000 regions. The age prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated nonlinear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology. © 2018 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Automatising the analysis of stochastic biochemical time-series

PubMed Central

2015-01-01

Background Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse complex models under a variety of configurations, eventually selecting the best setting of, e.g., parameters for a target system. Motivation This operational pipeline relies on the ability to interpret the predictions of a model, often represented as simulation time-series. Thus, an efficient data analysis pipeline is crucial to automatise time-series analyses, bearing in mind that errors in this phase might mislead the modeller's conclusions. Results For this reason we have developed an intuitive framework-independent Python tool to automate analyses common to a variety of modelling approaches. These include assessment of useful non-trivial statistics for simulation ensembles, e.g., estimation of master equations. Intuitive and domain-independent batch scripts will allow the researcher to automatically prepare reports, thus speeding up the usual model-definition, testing and refinement pipeline. PMID:26051821

Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making.

PubMed

Schönberg, Tom; Daw, Nathaniel D; Joel, Daphna; O'Doherty, John P

2007-11-21

The computational framework of reinforcement learning has been used to forward our understanding of the neural mechanisms underlying reward learning and decision-making behavior. It is known that humans vary widely in their performance in decision-making tasks. Here, we used a simple four-armed bandit task in which subjects are almost evenly split into two groups on the basis of their performance: those who do learn to favor choice of the optimal action and those who do not. Using models of reinforcement learning we sought to determine the neural basis of these intrinsic differences in performance by scanning both groups with functional magnetic resonance imaging. We scanned 29 subjects while they performed the reward-based decision-making task. Our results suggest that these two groups differ markedly in the degree to which reinforcement learning signals in the striatum are engaged during task performance. While the learners showed robust prediction error signals in both the ventral and dorsal striatum during learning, the nonlearner group showed a marked absence of such signals. Moreover, the magnitude of prediction error signals in a region of dorsal striatum correlated significantly with a measure of behavioral performance across all subjects. These findings support a crucial role of prediction error signals, likely originating from dopaminergic midbrain neurons, in enabling learning of action selection preferences on the basis of obtained rewards. Thus, spontaneously observed individual differences in decision making performance demonstrate the suggested dependence of this type of learning on the functional integrity of the dopaminergic striatal system in humans.

Hardware-efficient bosonic quantum error-correcting codes based on symmetry operators

NASA Astrophysics Data System (ADS)

Niu, Murphy Yuezhen; Chuang, Isaac L.; Shapiro, Jeffrey H.

2018-03-01

We establish a symmetry-operator framework for designing quantum error-correcting (QEC) codes based on fundamental properties of the underlying system dynamics. Based on this framework, we propose three hardware-efficient bosonic QEC codes that are suitable for χ(2 )-interaction based quantum computation in multimode Fock bases: the χ(2 ) parity-check code, the χ(2 ) embedded error-correcting code, and the χ(2 ) binomial code. All of these QEC codes detect photon-loss or photon-gain errors by means of photon-number parity measurements, and then correct them via χ(2 ) Hamiltonian evolutions and linear-optics transformations. Our symmetry-operator framework provides a systematic procedure for finding QEC codes that are not stabilizer codes, and it enables convenient extension of a given encoding to higher-dimensional qudit bases. The χ(2 ) binomial code is of special interest because, with m ≤N identified from channel monitoring, it can correct m -photon-loss errors, or m -photon-gain errors, or (m -1 )th -order dephasing errors using logical qudits that are encoded in O (N ) photons. In comparison, other bosonic QEC codes require O (N2) photons to correct the same degree of bosonic errors. Such improved photon efficiency underscores the additional error-correction power that can be provided by channel monitoring. We develop quantum Hamming bounds for photon-loss errors in the code subspaces associated with the χ(2 ) parity-check code and the χ(2 ) embedded error-correcting code, and we prove that these codes saturate their respective bounds. Our χ(2 ) QEC codes exhibit hardware efficiency in that they address the principal error mechanisms and exploit the available physical interactions of the underlying hardware, thus reducing the physical resources required for implementing their encoding, decoding, and error-correction operations, and their universal encoded-basis gate sets.

TSSi--an R package for transcription start site identification from 5' mRNA tag data.

PubMed

Kreutz, C; Gehring, J S; Lang, D; Reski, R; Timmer, J; Rensing, S A

2012-06-15

High-throughput sequencing has become an essential experimental approach for the investigation of transcriptional mechanisms. For some applications like ChIP-seq, several approaches for the prediction of peak locations exist. However, these methods are not designed for the identification of transcription start sites (TSSs) because such datasets contain qualitatively different noise. In this application note, the R package TSSi is presented which provides a heuristic framework for the identification of TSSs based on 5' mRNA tag data. Probabilistic assumptions for the distribution of the data, i.e. for the observed positions of the mapped reads, as well as for systematic errors, i.e. for reads which map closely but not exactly to a real TSS, are made and can be adapted by the user. The framework also comprises a regularization procedure which can be applied as a preprocessing step to decrease the noise and thereby reduce the number of false predictions. The R package TSSi is available from the Bioconductor web site: www.bioconductor.org/packages/release/bioc/html/TSSi.html.

Rules and mechanisms for efficient two-stage learning in neural circuits

PubMed Central

Teşileanu, Tiberiu; Ölveczky, Bence; Balasubramanian, Vijay

2017-01-01

Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in ‘tutor’ circuits (e.g., LMAN) should match plasticity mechanisms in ‘student’ circuits (e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning. DOI: http://dx.doi.org/10.7554/eLife.20944.001 PMID:28374674

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

Smith, Kandler; Shi, Ying; Santhanagopalan, Shriram

Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under differentmore » levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.« less

Achieving the "triple aim" for inborn errors of metabolism: a review of challenges to outcomes research and presentation of a new practice-based evidence framework.

PubMed

Potter, Beth K; Chakraborty, Pranesh; Kronick, Jonathan B; Wilson, Kumanan; Coyle, Doug; Feigenbaum, Annette; Geraghty, Michael T; Karaceper, Maria D; Little, Julian; Mhanni, Aizeddin; Mitchell, John J; Siriwardena, Komudi; Wilson, Brenda J; Syrowatka, Ania

2013-06-01

Across all areas of health care, decision makers are in pursuit of what Berwick and colleagues have called the "triple aim": improving patient experiences with care, improving health outcomes, and managing health system impacts. This is challenging in a rare disease context, as exemplified by inborn errors of metabolism. There is a need for evaluative outcomes research to support effective and appropriate care for inborn errors of metabolism. We suggest that such research should consider interventions at both the level of the health system (e.g., early detection through newborn screening, programs to provide access to treatments) and the level of individual patient care (e.g., orphan drugs, medical foods). We have developed a practice-based evidence framework to guide outcomes research for inborn errors of metabolism. Focusing on outcomes across the triple aim, this framework integrates three priority themes: tailoring care in the context of clinical heterogeneity; a shift from "urgent care" to "opportunity for improvement"; and the need to evaluate the comparative effectiveness of emerging and established therapies. Guided by the framework, a new Canadian research network has been established to generate knowledge that will inform the design and delivery of health services for patients with inborn errors of metabolism and other rare diseases.

Human error analysis of commercial aviation accidents: application of the Human Factors Analysis and Classification system (HFACS).

PubMed

Wiegmann, D A; Shappell, S A

2001-11-01

The Human Factors Analysis and Classification System (HFACS) is a general human error framework originally developed and tested within the U.S. military as a tool for investigating and analyzing the human causes of aviation accidents. Based on Reason's (1990) model of latent and active failures, HFACS addresses human error at all levels of the system, including the condition of aircrew and organizational factors. The purpose of the present study was to assess the utility of the HFACS framework as an error analysis and classification tool outside the military. The HFACS framework was used to analyze human error data associated with aircrew-related commercial aviation accidents that occurred between January 1990 and December 1996 using database records maintained by the NTSB and the FAA. Investigators were able to reliably accommodate all the human causal factors associated with the commercial aviation accidents examined in this study using the HFACS system. In addition, the classification of data using HFACS highlighted several critical safety issues in need of intervention research. These results demonstrate that the HFACS framework can be a viable tool for use within the civil aviation arena. However, additional research is needed to examine its applicability to areas outside the flight deck, such as aircraft maintenance and air traffic control domains.

Reference-free error estimation for multiple measurement methods.

PubMed

Madan, Hennadii; Pernuš, Franjo; Špiclin, Žiga

2018-01-01

We present a computational framework to select the most accurate and precise method of measurement of a certain quantity, when there is no access to the true value of the measurand. A typical use case is when several image analysis methods are applied to measure the value of a particular quantitative imaging biomarker from the same images. The accuracy of each measurement method is characterized by systematic error (bias), which is modeled as a polynomial in true values of measurand, and the precision as random error modeled with a Gaussian random variable. In contrast to previous works, the random errors are modeled jointly across all methods, thereby enabling the framework to analyze measurement methods based on similar principles, which may have correlated random errors. Furthermore, the posterior distribution of the error model parameters is estimated from samples obtained by Markov chain Monte-Carlo and analyzed to estimate the parameter values and the unknown true values of the measurand. The framework was validated on six synthetic and one clinical dataset containing measurements of total lesion load, a biomarker of neurodegenerative diseases, which was obtained with four automatic methods by analyzing brain magnetic resonance images. The estimates of bias and random error were in a good agreement with the corresponding least squares regression estimates against a reference.

Translating Radiometric Requirements for Satellite Sensors to Match International Standards.

PubMed

Pearlman, Aaron; Datla, Raju; Kacker, Raghu; Cao, Changyong

2014-01-01

International scientific standards organizations created standards on evaluating uncertainty in the early 1990s. Although scientists from many fields use these standards, they are not consistently implemented in the remote sensing community, where traditional error analysis framework persists. For a satellite instrument under development, this can create confusion in showing whether requirements are met. We aim to create a methodology for translating requirements from the error analysis framework to the modern uncertainty approach using the product level requirements of the Advanced Baseline Imager (ABI) that will fly on the Geostationary Operational Environmental Satellite R-Series (GOES-R). In this paper we prescribe a method to combine several measurement performance requirements, written using a traditional error analysis framework, into a single specification using the propagation of uncertainties formula. By using this approach, scientists can communicate requirements in a consistent uncertainty framework leading to uniform interpretation throughout the development and operation of any satellite instrument.

Translating Radiometric Requirements for Satellite Sensors to Match International Standards

PubMed Central

Pearlman, Aaron; Datla, Raju; Kacker, Raghu; Cao, Changyong

2014-01-01

International scientific standards organizations created standards on evaluating uncertainty in the early 1990s. Although scientists from many fields use these standards, they are not consistently implemented in the remote sensing community, where traditional error analysis framework persists. For a satellite instrument under development, this can create confusion in showing whether requirements are met. We aim to create a methodology for translating requirements from the error analysis framework to the modern uncertainty approach using the product level requirements of the Advanced Baseline Imager (ABI) that will fly on the Geostationary Operational Environmental Satellite R-Series (GOES-R). In this paper we prescribe a method to combine several measurement performance requirements, written using a traditional error analysis framework, into a single specification using the propagation of uncertainties formula. By using this approach, scientists can communicate requirements in a consistent uncertainty framework leading to uniform interpretation throughout the development and operation of any satellite instrument. PMID:26601032

Towards an evaluation framework for Laboratory Information Systems.

PubMed

Yusof, Maryati M; Arifin, Azila

Laboratory testing and reporting are error-prone and redundant due to repeated, unnecessary requests and delayed or missed reactions to laboratory reports. Occurring errors may negatively affect the patient treatment process and clinical decision making. Evaluation on laboratory testing and Laboratory Information System (LIS) may explain the root cause to improve the testing process and enhance LIS in supporting the process. This paper discusses a new evaluation framework for LIS that encompasses the laboratory testing cycle and the socio-technical part of LIS. Literature review on discourses, dimensions and evaluation methods of laboratory testing and LIS. A critical appraisal of the Total Testing Process (TTP) and the human, organization, technology-fit factors (HOT-fit) evaluation frameworks was undertaken in order to identify error incident, its contributing factors and preventive action pertinent to laboratory testing process and LIS. A new evaluation framework for LIS using a comprehensive and socio-technical approach is outlined. Positive relationship between laboratory and clinical staff resulted in a smooth laboratory testing process, reduced errors and increased process efficiency whilst effective use of LIS streamlined the testing processes. The TTP-LIS framework could serve as an assessment as well as a problem-solving tool for the laboratory testing process and system. Copyright © 2016 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

Balancing the stochastic description of uncertainties as a function of hydrologic model complexity

NASA Astrophysics Data System (ADS)

Del Giudice, D.; Reichert, P.; Albert, C.; Kalcic, M.; Logsdon Muenich, R.; Scavia, D.; Bosch, N. S.; Michalak, A. M.

2016-12-01

Uncertainty analysis is becoming an important component of forecasting water and pollutant fluxes in urban and rural environments. Properly accounting for errors in the modeling process can help to robustly assess the uncertainties associated with the inputs (e.g. precipitation) and outputs (e.g. runoff) of hydrological models. In recent years we have investigated several Bayesian methods to infer the parameters of a mechanistic hydrological model along with those of the stochastic error component. The latter describes the uncertainties of model outputs and possibly inputs. We have adapted our framework to a variety of applications, ranging from predicting floods in small stormwater systems to nutrient loads in large agricultural watersheds. Given practical constraints, we discuss how in general the number of quantities to infer probabilistically varies inversely with the complexity of the mechanistic model. Most often, when evaluating a hydrological model of intermediate complexity, we can infer the parameters of the model as well as of the output error model. Describing the output errors as a first order autoregressive process can realistically capture the "downstream" effect of inaccurate inputs and structure. With simpler runoff models we can additionally quantify input uncertainty by using a stochastic rainfall process. For complex hydrologic transport models, instead, we show that keeping model parameters fixed and just estimating time-dependent output uncertainties could be a viable option. The common goal across all these applications is to create time-dependent prediction intervals which are both reliable (cover the nominal amount of validation data) and precise (are as narrow as possible). In conclusion, we recommend focusing both on the choice of the hydrological model and of the probabilistic error description. The latter can include output uncertainty only, if the model is computationally-expensive, or, with simpler models, it can separately account for different sources of errors like in the inputs and the structure of the model.

Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar Data

NASA Technical Reports Server (NTRS)

Babcock, Chad; Finley, Andrew O.; Cook, Bruce D.; Weiskittel, Andrew; Woodall, Christopher W.

2016-01-01

Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data-a problem pervasive in such settings-by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects.

Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation.

PubMed

Fleming, Stephen M; Daw, Nathaniel D

2017-01-01

People are often aware of their mistakes, and report levels of confidence in their choices that correlate with objective performance. These metacognitive assessments of decision quality are important for the guidance of behavior, particularly when external feedback is absent or sporadic. However, a computational framework that accounts for both confidence and error detection is lacking. In addition, accounts of dissociations between performance and metacognition have often relied on ad hoc assumptions, precluding a unified account of intact and impaired self-evaluation. Here we present a general Bayesian framework in which self-evaluation is cast as a "second-order" inference on a coupled but distinct decision system, computationally equivalent to inferring the performance of another actor. Second-order computation may ensue whenever there is a separation between internal states supporting decisions and confidence estimates over space and/or time. We contrast second-order computation against simpler first-order models in which the same internal state supports both decisions and confidence estimates. Through simulations we show that second-order computation provides a unified account of different types of self-evaluation often considered in separate literatures, such as confidence and error detection, and generates novel predictions about the contribution of one's own actions to metacognitive judgments. In addition, the model provides insight into why subjects' metacognition may sometimes be better or worse than task performance. We suggest that second-order computation may underpin self-evaluative judgments across a range of domains. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation

PubMed Central

2017-01-01

People are often aware of their mistakes, and report levels of confidence in their choices that correlate with objective performance. These metacognitive assessments of decision quality are important for the guidance of behavior, particularly when external feedback is absent or sporadic. However, a computational framework that accounts for both confidence and error detection is lacking. In addition, accounts of dissociations between performance and metacognition have often relied on ad hoc assumptions, precluding a unified account of intact and impaired self-evaluation. Here we present a general Bayesian framework in which self-evaluation is cast as a “second-order” inference on a coupled but distinct decision system, computationally equivalent to inferring the performance of another actor. Second-order computation may ensue whenever there is a separation between internal states supporting decisions and confidence estimates over space and/or time. We contrast second-order computation against simpler first-order models in which the same internal state supports both decisions and confidence estimates. Through simulations we show that second-order computation provides a unified account of different types of self-evaluation often considered in separate literatures, such as confidence and error detection, and generates novel predictions about the contribution of one’s own actions to metacognitive judgments. In addition, the model provides insight into why subjects’ metacognition may sometimes be better or worse than task performance. We suggest that second-order computation may underpin self-evaluative judgments across a range of domains. PMID:28004960

Trends in Health Information Technology Safety: From Technology-Induced Errors to Current Approaches for Ensuring Technology Safety

PubMed Central

2013-01-01

Objectives Health information technology (HIT) research findings suggested that new healthcare technologies could reduce some types of medical errors while at the same time introducing classes of medical errors (i.e., technology-induced errors). Technology-induced errors have their origins in HIT, and/or HIT contribute to their occurrence. The objective of this paper is to review current trends in the published literature on HIT safety. Methods A review and synthesis of the medical and life sciences literature focusing on the area of technology-induced error was conducted. Results There were four main trends in the literature on technology-induced error. The following areas were addressed in the literature: definitions of technology-induced errors; models, frameworks and evidence for understanding how technology-induced errors occur; a discussion of monitoring; and methods for preventing and learning about technology-induced errors. Conclusions The literature focusing on technology-induced errors continues to grow. Research has focused on the defining what an error is, models and frameworks used to understand these new types of errors, monitoring of such errors and methods that can be used to prevent these errors. More research will be needed to better understand and mitigate these types of errors. PMID:23882411

Recommendations to Improve the Accuracy of Estimates of Physical Activity Derived from Self Report

PubMed Central

Ainsworth, Barbara E; Caspersen, Carl J; Matthews, Charles E; Mâsse, Louise C; Baranowski, Tom; Zhu, Weimo

2013-01-01

Context Assessment of physical activity using self-report has the potential for measurement error that can lead to incorrect inferences about physical activity behaviors and bias study results. Objective To provide recommendations to improve the accuracy of physical activity derived from self report. Process We provide an overview of presentations and a compilation of perspectives shared by the authors of this paper and workgroup members. Findings We identified a conceptual framework for reducing errors using physical activity self-report questionnaires. The framework identifies six steps to reduce error: (1) identifying the need to measure physical activity, (2) selecting an instrument, (3) collecting data, (4) analyzing data, (5) developing a summary score, and (6) interpreting data. Underlying the first four steps are behavioral parameters of type, intensity, frequency, and duration of physical activities performed, activity domains, and the location where activities are performed. We identified ways to reduce measurement error at each step and made recommendations for practitioners, researchers, and organizational units to reduce error in questionnaire assessment of physical activity. Conclusions Self-report measures of physical activity have a prominent role in research and practice settings. Measurement error can be reduced by applying the framework discussed in this paper. PMID:22287451

New efficient optimizing techniques for Kalman filters and numerical weather prediction models

NASA Astrophysics Data System (ADS)

Famelis, Ioannis; Galanis, George; Liakatas, Aristotelis

2016-06-01

The need for accurate local environmental predictions and simulations beyond the classical meteorological forecasts are increasing the last years due to the great number of applications that are directly or not affected: renewable energy resource assessment, natural hazards early warning systems, global warming and questions on the climate change can be listed among them. Within this framework the utilization of numerical weather and wave prediction systems in conjunction with advanced statistical techniques that support the elimination of the model bias and the reduction of the error variability may successfully address the above issues. In the present work, new optimization methods are studied and tested in selected areas of Greece where the use of renewable energy sources is of critical. The added value of the proposed work is due to the solid mathematical background adopted making use of Information Geometry and Statistical techniques, new versions of Kalman filters and state of the art numerical analysis tools.

The information value of early career productivity in mathematics: a ROC analysis of prediction errors in bibliometricly informed decision making.

PubMed

Lindahl, Jonas; Danell, Rickard

The aim of this study was to provide a framework to evaluate bibliometric indicators as decision support tools from a decision making perspective and to examine the information value of early career publication rate as a predictor of future productivity. We used ROC analysis to evaluate a bibliometric indicator as a tool for binary decision making. The dataset consisted of 451 early career researchers in the mathematical sub-field of number theory. We investigated the effect of three different definitions of top performance groups-top 10, top 25, and top 50 %; the consequences of using different thresholds in the prediction models; and the added prediction value of information on early career research collaboration and publications in prestige journals. We conclude that early career performance productivity has an information value in all tested decision scenarios, but future performance is more predictable if the definition of a high performance group is more exclusive. Estimated optimal decision thresholds using the Youden index indicated that the top 10 % decision scenario should use 7 articles, the top 25 % scenario should use 7 articles, and the top 50 % should use 5 articles to minimize prediction errors. A comparative analysis between the decision thresholds provided by the Youden index which take consequences into consideration and a method commonly used in evaluative bibliometrics which do not take consequences into consideration when determining decision thresholds, indicated that differences are trivial for the top 25 and the 50 % groups. However, a statistically significant difference between the methods was found for the top 10 % group. Information on early career collaboration and publication strategies did not add any prediction value to the bibliometric indicator publication rate in any of the models. The key contributions of this research is the focus on consequences in terms of prediction errors and the notion of transforming uncertainty into risk when we are choosing decision thresholds in bibliometricly informed decision making. The significance of our results are discussed from the point of view of a science policy and management.

Plant traits determine forest flammability

NASA Astrophysics Data System (ADS)

Zylstra, Philip; Bradstock, Ross

2016-04-01

Carbon and nutrient cycles in forest ecosystems are influenced by their inherent flammability - a property determined by the traits of the component plant species that form the fuel and influence the micro climate of a fire. In the absence of a model capable of explaining the complexity of such a system however, flammability is frequently represented by simple metrics such as surface fuel load. The implications of modelling fire - flammability feedbacks using surface fuel load were examined and compared to a biophysical, mechanistic model (Forest Flammability Model) that incorporates the influence of structural plant traits (e.g. crown shape and spacing) and leaf traits (e.g. thickness, dimensions and moisture). Fuels burn with values of combustibility modelled from leaf traits, transferring convective heat along vectors defined by flame angle and with plume temperatures that decrease with distance from the flame. Flames are re-calculated in one-second time-steps, with new leaves within the plant, neighbouring plants or higher strata ignited when the modelled time to ignition is reached, and other leaves extinguishing when their modelled flame duration is exceeded. The relative influence of surface fuels, vegetation structure and plant leaf traits were examined by comparing flame heights modelled using three treatments that successively added these components within the FFM. Validation was performed across a diverse range of eucalypt forests burnt under widely varying conditions during a forest fire in the Brindabella Ranges west of Canberra (ACT) in 2003. Flame heights ranged from 10 cm to more than 20 m, with an average of 4 m. When modelled from surface fuels alone, flame heights were on average 1.5m smaller than observed values, and were predicted within the error range 28% of the time. The addition of plant structure produced predicted flame heights that were on average 1.5m larger than observed, but were correct 53% of the time. The over-prediction in this case was the result of a small number of large errors, where higher strata such as forest canopy were modelled to ignite but did not. The addition of leaf traits largely addressed this error, so that the mean flame height over-prediction was reduced to 0.3m and the fully parameterised FFM gave correct predictions 62% of the time. When small (<1m) flames were excluded, the fully parameterised model correctly predicted flame heights 12 times more often than could be predicted using surface fuels alone, and the Mean Absolute Error was 4 times smaller. The inadequate consideration of plant traits within a mechanistic framework introduces significant error to forest fire behaviour modelling. The FFM provides a solution to this, and an avenue by which plant trait information can be used to better inform Global Vegetation Models and decision-making tools used to mitigate the impacts of fire.

Compound Stimulus Presentation Does Not Deepen Extinction in Human Causal Learning

PubMed Central

Griffiths, Oren; Holmes, Nathan; Westbrook, R. Fred

2017-01-01

Models of associative learning have proposed that cue-outcome learning critically depends on the degree of prediction error encountered during training. Two experiments examined the role of error-driven extinction learning in a human causal learning task. Target cues underwent extinction in the presence of additional cues, which differed in the degree to which they predicted the outcome, thereby manipulating outcome expectancy and, in the absence of any change in reinforcement, prediction error. These prediction error manipulations have each been shown to modulate extinction learning in aversive conditioning studies. While both manipulations resulted in increased prediction error during training, neither enhanced extinction in the present human learning task (one manipulation resulted in less extinction at test). The results are discussed with reference to the types of associations that are regulated by prediction error, the types of error terms involved in their regulation, and how these interact with parameters involved in training. PMID:28232809

Influence of precision of emission characteristic parameters on model prediction error of VOCs/formaldehyde from dry building material.

PubMed

Wei, Wenjuan; Xiong, Jianyin; Zhang, Yinping

2013-01-01

Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs) and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0), the diffusion coefficient (D), and the partition coefficient (K), can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C.

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

PubMed Central

Schiffer, Anne-Marike; Ahlheim, Christiane; Wurm, Moritz F.; Schubotz, Ricarda I.

2012-01-01

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts. PMID:22570715

Predicting solubilisation features of ternary phase diagrams of fully dilutable lecithin linker microemulsions.

PubMed

Nouraei, Mehdi; Acosta, Edgar J

2017-06-01

Fully dilutable microemulsions (μEs), used to design self-microemulsifying delivery system (SMEDS), are formulated as concentrate solutions containing oil and surfactants, without water. As water is added to dilute these systems, various μEs are produced (water-swollen reverse micelles, bicontinuous systems, and oil-swollen micelles), without the onset of phase separation. Currently, the formulation dilutable μEs follows a trial and error approach that has had a limited success. The objective of this work is to introduce the use of the hydrophilic-lipophilic-difference (HLD) and net-average-curvature (NAC) frameworks to predict the solubilisation features of ternary phase diagrams of lecithin-linker μEs and the use of these predictions to guide the formulation of dilutable μEs. To this end, the characteristic curvatures (Cc) of soybean lecithin (surfactant), glycerol monooleate (lipophilic linker) and polyglycerol caprylate (hydrophilic linker) and the equivalent alkane carbon number (EACN) of ethyl caprate (oil) were obtained via phase scans with reference surfactant-oil systems. These parameters were then used to calculate the HLD of lecithin-linkers-ethyl caprate microemulsions. The calculated HLDs were able to predict the phase transitions observed in the phase scans. The NAC was then used to fit and predict phase volumes obtained from salinity phase scans, and to predict the solubilisation features of ternary phase diagrams of the lecithin-linker formulations. The HLD-NAC predictions were reasonably accurate, and indicated that the largest region for dilutable μEs was obtained with slightly negative HLD values. The NAC framework also predicted, and explained, the changes in microemulsion properties along dilution lines. Copyright © 2017 Elsevier Inc. All rights reserved.

The application of SHERPA (Systematic Human Error Reduction and Prediction Approach) in the development of compensatory cognitive rehabilitation strategies for stroke patients with left and right brain damage.

PubMed

Hughes, Charmayne M L; Baber, Chris; Bienkiewicz, Marta; Worthington, Andrew; Hazell, Alexa; Hermsdörfer, Joachim

2015-01-01

Approximately 33% of stroke patients have difficulty performing activities of daily living, often committing errors during the planning and execution of such activities. The objective of this study was to evaluate the ability of the human error identification (HEI) technique SHERPA (Systematic Human Error Reduction and Prediction Approach) to predict errors during the performance of daily activities in stroke patients with left and right hemisphere lesions. Using SHERPA we successfully predicted 36 of the 38 observed errors, with analysis indicating that the proportion of predicted and observed errors was similar for all sub-tasks and severity levels. HEI results were used to develop compensatory cognitive strategies that clinicians could employ to reduce or prevent errors from occurring. This study provides evidence for the reliability and validity of SHERPA in the design of cognitive rehabilitation strategies in stroke populations.

Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation

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

Daly, Don S.; Anderson, Kevin K.; White, Amanda M.

Background: A microarray of enzyme-linked immunosorbent assays, or ELISA microarray, predicts simultaneously the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Making sound biological inferences as well as improving the ELISA microarray process require require both concentration predictions and creditable estimates of their errors. Methods: We present a statistical method based on monotonic spline statistical models, penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict concentrations and estimate prediction errors in ELISA microarray. PCLS restrains the flexible spline to a fit of assay intensitymore » that is a monotone function of protein concentration. With MC, both modeling and measurement errors are combined to estimate prediction error. The spline/PCLS/MC method is compared to a common method using simulated and real ELISA microarray data sets. Results: In contrast to the rigid logistic model, the flexible spline model gave credible fits in almost all test cases including troublesome cases with left and/or right censoring, or other asymmetries. For the real data sets, 61% of the spline predictions were more accurate than their comparable logistic predictions; especially the spline predictions at the extremes of the prediction curve. The relative errors of 50% of comparable spline and logistic predictions differed by less than 20%. Monte Carlo simulation rendered acceptable asymmetric prediction intervals for both spline and logistic models while propagation of error produced symmetric intervals that diverged unrealistically as the standard curves approached horizontal asymptotes. Conclusions: The spline/PCLS/MC method is a flexible, robust alternative to a logistic/NLS/propagation-of-error method to reliably predict protein concentrations and estimate their errors. The spline method simplifies model selection and fitting, and reliably estimates believable prediction errors. For the 50% of the real data sets fit well by both methods, spline and logistic predictions are practically indistinguishable, varying in accuracy by less than 15%. The spline method may be useful when automated prediction across simultaneous assays of numerous proteins must be applied routinely with minimal user intervention.« less

Temporal-difference prediction errors and Pavlovian fear conditioning: role of NMDA and opioid receptors.

PubMed

Cole, Sindy; McNally, Gavan P

2007-10-01

Three experiments studied temporal-difference (TD) prediction errors during Pavlovian fear conditioning. In Stage I, rats received conditioned stimulus A (CSA) paired with shock. In Stage II, they received pairings of CSA and CSB with shock that blocked learning to CSB. In Stage III, a serial overlapping compound, CSB --> CSA, was followed by shock. The change in intratrial durations supported fear learning to CSB but reduced fear of CSA, revealing the operation of TD prediction errors. N-methyl- D-aspartate (NMDA) receptor antagonism prior to Stage III prevented learning, whereas opioid receptor antagonism selectively affected predictive learning. These findings support a role for TD prediction errors in fear conditioning. They suggest that NMDA receptors contribute to fear learning by acting on the product of predictive error, whereas opioid receptors contribute to predictive error. (PsycINFO Database Record (c) 2007 APA, all rights reserved).

Dopamine neurons share common response function for reward prediction error

PubMed Central

Eshel, Neir; Tian, Ju; Bukwich, Michael; Uchida, Naoshige

2016-01-01

Dopamine neurons are thought to signal reward prediction error, or the difference between actual and predicted reward. How dopamine neurons jointly encode this information, however, remains unclear. One possibility is that different neurons specialize in different aspects of prediction error; another is that each neuron calculates prediction error in the same way. We recorded from optogenetically-identified dopamine neurons in the lateral ventral tegmental area (VTA) while mice performed classical conditioning tasks. Our tasks allowed us to determine the full prediction error functions of dopamine neurons and compare them to each other. We found striking homogeneity among individual dopamine neurons: their responses to both unexpected and expected rewards followed the same function, just scaled up or down. As a result, we could describe both individual and population responses using just two parameters. Such uniformity ensures robust information coding, allowing each dopamine neuron to contribute fully to the prediction error signal. PMID:26854803

Temporal specificity of reward prediction errors signaled by putative dopamine neurons in rat VTA depends on ventral striatum

PubMed Central

Takahashi, Yuji K.; Langdon, Angela J.; Niv, Yael; Schoenbaum, Geoffrey

2016-01-01

Summary Dopamine neurons signal reward prediction errors. This requires accurate reward predictions. It has been suggested that the ventral striatum provides these predictions. Here we tested this hypothesis by recording from putative dopamine neurons in the VTA of rats performing a task in which prediction errors were induced by shifting reward timing or number. In controls, the neurons exhibited error signals in response to both manipulations. However, dopamine neurons in rats with ipsilateral ventral striatal lesions exhibited errors only to changes in number and failed to respond to changes in timing of reward. These results, supported by computational modeling, indicate that predictions about the temporal specificity and the number of expected rewards are dissociable, and that dopaminergic prediction-error signals rely on the ventral striatum for the former but not the latter. PMID:27292535

Injury Profile SIMulator, a Qualitative Aggregative Modelling Framework to Predict Injury Profile as a Function of Cropping Practices, and Abiotic and Biotic Environment. II. Proof of Concept: Design of IPSIM-Wheat-Eyespot

PubMed Central

Robin, Marie-Hélène; Colbach, Nathalie; Lucas, Philippe; Montfort, Françoise; Cholez, Célia; Debaeke, Philippe; Aubertot, Jean-Noël

2013-01-01

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM's modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM's modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiency = 0.51, Root Mean Square Error of Prediction = 24%; bias = 5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation. PMID:24146783

Bayesian Modeling of Exposure and Airflow Using Two-Zone Models

PubMed Central

Zhang, Yufen; Banerjee, Sudipto; Yang, Rui; Lungu, Claudiu; Ramachandran, Gurumurthy

2009-01-01

Mathematical modeling is being increasingly used as a means for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Validation of models in occupational settings is, therefore, a challenge. Not only do the model parameters need to be known, the models also need to predict the output with some degree of accuracy. In this paper, a Bayesian statistical framework is used for estimating model parameters and exposure concentrations for a two-zone model. The model predicts concentrations in a zone near the source and far away from the source as functions of the toluene generation rate, air ventilation rate through the chamber, and the airflow between near and far fields. The framework combines prior or expert information on the physical model along with the observed data. The framework is applied to simulated data as well as data obtained from the experiments conducted in a chamber. Toluene vapors are generated from a source under different conditions of airflow direction, the presence of a mannequin, and simulated body heat of the mannequin. The Bayesian framework accounts for uncertainty in measurement as well as in the unknown rate of airflow between the near and far fields. The results show that estimates of the interzonal airflow are always close to the estimated equilibrium solutions, which implies that the method works efficiently. The predictions of near-field concentration for both the simulated and real data show nice concordance with the true values, indicating that the two-zone model assumptions agree with the reality to a large extent and the model is suitable for predicting the contaminant concentration. Comparison of the estimated model and its margin of error with the experimental data thus enables validation of the physical model assumptions. The approach illustrates how exposure models and information on model parameters together with the knowledge of uncertainty and variability in these quantities can be used to not only provide better estimates of model outputs but also model parameters. PMID:19403840

Conditional power and predictive power based on right censored data with supplementary auxiliary information.

PubMed

Sun, Libo; Wan, Ying

2018-04-22

Conditional power and predictive power provide estimates of the probability of success at the end of the trial based on the information from the interim analysis. The observed value of the time to event endpoint at the interim analysis could be biased for the true treatment effect due to early censoring, leading to a biased estimate of conditional power and predictive power. In such cases, the estimates and inference for this right censored primary endpoint are enhanced by incorporating a fully observed auxiliary variable. We assume a bivariate normal distribution of the transformed primary variable and a correlated auxiliary variable. Simulation studies are conducted that not only shows enhanced conditional power and predictive power but also can provide the framework for a more efficient futility interim analysis in terms of an improved accuracy in estimator, a smaller inflation in type II error and an optimal timing for such analysis. We also illustrated the new approach by a real clinical trial example. Copyright © 2018 John Wiley & Sons, Ltd.

Visuomotor adaptation needs a validation of prediction error by feedback error

PubMed Central

Gaveau, Valérie; Prablanc, Claude; Laurent, Damien; Rossetti, Yves; Priot, Anne-Emmanuelle

2014-01-01

The processes underlying short-term plasticity induced by visuomotor adaptation to a shifted visual field are still debated. Two main sources of error can induce motor adaptation: reaching feedback errors, which correspond to visually perceived discrepancies between hand and target positions, and errors between predicted and actual visual reafferences of the moving hand. These two sources of error are closely intertwined and difficult to disentangle, as both the target and the reaching limb are simultaneously visible. Accordingly, the goal of the present study was to clarify the relative contributions of these two types of errors during a pointing task under prism-displaced vision. In “terminal feedback error” condition, viewing of their hand by subjects was allowed only at movement end, simultaneously with viewing of the target. In “movement prediction error” condition, viewing of the hand was limited to movement duration, in the absence of any visual target, and error signals arose solely from comparisons between predicted and actual reafferences of the hand. In order to prevent intentional corrections of errors, a subthreshold, progressive stepwise increase in prism deviation was used, so that subjects remained unaware of the visual deviation applied in both conditions. An adaptive aftereffect was observed in the “terminal feedback error” condition only. As far as subjects remained unaware of the optical deviation and self-assigned pointing errors, prediction error alone was insufficient to induce adaptation. These results indicate a critical role of hand-to-target feedback error signals in visuomotor adaptation; consistent with recent neurophysiological findings, they suggest that a combination of feedback and prediction error signals is necessary for eliciting aftereffects. They also suggest that feedback error updates the prediction of reafferences when a visual perturbation is introduced gradually and cognitive factors are eliminated or strongly attenuated. PMID:25408644

A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA

USGS Publications Warehouse

Nolan, Bernard T.; Fienen, Michael N.; Lorenz, David L.

2015-01-01

We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R2 and a version with R2 within one standard error of the maximum (the 1SE model). The former yielded CV training R2 values of 0.94–1.0. Cross-validation testing R2 values indicate predictive performance, and these were 0.22–0.39 for the maximum R2 models and 0.19–0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.

Interactions of timing and prediction error learning.

PubMed

Kirkpatrick, Kimberly

2014-01-01

Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, through changes in reward magnitude or value alter timing of behavior. Thus, there appears to be a bi-directional interaction between timing and prediction error learning. Modern theories have attempted to integrate the two processes with mixed success. A neurocomputational approach to theory development is espoused, which draws on neurobiological evidence to guide and constrain computational model development. Heuristics for future model development are presented with the goal of sparking new approaches to theory development in the timing and prediction error fields. Copyright © 2013 Elsevier B.V. All rights reserved.

Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal ganglia.

PubMed

Diuk, Carlos; Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew; Niv, Yael

2013-03-27

Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously.

Influence of Precision of Emission Characteristic Parameters on Model Prediction Error of VOCs/Formaldehyde from Dry Building Material

PubMed Central

Wei, Wenjuan; Xiong, Jianyin; Zhang, Yinping

2013-01-01

Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs) and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0), the diffusion coefficient (D), and the partition coefficient (K), can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C. PMID:24312497

Application of optimal data assimilation techniques in oceanography

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

Miller, R.N.

Application of optimal data assimilation methods in oceanography is, if anything, more important than it is in numerical weather prediction, due to the sparsity of data. Here, a general framework is presented and practical examples taken from the author`s work are described, with the purpose of conveying to the reader some idea of the state of the art of data assimilation in oceanography. While no attempt is made to be exhaustive, references to other lines of research are included. Major challenges to the community include design of statistical error models and handling of strong nonlinearity.

Quantum-mechanical treatment of an electron undergoing synchrotron radiation.

NASA Technical Reports Server (NTRS)

White, D.

1972-01-01

The problem of an electron moving perpendicular to an intense magnetic field is approached from the framework of quantum mechanics. A numerical solution to the related rate equations describing the probabilities of occupation of the electron's energy states is put forth along with the expected errors involved. The quantum-mechanical approach is found to predict a significant amount of energy broadening with time for an initially monoenergetic electron beam entering a region of an intense magnetic field as long as the product of initial energy and magnetic field is of order 50 MG BeV or larger.

Neurocomputational mechanisms of prosocial learning and links to empathy.

PubMed

Lockwood, Patricia L; Apps, Matthew A J; Valton, Vincent; Viding, Essi; Roiser, Jonathan P

2016-08-30

Reinforcement learning theory powerfully characterizes how we learn to benefit ourselves. In this theory, prediction errors-the difference between a predicted and actual outcome of a choice-drive learning. However, we do not operate in a social vacuum. To behave prosocially we must learn the consequences of our actions for other people. Empathy, the ability to vicariously experience and understand the affect of others, is hypothesized to be a critical facilitator of prosocial behaviors, but the link between empathy and prosocial behavior is still unclear. During functional magnetic resonance imaging (fMRI) participants chose between different stimuli that were probabilistically associated with rewards for themselves (self), another person (prosocial), or no one (control). Using computational modeling, we show that people can learn to obtain rewards for others but do so more slowly than when learning to obtain rewards for themselves. fMRI revealed that activity in a posterior portion of the subgenual anterior cingulate cortex/basal forebrain (sgACC) drives learning only when we are acting in a prosocial context and signals a prosocial prediction error conforming to classical principles of reinforcement learning theory. However, there is also substantial variability in the neural and behavioral efficiency of prosocial learning, which is predicted by trait empathy. More empathic people learn more quickly when benefitting others, and their sgACC response is the most selective for prosocial learning. We thus reveal a computational mechanism driving prosocial learning in humans. This framework could provide insights into atypical prosocial behavior in those with disorders of social cognition.

Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions.

PubMed

Vosmeer, C Ruben; Kooi, Derk P; Capoferri, Luigi; Terpstra, Margreet M; Vermeulen, Nico P E; Geerke, Daan P

2016-01-01

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

PubMed Central

Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-01-01

Background Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Objective Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Methods Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Results Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. Conclusions To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. PMID:29506966

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation.

PubMed

Sadat, Md Nazmus; Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-03-05

Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. ©Md Nazmus Sadat, Xiaoqian Jiang, Md Momin Al Aziz, Shuang Wang, Noman Mohammed. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.03.2018.

Thread mapping using system-level model for shared memory multicores

NASA Astrophysics Data System (ADS)

Mitra, Reshmi

Exploring thread-to-core mapping options for a parallel application on a multicore architecture is computationally very expensive. For the same algorithm, the mapping strategy (MS) with the best response time may change with data size and thread counts. The primary challenge is to design a fast, accurate and automatic framework for exploring these MSs for large data-intensive applications. This is to ensure that the users can explore the design space within reasonable machine hours, without thorough understanding on how the code interacts with the platform. Response time is related to the cycles per instructions retired (CPI), taking into account both active and sleep states of the pipeline. This work establishes a hybrid approach, based on Markov Chain Model (MCM) and Model Tree (MT) for system-level steady state CPI prediction. It is designed for shared memory multicore processors with coarse-grained multithreading. The thread status is represented by the MCM states. The program characteristics are modeled as the transition probabilities, representing the system moving between active and suspended thread states. The MT model extrapolates these probabilities for the actual application size (AS) from the smaller AS performance. This aspect of the framework, along with, the use of mathematical expressions for the actual AS performance information, results in a tremendous reduction in the CPI prediction time. The framework is validated using an electromagnetics application. The average performance prediction error for steady state CPI results with 12 different MSs is less than 1%. The total run time of model is of the order of minutes, whereas the actual application execution time is in terms of days.

A Framework for Performing V&V within Reuse-Based Software Engineering

NASA Technical Reports Server (NTRS)

Addy, Edward A.

1996-01-01

Verification and validation (V&V) is performed during application development for many systems, especially safety-critical and mission-critical systems. The V&V process is intended to discover errors, especially errors related to critical processing, as early as possible during the development process. Early discovery is important in order to minimize the cost and other impacts of correcting these errors. In order to provide early detection of errors, V&V is conducted in parallel with system development, often beginning with the concept phase. In reuse-based software engineering, however, decisions on the requirements, design and even implementation of domain assets can be made prior to beginning development of a specific system. In this case, V&V must be performed during domain engineering in order to have an impact on system development. This paper describes a framework for performing V&V within architecture-centric, reuse-based software engineering. This framework includes the activities of traditional application-level V&V, and extends these activities into domain engineering and into the transition between domain engineering and application engineering. The framework includes descriptions of the types of activities to be performed during each of the life-cycle phases, and provides motivation for the activities.

Predicting the thermal/structural performance of the atmospheric trace molecules spectroscopy /ATMOS/ Fourier transform spectrometer

NASA Technical Reports Server (NTRS)

Miller, J. M.

1980-01-01

ATMOS is a Fourier transform spectrometer to measure atmospheric trace molecules over a spectral range of 2-16 microns. Assessment of the system performance of ATMOS includes evaluations of optical system errors induced by thermal and structural effects. In order to assess the optical system errors induced from thermal and structural effects, error budgets are assembled during system engineering tasks and line of sight and wavefront deformations predictions (using operational thermal and vibration environments and computer models) are subsequently compared to the error budgets. This paper discusses the thermal/structural error budgets, modelling and analysis methods used to predict thermal/structural induced errors and the comparisons that show that predictions are within the error budgets.

A hybrid approach to estimate the complex motions of clouds in sky images

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

Peng, Zhenzhou; Yu, Dantong; Huang, Dong

Tracking the motion of clouds is essential to forecasting the weather and to predicting the short-term solar energy generation. Existing techniques mainly fall into two categories: variational optical flow, and block matching. In this article, we summarize recent advances in estimating cloud motion using ground-based sky imagers and quantitatively evaluate state-of-the-art approaches. Then we propose a hybrid tracking framework to incorporate the strength of both block matching and optical flow models. To validate the accuracy of the proposed approach, we introduce a series of synthetic images to simulate the cloud movement and deformation, and thereafter comprehensively compare our hybrid approachmore » with several representative tracking algorithms over both simulated and real images collected from various sites/imagers. The results show that our hybrid approach outperforms state-of-the-art models by reducing at least 30% motion estimation errors compared with the ground-truth motions in most of simulated image sequences. Furthermore, our hybrid model demonstrates its superior efficiency in several real cloud image datasets by lowering at least 15% Mean Absolute Error (MAE) between predicted images and ground-truth images.« less

A hybrid approach to estimate the complex motions of clouds in sky images

DOE PAGES

Peng, Zhenzhou; Yu, Dantong; Huang, Dong; ...

2016-09-14

Tracking the motion of clouds is essential to forecasting the weather and to predicting the short-term solar energy generation. Existing techniques mainly fall into two categories: variational optical flow, and block matching. In this article, we summarize recent advances in estimating cloud motion using ground-based sky imagers and quantitatively evaluate state-of-the-art approaches. Then we propose a hybrid tracking framework to incorporate the strength of both block matching and optical flow models. To validate the accuracy of the proposed approach, we introduce a series of synthetic images to simulate the cloud movement and deformation, and thereafter comprehensively compare our hybrid approachmore » with several representative tracking algorithms over both simulated and real images collected from various sites/imagers. The results show that our hybrid approach outperforms state-of-the-art models by reducing at least 30% motion estimation errors compared with the ground-truth motions in most of simulated image sequences. Furthermore, our hybrid model demonstrates its superior efficiency in several real cloud image datasets by lowering at least 15% Mean Absolute Error (MAE) between predicted images and ground-truth images.« less

Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

Klotz, S.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

2013-12-01

The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography. These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as multi-model ensemble forecasting, to U.S. Department of Defense use, and creating a superior U.S. global meteorological and oceanographic prediction capability. Forecast verification is an important component of NAEFS and NUOPC. Distribution Statement A: Approved for Public Release; distribution is unlimited

Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

Klotz, S. P.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

2012-12-01

The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography (METOC). These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as multi-model ensemble forecasting, to U.S. Department of Defense use, and creating a superior U.S. global meteorological and oceanographic prediction capability. Forecast verification is an important component of NAEFS and NUOPC.

Multi-scale assimilation of remotely sensed snow observations for hydrologic estimation

NASA Astrophysics Data System (ADS)

Andreadis, K.; Lettenmaier, D.

2008-12-01

Data assimilation provides a framework for optimally merging model predictions and remote sensing observations of snow properties (snow cover extent, water equivalent, grain size, melt state), ideally overcoming limitations of both. A synthetic twin experiment is used to evaluate a data assimilation system that would ingest remotely sensed observations from passive microwave and visible wavelength sensors (brightness temperature and snow cover extent derived products, respectively) with the objective of estimating snow water equivalent. Two data assimilation techniques are used, the Ensemble Kalman filter and the Ensemble Multiscale Kalman filter (EnMKF). One of the challenges inherent in such a data assimilation system is the discrepancy in spatial scales between the different types of snow-related observations. The EnMKF represents the sample model error covariance with a tree that relates the system state variables at different locations and scales through a set of parent-child relationships. This provides an attractive framework to efficiently assimilate observations at different spatial scales. This study provides a first assessment of the feasibility of a system that would assimilate observations from multiple sensors (MODIS snow cover and AMSR-E brightness temperatures) and at different spatial scales for snow water equivalent estimation. The relative value of the different types of observations is examined. Additionally, the error characteristics of both model and observations are discussed.

Exploring Situational Awareness in Diagnostic Errors in Primary Care

PubMed Central

Singh, Hardeep; Giardina, Traber Davis; Petersen, Laura A.; Smith, Michael; Wilson, Lindsey; Dismukes, Key; Bhagwath, Gayathri; Thomas, Eric J.

2013-01-01

Objective Diagnostic errors in primary care are harmful but poorly studied. To facilitate understanding of diagnostic errors in real-world primary care settings using electronic health records (EHRs), this study explored the use of the Situational Awareness (SA) framework from aviation human factors research. Methods A mixed-methods study was conducted involving reviews of EHR data followed by semi-structured interviews of selected providers from two institutions in the US. The study population included 380 consecutive patients with colorectal and lung cancers diagnosed between February 2008 and January 2009. Using a pre-tested data collection instrument, trained physicians identified diagnostic errors, defined as lack of timely action on one or more established indications for diagnostic work-up for lung and colorectal cancers. Twenty-six providers involved in cases with and without errors were interviewed. Interviews probed for providers' lack of SA and how this may have influenced the diagnostic process. Results Of 254 cases meeting inclusion criteria, errors were found in 30 (32.6%) of 92 lung cancer cases and 56 (33.5%) of 167 colorectal cancer cases. Analysis of interviews related to error cases revealed evidence of lack of one of four levels of SA applicable to primary care practice: information perception, information comprehension, forecasting future events, and choosing appropriate action based on the first three levels. In cases without error, the application of the SA framework provided insight into processes involved in attention management. Conclusions A framework of SA can help analyze and understand diagnostic errors in primary care settings that use EHRs. PMID:21890757

Disrupted prediction-error signal in psychosis: evidence for an associative account of delusions

PubMed Central

Corlett, P. R.; Murray, G. K.; Honey, G. D.; Aitken, M. R. F.; Shanks, D. R.; Robbins, T.W.; Bullmore, E.T.; Dickinson, A.; Fletcher, P. C.

2012-01-01

Delusions are maladaptive beliefs about the world. Based upon experimental evidence that prediction error—a mismatch between expectancy and outcome—drives belief formation, this study examined the possibility that delusions form because of disrupted prediction-error processing. We used fMRI to determine prediction-error-related brain responses in 12 healthy subjects and 12 individuals (7 males) with delusional beliefs. Frontal cortex responses in the patient group were suggestive of disrupted prediction-error processing. Furthermore, across subjects, the extent of disruption was significantly related to an individual’s propensity to delusion formation. Our results support a neurobiological theory of delusion formation that implicates aberrant prediction-error signalling, disrupted attentional allocation and associative learning in the formation of delusional beliefs. PMID:17690132

An effective framework for finding similar cases of dengue from audio and text data using domain thesaurus and case base reasoning

NASA Astrophysics Data System (ADS)

Sandhu, Rajinder; Kaur, Jaspreet; Thapar, Vivek

2018-02-01

Dengue, also known as break-bone fever, is a tropical disease transmitted by mosquitoes. If the similarity between dengue infected users can be identified, it can help government's health agencies to manage the outbreak more effectively. To find similarity between cases affected by Dengue, user's personal and health information are the two fundamental requirements. Identification of similar symptoms, causes, effects, predictions and treatment procedures, is important. In this paper, an effective framework is proposed which finds similar patients suffering from dengue using keyword aware domain thesaurus and case base reasoning method. This paper focuses on the use of ontology dependent domain thesaurus technique to extract relevant keywords and then build cases with the help of case base reasoning method. Similar cases can be shared with users, nearby hospitals and health organizations to manage the problem more adequately. Two million case bases were generated to test the proposed similarity method. Experimental evaluations of proposed framework resulted in high accuracy and low error rate for finding similar cases of dengue as compared to UPCC and IPCC algorithms. The framework developed in this paper is for dengue but can easily be extended to other domains also.

A Framework for Debugging Geoscience Projects in a High Performance Computing Environment

NASA Astrophysics Data System (ADS)

Baxter, C.; Matott, L.

2012-12-01

High performance computing (HPC) infrastructure has become ubiquitous in today's world with the emergence of commercial cloud computing and academic supercomputing centers. Teams of geoscientists, hydrologists and engineers can take advantage of this infrastructure to undertake large research projects - for example, linking one or more site-specific environmental models with soft computing algorithms, such as heuristic global search procedures, to perform parameter estimation and predictive uncertainty analysis, and/or design least-cost remediation systems. However, the size, complexity and distributed nature of these projects can make identifying failures in the associated numerical experiments using conventional ad-hoc approaches both time- consuming and ineffective. To address these problems a multi-tiered debugging framework has been developed. The framework allows for quickly isolating and remedying a number of potential experimental failures, including: failures in the HPC scheduler; bugs in the soft computing code; bugs in the modeling code; and permissions and access control errors. The utility of the framework is demonstrated via application to a series of over 200,000 numerical experiments involving a suite of 5 heuristic global search algorithms and 15 mathematical test functions serving as cheap analogues for the simulation-based optimization of pump-and-treat subsurface remediation systems.

Hierarchical Learning Induces Two Simultaneous, But Separable, Prediction Errors in Human Basal Ganglia

PubMed Central

Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew

2013-01-01

Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously. PMID:23536092

The Binding of Learning to Action in Motor Adaptation

PubMed Central

Gonzalez Castro, Luis Nicolas; Monsen, Craig Bryant; Smith, Maurice A.

2011-01-01

In motor tasks, errors between planned and actual movements generally result in adaptive changes which reduce the occurrence of similar errors in the future. It has commonly been assumed that the motor adaptation arising from an error occurring on a particular movement is specifically associated with the motion that was planned. Here we show that this is not the case. Instead, we demonstrate the binding of the adaptation arising from an error on a particular trial to the motion experienced on that same trial. The formation of this association means that future movements planned to resemble the motion experienced on a given trial benefit maximally from the adaptation arising from it. This reflects the idea that actual rather than planned motions are assigned ‘credit’ for motor errors because, in a computational sense, the maximal adaptive response would be associated with the condition credited with the error. We studied this process by examining the patterns of generalization associated with motor adaptation to novel dynamic environments during reaching arm movements in humans. We found that these patterns consistently matched those predicted by adaptation associated with the actual rather than the planned motion, with maximal generalization observed where actual motions were clustered. We followed up these findings by showing that a novel training procedure designed to leverage this newfound understanding of the binding of learning to action, can improve adaptation rates by greater than 50%. Our results provide a mechanistic framework for understanding the effects of partial assistance and error augmentation during neurologic rehabilitation, and they suggest ways to optimize their use. PMID:21731476

Forecasting influenza in Hong Kong with Google search queries and statistical model fusion.

PubMed

Xu, Qinneng; Gel, Yulia R; Ramirez Ramirez, L Leticia; Nezafati, Kusha; Zhang, Qingpeng; Tsui, Kwok-Leung

2017-01-01

The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources. Four individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates include Google search queries, meteorological data, and previously recorded offline ILI. To our knowledge, this is the first study that introduces deep learning methodology into surveillance of infectious diseases and investigates its predictive utility. Furthermore, to exploit the strength from each individual forecasting models, we use statistical model fusion, using Bayesian model averaging (BMA), which allows a systematic integration of multiple forecast scenarios. For each model, an adaptive approach is used to capture the recent relationship between ILI and covariates. DL with FNN appears to deliver the most competitive predictive performance among the four considered individual models. Combing all four models in a comprehensive BMA framework allows to further improve such predictive evaluation metrics as root mean squared error (RMSE) and mean absolute predictive error (MAPE). Nevertheless, DL with FNN remains the preferred method for predicting locations of influenza peaks. The proposed approach can be viewed a feasible alternative to forecast ILI in Hong Kong or other countries where ILI has no constant seasonal trend and influenza data resources are limited. The proposed methodology is easily tractable and computationally efficient.

Does prediction error drive one-shot declarative learning?

PubMed

Greve, Andrea; Cooper, Elisa; Kaula, Alexander; Anderson, Michael C; Henson, Richard

2017-06-01

The role of prediction error (PE) in driving learning is well-established in fields such as classical and instrumental conditioning, reward learning and procedural memory; however, its role in human one-shot declarative encoding is less clear. According to one recent hypothesis, PE reflects the divergence between two probability distributions: one reflecting the prior probability (from previous experiences) and the other reflecting the sensory evidence (from the current experience). Assuming unimodal probability distributions, PE can be manipulated in three ways: (1) the distance between the mode of the prior and evidence, (2) the precision of the prior, and (3) the precision of the evidence. We tested these three manipulations across five experiments, in terms of peoples' ability to encode a single presentation of a scene-item pairing as a function of previous exposures to that scene and/or item. Memory was probed by presenting the scene together with three choices for the previously paired item, in which the two foil items were from other pairings within the same condition as the target item. In Experiment 1, we manipulated the evidence to be either consistent or inconsistent with prior expectations, predicting PE to be larger, and hence memory better, when the new pairing was inconsistent. In Experiments 2a-c, we manipulated the precision of the priors, predicting better memory for a new pairing when the (inconsistent) priors were more precise. In Experiment 3, we manipulated both visual noise and prior exposure for unfamiliar faces, before pairing them with scenes, predicting better memory when the sensory evidence was more precise. In all experiments, the PE hypotheses were supported. We discuss alternative explanations of individual experiments, and conclude the Predictive Interactive Multiple Memory Signals (PIMMS) framework provides the most parsimonious account of the full pattern of results.

New dimension analyses with error analysis for quaking aspen and black spruce

NASA Technical Reports Server (NTRS)

Woods, K. D.; Botkin, D. B.; Feiveson, A. H.

1987-01-01

Dimension analysis for black spruce in wetland stands and trembling aspen are reported, including new approaches in error analysis. Biomass estimates for sacrificed trees have standard errors of 1 to 3%; standard errors for leaf areas are 10 to 20%. Bole biomass estimation accounts for most of the error for biomass, while estimation of branch characteristics and area/weight ratios accounts for the leaf area error. Error analysis provides insight for cost effective design of future analyses. Predictive equations for biomass and leaf area, with empirically derived estimators of prediction error, are given. Systematic prediction errors for small aspen trees and for leaf area of spruce from different site-types suggest a need for different predictive models within species. Predictive equations are compared with published equations; significant differences may be due to species responses to regional or site differences. Proportional contributions of component biomass in aspen change in ways related to tree size and stand development. Spruce maintains comparatively constant proportions with size, but shows changes corresponding to site. This suggests greater morphological plasticity of aspen and significance for spruce of nutrient conditions.

Frontal Theta Links Prediction Errors to Behavioral Adaptation in Reinforcement Learning

PubMed Central

Cavanagh, James F.; Frank, Michael J.; Klein, Theresa J.; Allen, John J.B.

2009-01-01

Investigations into action monitoring have consistently detailed a fronto-central voltage deflection in the Event-Related Potential (ERP) following the presentation of negatively valenced feedback, sometimes termed the Feedback Related Negativity (FRN). The FRN has been proposed to reflect a neural response to prediction errors during reinforcement learning, yet the single trial relationship between neural activity and the quanta of expectation violation remains untested. Although ERP methods are not well suited to single trial analyses, the FRN has been associated with theta band oscillatory perturbations in the medial prefrontal cortex. Medio-frontal theta oscillations have been previously associated with expectation violation and behavioral adaptation and are well suited to single trial analysis. Here, we recorded EEG activity during a probabilistic reinforcement learning task and fit the performance data to an abstract computational model (Q-learning) for calculation of single-trial reward prediction errors. Single-trial theta oscillatory activities following feedback were investigated within the context of expectation (prediction error) and adaptation (subsequent reaction time change). Results indicate that interactive medial and lateral frontal theta activities reflect the degree of negative and positive reward prediction error in the service of behavioral adaptation. These different brain areas use prediction error calculations for different behavioral adaptations: with medial frontal theta reflecting the utilization of prediction errors for reaction time slowing (specifically following errors), but lateral frontal theta reflecting prediction errors leading to working memory-related reaction time speeding for the correct choice. PMID:19969093

Association between split selection instability and predictive error in survival trees.

PubMed

Radespiel-Tröger, M; Gefeller, O; Rabenstein, T; Hothorn, T

2006-01-01

To evaluate split selection instability in six survival tree algorithms and its relationship with predictive error by means of a bootstrap study. We study the following algorithms: logrank statistic with multivariate p-value adjustment without pruning (LR), Kaplan-Meier distance of survival curves (KM), martingale residuals (MR), Poisson regression for censored data (PR), within-node impurity (WI), and exponential log-likelihood loss (XL). With the exception of LR, initial trees are pruned by using split-complexity, and final trees are selected by means of cross-validation. We employ a real dataset from a clinical study of patients with gallbladder stones. The predictive error is evaluated using the integrated Brier score for censored data. The relationship between split selection instability and predictive error is evaluated by means of box-percentile plots, covariate and cutpoint selection entropy, and cutpoint selection coefficients of variation, respectively, in the root node. We found a positive association between covariate selection instability and predictive error in the root node. LR yields the lowest predictive error, while KM and MR yield the highest predictive error. The predictive error of survival trees is related to split selection instability. Based on the low predictive error of LR, we recommend the use of this algorithm for the construction of survival trees. Unpruned survival trees with multivariate p-value adjustment can perform equally well compared to pruned trees. The analysis of split selection instability can be used to communicate the results of tree-based analyses to clinicians and to support the application of survival trees.

Estimation of Separation Buffers for Wind-Prediction Error in an Airborne Separation Assistance System

NASA Technical Reports Server (NTRS)

Consiglio, Maria C.; Hoadley, Sherwood T.; Allen, B. Danette

2009-01-01

Wind prediction errors are known to affect the performance of automated air traffic management tools that rely on aircraft trajectory predictions. In particular, automated separation assurance tools, planned as part of the NextGen concept of operations, must be designed to account and compensate for the impact of wind prediction errors and other system uncertainties. In this paper we describe a high fidelity batch simulation study designed to estimate the separation distance required to compensate for the effects of wind-prediction errors throughout increasing traffic density on an airborne separation assistance system. These experimental runs are part of the Safety Performance of Airborne Separation experiment suite that examines the safety implications of prediction errors and system uncertainties on airborne separation assurance systems. In this experiment, wind-prediction errors were varied between zero and forty knots while traffic density was increased several times current traffic levels. In order to accurately measure the full unmitigated impact of wind-prediction errors, no uncertainty buffers were added to the separation minima. The goal of the study was to measure the impact of wind-prediction errors in order to estimate the additional separation buffers necessary to preserve separation and to provide a baseline for future analyses. Buffer estimations from this study will be used and verified in upcoming safety evaluation experiments under similar simulation conditions. Results suggest that the strategic airborne separation functions exercised in this experiment can sustain wind prediction errors up to 40kts at current day air traffic density with no additional separation distance buffer and at eight times the current day with no more than a 60% increase in separation distance buffer.

Subject-specific finite element modelling of the human foot complex during walking: sensitivity analysis of material properties, boundary and loading conditions.

PubMed

Akrami, Mohammad; Qian, Zhihui; Zou, Zhemin; Howard, David; Nester, Chris J; Ren, Lei

2018-04-01

The objective of this study was to develop and validate a subject-specific framework for modelling the human foot. This was achieved by integrating medical image-based finite element modelling, individualised multi-body musculoskeletal modelling and 3D gait measurements. A 3D ankle-foot finite element model comprising all major foot structures was constructed based on MRI of one individual. A multi-body musculoskeletal model and 3D gait measurements for the same subject were used to define loading and boundary conditions. Sensitivity analyses were used to investigate the effects of key modelling parameters on model predictions. Prediction errors of average and peak plantar pressures were below 10% in all ten plantar regions at five key gait events with only one exception (lateral heel, in early stance, error of 14.44%). The sensitivity analyses results suggest that predictions of peak plantar pressures are moderately sensitive to material properties, ground reaction forces and muscle forces, and significantly sensitive to foot orientation. The maximum region-specific percentage change ratios (peak stress percentage change over parameter percentage change) were 1.935-2.258 for ground reaction forces, 1.528-2.727 for plantar flexor muscles and 4.84-11.37 for foot orientations. This strongly suggests that loading and boundary conditions need to be very carefully defined based on personalised measurement data.

Artificial neural network implementation of a near-ideal error prediction controller

NASA Technical Reports Server (NTRS)

Mcvey, Eugene S.; Taylor, Lynore Denise

1992-01-01

A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error responses be known for a particular input and modeled plant. These responses are used in the error prediction controller. An analysis was done on the general dynamic behavior that results from including a digital error predictor in a control loop and these were compared to those including the near-ideal Neural Network error predictor. This analysis was done for a second and third order system.

Comprehensive preclinical evaluation of a multi-physics model of liver tumor radiofrequency ablation.

PubMed

Audigier, Chloé; Mansi, Tommaso; Delingette, Hervé; Rapaka, Saikiran; Passerini, Tiziano; Mihalef, Viorel; Jolly, Marie-Pierre; Pop, Raoul; Diana, Michele; Soler, Luc; Kamen, Ali; Comaniciu, Dorin; Ayache, Nicholas

2017-09-01

We aim at developing a framework for the validation of a subject-specific multi-physics model of liver tumor radiofrequency ablation (RFA). The RFA computation becomes subject specific after several levels of personalization: geometrical and biophysical (hemodynamics, heat transfer and an extended cellular necrosis model). We present a comprehensive experimental setup combining multimodal, pre- and postoperative anatomical and functional images, as well as the interventional monitoring of intra-operative signals: the temperature and delivered power. To exploit this dataset, an efficient processing pipeline is introduced, which copes with image noise, variable resolution and anisotropy. The validation study includes twelve ablations from five healthy pig livers: a mean point-to-mesh error between predicted and actual ablation extent of 5.3 ± 3.6 mm is achieved. This enables an end-to-end preclinical validation framework that considers the available dataset.

Goal-oriented Site Characterization in Hydrogeological Applications: An Overview

NASA Astrophysics Data System (ADS)

Nowak, W.; de Barros, F.; Rubin, Y.

2011-12-01

In this study, we address the importance of goal-oriented site characterization. Given the multiple sources of uncertainty in hydrogeological applications, information needs of modeling, prediction and decision support should be satisfied with efficient and rational field campaigns. In this work, we provide an overview of an optimal sampling design framework based on Bayesian decision theory, statistical parameter inference and Bayesian model averaging. It optimizes the field sampling campaign around decisions on environmental performance metrics (e.g., risk, arrival times, etc.) while accounting for parametric and model uncertainty in the geostatistical characterization, in forcing terms, and measurement error. The appealing aspects of the framework lie on its goal-oriented character and that it is directly linked to the confidence in a specified decision. We illustrate how these concepts could be applied in a human health risk problem where uncertainty from both hydrogeological and health parameters are accounted.

Optimally designing games for behavioural research

PubMed Central

Rafferty, Anna N.; Zaharia, Matei; Griffiths, Thomas L.

2014-01-01

Computer games can be motivating and engaging experiences that facilitate learning, leading to their increasing use in education and behavioural experiments. For these applications, it is often important to make inferences about the knowledge and cognitive processes of players based on their behaviour. However, designing games that provide useful behavioural data are a difficult task that typically requires significant trial and error. We address this issue by creating a new formal framework that extends optimal experiment design, used in statistics, to apply to game design. In this framework, we use Markov decision processes to model players' actions within a game, and then make inferences about the parameters of a cognitive model from these actions. Using a variety of concept learning games, we show that in practice, this method can predict which games will result in better estimates of the parameters of interest. The best games require only half as many players to attain the same level of precision. PMID:25002821

Computational Models of Anterior Cingulate Cortex: At the Crossroads between Prediction and Effort.

PubMed

Vassena, Eliana; Holroyd, Clay B; Alexander, William H

2017-01-01

In the last two decades the anterior cingulate cortex (ACC) has become one of the most investigated areas of the brain. Extensive neuroimaging evidence suggests countless functions for this region, ranging from conflict and error coding, to social cognition, pain and effortful control. In response to this burgeoning amount of data, a proliferation of computational models has tried to characterize the neurocognitive architecture of ACC. Early seminal models provided a computational explanation for a relatively circumscribed set of empirical findings, mainly accounting for EEG and fMRI evidence. More recent models have focused on ACC's contribution to effortful control. In parallel to these developments, several proposals attempted to explain within a single computational framework a wider variety of empirical findings that span different cognitive processes and experimental modalities. Here we critically evaluate these modeling attempts, highlighting the continued need to reconcile the array of disparate ACC observations within a coherent, unifying framework.

Quantified Choice of Root-Mean-Square Errors of Approximation for Evaluation and Power Analysis of Small Differences between Structural Equation Models

ERIC Educational Resources Information Center

Li, Libo; Bentler, Peter M.

2011-01-01

MacCallum, Browne, and Cai (2006) proposed a new framework for evaluation and power analysis of small differences between nested structural equation models (SEMs). In their framework, the null and alternative hypotheses for testing a small difference in fit and its related power analyses were defined by some chosen root-mean-square error of…

Error Tendencies in Processing Student Feedback for Instructional Decision Making.

ERIC Educational Resources Information Center

Schermerhorn, John R., Jr.; And Others

1985-01-01

Seeks to assist instructors in recognizing two basic errors that can occur in processing student evaluation data on instructional development efforts; offers a research framework for future investigations of the error tendencies and related issues; and suggests ways in which instructors can confront and manage error tendencies in practice. (MBR)

Forensic Entomology: Evaluating Uncertainty Associated With Postmortem Interval (PMI) Estimates With Ecological Models.

PubMed

Faris, A M; Wang, H-H; Tarone, A M; Grant, W E

2016-05-31

Estimates of insect age can be informative in death investigations and, when certain assumptions are met, can be useful for estimating the postmortem interval (PMI). Currently, the accuracy and precision of PMI estimates is unknown, as error can arise from sources of variation such as measurement error, environmental variation, or genetic variation. Ecological models are an abstract, mathematical representation of an ecological system that can make predictions about the dynamics of the real system. To quantify the variation associated with the pre-appearance interval (PAI), we developed an ecological model that simulates the colonization of vertebrate remains by Cochliomyia macellaria (Fabricius) (Diptera: Calliphoridae), a primary colonizer in the southern United States. The model is based on a development data set derived from a local population and represents the uncertainty in local temperature variability to address PMI estimates at local sites. After a PMI estimate is calculated for each individual, the model calculates the maximum, minimum, and mean PMI, as well as the range and standard deviation for stadia collected. The model framework presented here is one manner by which errors in PMI estimates can be addressed in court when no empirical data are available for the parameter of interest. We show that PAI is a potential important source of error and that an ecological model is one way to evaluate its impact. Such models can be re-parameterized with any development data set, PAI function, temperature regime, assumption of interest, etc., to estimate PMI and quantify uncertainty that arises from specific prediction systems. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported.

PubMed

Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D

2018-05-18

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.

The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies

NASA Astrophysics Data System (ADS)

Hu, B.; Li, J.; Wang, J.; Hall, B.

2014-11-01

The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, individual trees were first extracted and then classified into different species based on their spectral information derived from hyperspectral imagery, spatial information from VHR imagery, and for each ash tree its health state and EAB infestation stage were determined based on hyperspectral imagery. The developed framework and methods were demonstrated to be effective according to the results obtained on two study sites in the city of Toronto, Ontario Canada. The individual tree delineation method provided satisfactory results with an overall accuracy of 78 % and 19 % commission and 23 % omission errors when used on the combined very high-spatial resolution imagery and LiDAR data. In terms of the identification of ash trees, given sufficient representative training data, our classification model was able to predict tree species with above 75 % overall accuracy, and mis-classification occurred mainly between ash and maple trees. The hypothesis that a strong correlation exists between general tree stress and EAB infestation was confirmed. Vegetation indices sensitive to leaf chlorophyll content derived from hyperspectral imagery can be used to predict the EAB infestation levels for each ash tree.

Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR.

PubMed

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.

Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR

PubMed Central

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha−1. Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly. PMID:23967112

Software platform for managing the classification of error- related potentials of observers

NASA Astrophysics Data System (ADS)

Asvestas, P.; Ventouras, E.-C.; Kostopoulos, S.; Sidiropoulos, K.; Korfiatis, V.; Korda, A.; Uzunolglu, A.; Karanasiou, I.; Kalatzis, I.; Matsopoulos, G.

2015-09-01

Human learning is partly based on observation. Electroencephalographic recordings of subjects who perform acts (actors) or observe actors (observers), contain a negative waveform in the Evoked Potentials (EPs) of the actors that commit errors and of observers who observe the error-committing actors. This waveform is called the Error-Related Negativity (ERN). Its detection has applications in the context of Brain-Computer Interfaces. The present work describes a software system developed for managing EPs of observers, with the aim of classifying them into observations of either correct or incorrect actions. It consists of an integrated platform for the storage, management, processing and classification of EPs recorded during error-observation experiments. The system was developed using C# and the following development tools and frameworks: MySQL, .NET Framework, Entity Framework and Emgu CV, for interfacing with the machine learning library of OpenCV. Up to six features can be computed per EP recording per electrode. The user can select among various feature selection algorithms and then proceed to train one of three types of classifiers: Artificial Neural Networks, Support Vector Machines, k-nearest neighbour. Next the classifier can be used for classifying any EP curve that has been inputted to the database.

A Bayesian model for estimating multi-state disease progression.

PubMed

Shen, Shiwen; Han, Simon X; Petousis, Panayiotis; Weiss, Robert E; Meng, Frank; Bui, Alex A T; Hsu, William

2017-02-01

A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson's chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multivariate decoding of brain images using ordinal regression.

PubMed

Doyle, O M; Ashburner, J; Zelaya, F O; Williams, S C R; Mehta, M A; Marquand, A F

2013-11-01

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection. Copyright © 2013. Published by Elsevier Inc.

A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

PubMed Central

Ye, Min; Nagar, Swati; Korzekwa, Ken

2015-01-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057

Towards a whole-cell modeling approach for synthetic biology

NASA Astrophysics Data System (ADS)

Purcell, Oliver; Jain, Bonny; Karr, Jonathan R.; Covert, Markus W.; Lu, Timothy K.

2013-06-01

Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.

An unusual kind of complex synchronizations and its applications in secure communications

NASA Astrophysics Data System (ADS)

Mahmoud, Emad E.

2017-11-01

In this paper, we talk about the meaning of complex anti-syncrhonization (CAS) of hyperchaotic nonlinear frameworks comprehensive complex variables and indeterminate parameters. This sort of synchronization can break down just for complex nonlinear frameworks. The CAS contains or fuses two sorts of synchronizations (complete synchronization and anti-synchronization). In the CAS the attractors of the master and slave frameworks are moving opposite or orthogonal to each other with a similar form; this phenomenon does not exist in the literature. Upon confirmation of the Lyapunov function and a versatile control strategy, a plan is made to play out the CAS of two indistinguishable hyperchaotic attractors of these frameworks. The adequacy of the obtained results is shown by a simulation case. Numerical issues are plotted to decide state variables, synchronization errors, modules errors, and phases errors of those hyperchaotic attractors after synchronization to determine that the CAS is accomplished. The above outcomes will present the possible establishment to the secure communication applications. The CAS of hyperchaotic complex frameworks in which a state variable of the master framework synchronizes with an alternate state variable of the slave framework is an encouraging kind of synchronization as it contributes fantastic security in secure communications. Amid this secure communications, the synchronization between transmitter and collector is shut and message signs are recouped. The encryption and reclamation of the signs are reproduced numerically.

Model parameter-related optimal perturbations and their contributions to El Niño prediction errors

NASA Astrophysics Data System (ADS)

Tao, Ling-Jiang; Gao, Chuan; Zhang, Rong-Hua

2018-04-01

Errors in initial conditions and model parameters (MPs) are the main sources that limit the accuracy of ENSO predictions. In addition to exploring the initial error-induced prediction errors, model errors are equally important in determining prediction performance. In this paper, the MP-related optimal errors that can cause prominent error growth in ENSO predictions are investigated using an intermediate coupled model (ICM) and a conditional nonlinear optimal perturbation (CNOP) approach. Two MPs related to the Bjerknes feedback are considered in the CNOP analysis: one involves the SST-surface wind coupling ({α _τ } ), and the other involves the thermocline effect on the SST ({α _{Te}} ). The MP-related optimal perturbations (denoted as CNOP-P) are found uniformly positive and restrained in a small region: the {α _τ } component is mainly concentrated in the central equatorial Pacific, and the {α _{Te}} component is mainly located in the eastern cold tongue region. This kind of CNOP-P enhances the strength of the Bjerknes feedback and induces an El Niño- or La Niña-like error evolution, resulting in an El Niño-like systematic bias in this model. The CNOP-P is also found to play a role in the spring predictability barrier (SPB) for ENSO predictions. Evidently, such error growth is primarily attributed to MP errors in small areas based on the localized distribution of CNOP-P. Further sensitivity experiments firmly indicate that ENSO simulations are sensitive to the representation of SST-surface wind coupling in the central Pacific and to the thermocline effect in the eastern Pacific in the ICM. These results provide guidance and theoretical support for the future improvement in numerical models to reduce the systematic bias and SPB phenomenon in ENSO predictions.

The prediction of satellite ephemeris errors as they result from surveillance system measurement errors

NASA Astrophysics Data System (ADS)

Simmons, B. E.

1981-08-01

This report derives equations predicting satellite ephemeris error as a function of measurement errors of space-surveillance sensors. These equations lend themselves to rapid computation with modest computer resources. They are applicable over prediction times such that measurement errors, rather than uncertainties of atmospheric drag and of Earth shape, dominate in producing ephemeris error. This report describes the specialization of these equations underlying the ANSER computer program, SEEM (Satellite Ephemeris Error Model). The intent is that this report be of utility to users of SEEM for interpretive purposes, and to computer programmers who may need a mathematical point of departure for limited generalization of SEEM.

Prediction error induced motor contagions in human behaviors.

PubMed

Ikegami, Tsuyoshi; Ganesh, Gowrishankar; Takeuchi, Tatsuya; Nakamoto, Hiroki

2018-05-29

Motor contagions refer to implicit effects on one's actions induced by observed actions. Motor contagions are believed to be induced simply by action observation and cause an observer's action to become similar to the action observed. In contrast, here we report a new motor contagion that is induced only when the observation is accompanied by prediction errors - differences between actions one observes and those he/she predicts or expects. In two experiments, one on whole-body baseball pitching and another on simple arm reaching, we show that the observation of the same action induces distinct motor contagions, depending on whether prediction errors are present or not. In the absence of prediction errors, as in previous reports, participants' actions changed to become similar to the observed action, while in the presence of prediction errors, their actions changed to diverge away from it, suggesting distinct effects of action observation and action prediction on human actions. © 2018, Ikegami et al.

On the Calculation of Uncertainty Statistics with Error Bounds for CFD Calculations Containing Random Parameters and Fields

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2016-01-01

This chapter discusses the ongoing development of combined uncertainty and error bound estimates for computational fluid dynamics (CFD) calculations subject to imposed random parameters and random fields. An objective of this work is the construction of computable error bound formulas for output uncertainty statistics that guide CFD practitioners in systematically determining how accurately CFD realizations should be approximated and how accurately uncertainty statistics should be approximated for output quantities of interest. Formal error bounds formulas for moment statistics that properly account for the presence of numerical errors in CFD calculations and numerical quadrature errors in the calculation of moment statistics have been previously presented in [8]. In this past work, hierarchical node-nested dense and sparse tensor product quadratures are used to calculate moment statistics integrals. In the present work, a framework has been developed that exploits the hierarchical structure of these quadratures in order to simplify the calculation of an estimate of the quadrature error needed in error bound formulas. When signed estimates of realization error are available, this signed error may also be used to estimate output quantity of interest probability densities as a means to assess the impact of realization error on these density estimates. Numerical results are presented for CFD problems with uncertainty to demonstrate the capabilities of this framework.

Computational Analysis of Advanced Shape-Memory Alloy Devices Through a Robust Modeling Framework

NASA Astrophysics Data System (ADS)

Scalet, Giulia; Conti, Michele; Auricchio, Ferdinando

2017-06-01

Shape-memory alloys (SMA) provide significant advantages in various industrial fields, but their manufacturing and commercialization are currently hindered. This is attributed mainly to the poor knowledge of material behavior and the lack of standards in its mechanical characterization. SMA products are usually developed by trial-and-error testing to address specific design requirements, thus increasing costs and time. The development of simulation tools offers a possible solution to assist engineers and designers and allows to better understand SMA transformation phenomena. Accordingly, the purpose of the present paper is to numerically analyze and predict the response of spring-like actuators and septal occluders, which are industrial components exploiting the shape-memory and pseudoelastic properties of SMAs, respectively. The methodology includes two main stages: the implementation of the three-dimensional phenomenological model known as Souza- Auricchio model and the finite element modeling of the device. A discussion about the steps of each stage, as parameter identification and model generalizations, is provided. Validation results are presented through a comparison with the results of a performed experimental campaign. The framework proves good prediction capabilities and allows to reduce the number of experimental tests in the future.

ITrace: An implicit trust inference method for trust-aware collaborative filtering

NASA Astrophysics Data System (ADS)

He, Xu; Liu, Bin; Chen, Kejia

2018-04-01

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A CF algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and thus may lead to misleading recommendations in many practical applications. A natural countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improved CF algorithm termed iTrace is proposed, which takes advantage of both the explicit and the predicted implicit trust to provide recommendations with the CF framework. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error.

Error sensitivity analysis in 10-30-day extended range forecasting by using a nonlinear cross-prediction error model

NASA Astrophysics Data System (ADS)

Xia, Zhiye; Xu, Lisheng; Chen, Hongbin; Wang, Yongqian; Liu, Jinbao; Feng, Wenlan

2017-06-01

Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous meteorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear crossprediction error (NCPE) model, and their stability in the prediction validity period in 10-30-day extended range forecasting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small (10-6-10-2), minor variation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random error has minimal effect on the prediction. When the ratio is in the range of 10-1-2 (i.e., random error dominates), attention should be paid to the random error instead of only the initial error. When the ratio is around 10-2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecasting, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depicted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect ( m > 4) is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperature or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.

Predictability of CFSv2 in the tropical Indo-Pacific region, at daily and subseasonal time scales

NASA Astrophysics Data System (ADS)

Krishnamurthy, V.

2018-06-01

The predictability of a coupled climate model is evaluated at daily and intraseasonal time scales in the tropical Indo-Pacific region during boreal summer and winter. This study has assessed the daily retrospective forecasts of the Climate Forecast System version 2 from the National Centers of Environmental Prediction for the period 1982-2010. The growth of errors in the forecasts of daily precipitation, monsoon intraseasonal oscillation (MISO) and the Madden-Julian oscillation (MJO) is studied. The seasonal cycle of the daily climatology of precipitation is reasonably well predicted except for the underestimation during the peak of summer. The anomalies follow the typical pattern of error growth in nonlinear systems and show no difference between summer and winter. The initial errors in all the cases are found to be in the nonlinear phase of the error growth. The doubling time of small errors is estimated by applying Lorenz error formula. For summer and winter, the doubling time of the forecast errors is in the range of 4-7 and 5-14 days while the doubling time of the predictability errors is 6-8 and 8-14 days, respectively. The doubling time in MISO during the summer and MJO during the winter is in the range of 12-14 days, indicating higher predictability and providing optimism for long-range prediction. There is no significant difference in the growth of forecasts errors originating from different phases of MISO and MJO, although the prediction of the active phase seems to be slightly better.

Effect of grid resolution on large eddy simulation of wall-bounded turbulence

NASA Astrophysics Data System (ADS)

Rezaeiravesh, S.; Liefvendahl, M.

2018-05-01

The effect of grid resolution on a large eddy simulation (LES) of a wall-bounded turbulent flow is investigated. A channel flow simulation campaign involving a systematic variation of the streamwise (Δx) and spanwise (Δz) grid resolution is used for this purpose. The main friction-velocity-based Reynolds number investigated is 300. Near the walls, the grid cell size is determined by the frictional scaling, Δx+ and Δz+, and strongly anisotropic cells, with first Δy+ ˜ 1, thus aiming for the wall-resolving LES. Results are compared to direct numerical simulations, and several quality measures are investigated, including the error in the predicted mean friction velocity and the error in cross-channel profiles of flow statistics. To reduce the total number of channel flow simulations, techniques from the framework of uncertainty quantification are employed. In particular, a generalized polynomial chaos expansion (gPCE) is used to create metamodels for the errors over the allowed parameter ranges. The differing behavior of the different quality measures is demonstrated and analyzed. It is shown that friction velocity and profiles of the velocity and Reynolds stress tensor are most sensitive to Δz+, while the error in the turbulent kinetic energy is mostly influenced by Δx+. Recommendations for grid resolution requirements are given, together with the quantification of the resulting predictive accuracy. The sensitivity of the results to the subgrid-scale (SGS) model and varying Reynolds number is also investigated. All simulations are carried out with second-order accurate finite-volume-based solver OpenFOAM. It is shown that the choice of numerical scheme for the convective term significantly influences the error portraits. It is emphasized that the proposed methodology, involving the gPCE, can be applied to other modeling approaches, i.e., other numerical methods and the choice of SGS model.

Hierarchical Bayesian Model Averaging for Chance Constrained Remediation Designs

NASA Astrophysics Data System (ADS)

Chitsazan, N.; Tsai, F. T.

2012-12-01

Groundwater remediation designs are heavily relying on simulation models which are subjected to various sources of uncertainty in their predictions. To develop a robust remediation design, it is crucial to understand the effect of uncertainty sources. In this research, we introduce a hierarchical Bayesian model averaging (HBMA) framework to segregate and prioritize sources of uncertainty in a multi-layer frame, where each layer targets a source of uncertainty. The HBMA framework provides an insight to uncertainty priorities and propagation. In addition, HBMA allows evaluating model weights in different hierarchy levels and assessing the relative importance of models in each level. To account for uncertainty, we employ a chance constrained (CC) programming for stochastic remediation design. Chance constrained programming was implemented traditionally to account for parameter uncertainty. Recently, many studies suggested that model structure uncertainty is not negligible compared to parameter uncertainty. Using chance constrained programming along with HBMA can provide a rigorous tool for groundwater remediation designs under uncertainty. In this research, the HBMA-CC was applied to a remediation design in a synthetic aquifer. The design was to develop a scavenger well approach to mitigate saltwater intrusion toward production wells. HBMA was employed to assess uncertainties from model structure, parameter estimation and kriging interpolation. An improved harmony search optimization method was used to find the optimal location of the scavenger well. We evaluated prediction variances of chloride concentration at the production wells through the HBMA framework. The results showed that choosing the single best model may lead to a significant error in evaluating prediction variances for two reasons. First, considering the single best model, variances that stem from uncertainty in the model structure will be ignored. Second, considering the best model with non-dominant model weight may underestimate or overestimate prediction variances by ignoring other plausible propositions. Chance constraints allow developing a remediation design with a desirable reliability. However, considering the single best model, the calculated reliability will be different from the desirable reliability. We calculated the reliability of the design for the models at different levels of HBMA. The results showed that by moving toward the top layers of HBMA, the calculated reliability converges to the chosen reliability. We employed the chance constrained optimization along with the HBMA framework to find the optimal location and pumpage for the scavenger well. The results showed that using models at different levels in the HBMA framework, the optimal location of the scavenger well remained the same, but the optimal extraction rate was altered. Thus, we concluded that the optimal pumping rate was sensitive to the prediction variance. Also, the prediction variance was changed by using different extraction rate. Using very high extraction rate will cause prediction variances of chloride concentration at the production wells to approach zero regardless of which HBMA models used.

Ensemble positive unlabeled learning for disease gene identification.

PubMed

Yang, Peng; Li, Xiaoli; Chua, Hon-Nian; Kwoh, Chee-Keong; Ng, See-Kiong

2014-01-01

An increasing number of genes have been experimentally confirmed in recent years as causative genes to various human diseases. The newly available knowledge can be exploited by machine learning methods to discover additional unknown genes that are likely to be associated with diseases. In particular, positive unlabeled learning (PU learning) methods, which require only a positive training set P (confirmed disease genes) and an unlabeled set U (the unknown candidate genes) instead of a negative training set N, have been shown to be effective in uncovering new disease genes in the current scenario. Using only a single source of data for prediction can be susceptible to bias due to incompleteness and noise in the genomic data and a single machine learning predictor prone to bias caused by inherent limitations of individual methods. In this paper, we propose an effective PU learning framework that integrates multiple biological data sources and an ensemble of powerful machine learning classifiers for disease gene identification. Our proposed method integrates data from multiple biological sources for training PU learning classifiers. A novel ensemble-based PU learning method EPU is then used to integrate multiple PU learning classifiers to achieve accurate and robust disease gene predictions. Our evaluation experiments across six disease groups showed that EPU achieved significantly better results compared with various state-of-the-art prediction methods as well as ensemble learning classifiers. Through integrating multiple biological data sources for training and the outputs of an ensemble of PU learning classifiers for prediction, we are able to minimize the potential bias and errors in individual data sources and machine learning algorithms to achieve more accurate and robust disease gene predictions. In the future, our EPU method provides an effective framework to integrate the additional biological and computational resources for better disease gene predictions.

Statistical Modeling of Fire Occurrence Using Data from the Tōhoku, Japan Earthquake and Tsunami.

PubMed

Anderson, Dana; Davidson, Rachel A; Himoto, Keisuke; Scawthorn, Charles

2016-02-01

In this article, we develop statistical models to predict the number and geographic distribution of fires caused by earthquake ground motion and tsunami inundation in Japan. Using new, uniquely large, and consistent data sets from the 2011 Tōhoku earthquake and tsunami, we fitted three types of models-generalized linear models (GLMs), generalized additive models (GAMs), and boosted regression trees (BRTs). This is the first time the latter two have been used in this application. A simple conceptual framework guided identification of candidate covariates. Models were then compared based on their out-of-sample predictive power, goodness of fit to the data, ease of implementation, and relative importance of the framework concepts. For the ground motion data set, we recommend a Poisson GAM; for the tsunami data set, a negative binomial (NB) GLM or NB GAM. The best models generate out-of-sample predictions of the total number of ignitions in the region within one or two. Prefecture-level prediction errors average approximately three. All models demonstrate predictive power far superior to four from the literature that were also tested. A nonlinear relationship is apparent between ignitions and ground motion, so for GLMs, which assume a linear response-covariate relationship, instrumental intensity was the preferred ground motion covariate because it captures part of that nonlinearity. Measures of commercial exposure were preferred over measures of residential exposure for both ground motion and tsunami ignition models. This may vary in other regions, but nevertheless highlights the value of testing alternative measures for each concept. Models with the best predictive power included two or three covariates. © 2015 Society for Risk Analysis.

Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems

NASA Astrophysics Data System (ADS)

Sévellec, Florian; Dijkstra, Henk A.; Drijfhout, Sybren S.; Germe, Agathe

2017-11-01

In this study, the relation between two approaches to assess the ocean predictability on interannual to decadal time scales is investigated. The first pragmatic approach consists of sampling the initial condition uncertainty and assess the predictability through the divergence of this ensemble in time. The second approach is provided by a theoretical framework to determine error growth by estimating optimal linear growing modes. In this paper, it is shown that under the assumption of linearized dynamics and normal distributions of the uncertainty, the exact quantitative spread of ensemble can be determined from the theoretical framework. This spread is at least an order of magnitude less expensive to compute than the approximate solution given by the pragmatic approach. This result is applied to a state-of-the-art Ocean General Circulation Model to assess the predictability in the North Atlantic of four typical oceanic metrics: the strength of the Atlantic Meridional Overturning Circulation (AMOC), the intensity of its heat transport, the two-dimensional spatially-averaged Sea Surface Temperature (SST) over the North Atlantic, and the three-dimensional spatially-averaged temperature in the North Atlantic. For all tested metrics, except for SST, ˜ 75% of the total uncertainty on interannual time scales can be attributed to oceanic initial condition uncertainty rather than atmospheric stochastic forcing. The theoretical method also provide the sensitivity pattern to the initial condition uncertainty, allowing for targeted measurements to improve the skill of the prediction. It is suggested that a relatively small fleet of several autonomous underwater vehicles can reduce the uncertainty in AMOC strength prediction by 70% for 1-5 years lead times.

Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history

NASA Astrophysics Data System (ADS)

Gao, Tian; Zhu, Jiaojun; Deng, Songqiu; Zheng, Xiao; Zhang, Jinxin; Shang, Guiduo; Huang, Liyan

2016-10-01

Timber production is the purpose for managing plantation forests, and its spatial and quantitative information is critical for advising management strategies. Previous studies have focused on growing stock volume (GSV), which represents the current potential of timber production, yet few studies have investigated historical process-harvested timber. This resulted in a gap in a synthetical ecosystem service assessment of timber production. In this paper, we established a Management Process-based Timber production (MPT) framework to integrate the current GSV and the harvested timber derived from historical logging regimes, trying to synthetically assess timber production for a historical period. In the MPT framework, age-class and current GSV determine the times of historical thinning and the corresponding harvested timber, by using a ;space-for-time; substitution. The total timber production can be estimated by the historical harvested timber in each thinning and the current GSV. To test this MPT framework, an empirical study on a larch plantation (LP) with area of 43,946 ha was conducted in North China for a period from 1962 to 2010. Field-based inventory data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) and Landsat-8 OLI (Operational Land Imager) data for estimating the age-class and current GSV of LP. The random forest model with PALSAR backscatter intensity channels and OLI bands as input predictive variables yielded an accuracy of 67.9% with a Kappa coefficient of 0.59 for age-class classification. The regression model using PALSAR data produced a root mean square error (RMSE) of 36.5 m3 ha-1. The total timber production of LP was estimated to be 7.27 × 106 m3, with 4.87 × 106 m3 in current GSV and 2.40 × 106 m3 in harvested timber through historical thinning. The historical process-harvested timber accounts to 33.0% of the total timber production, which component has been neglected in the assessments for current status of plantation forests. Synthetically considering the RMSE for predictive GSV and misclassification of age-class, the error in timber production were supposed to range from -55.2 to 56.3 m3 ha-1. The MPT framework can be used to assess timber production of other tree species at a larger spatial scale, providing crucial information for a better understanding of forest ecosystem service.

New theoretical framework for designing nonionic surfactant mixtures that exhibit a desired adsorption kinetics behavior.

PubMed

Moorkanikkara, Srinivas Nageswaran; Blankschtein, Daniel

2010-12-21

How does one design a surfactant mixture using a set of available surfactants such that it exhibits a desired adsorption kinetics behavior? The traditional approach used to address this design problem involves conducting trial-and-error experiments with specific surfactant mixtures. This approach is typically time-consuming and resource-intensive and becomes increasingly challenging when the number of surfactants that can be mixed increases. In this article, we propose a new theoretical framework to identify a surfactant mixture that most closely meets a desired adsorption kinetics behavior. Specifically, the new theoretical framework involves (a) formulating the surfactant mixture design problem as an optimization problem using an adsorption kinetics model and (b) solving the optimization problem using a commercial optimization package. The proposed framework aims to identify the surfactant mixture that most closely satisfies the desired adsorption kinetics behavior subject to the predictive capabilities of the chosen adsorption kinetics model. Experiments can then be conducted at the identified surfactant mixture condition to validate the predictions. We demonstrate the reliability and effectiveness of the proposed theoretical framework through a realistic case study by identifying a nonionic surfactant mixture consisting of up to four alkyl poly(ethylene oxide) surfactants (C(10)E(4), C(12)E(5), C(12)E(6), and C(10)E(8)) such that it most closely exhibits a desired dynamic surface tension (DST) profile. Specifically, we use the Mulqueen-Stebe-Blankschtein (MSB) adsorption kinetics model (Mulqueen, M.; Stebe, K. J.; Blankschtein, D. Langmuir 2001, 17, 5196-5207) to formulate the optimization problem as well as the SNOPT commercial optimization solver to identify a surfactant mixture consisting of these four surfactants that most closely exhibits the desired DST profile. Finally, we compare the experimental DST profile measured at the surfactant mixture condition identified by the new theoretical framework with the desired DST profile and find good agreement between the two profiles.

Emulation as an Integrating Principle for Cognition

PubMed Central

Colder, Brian

2011-01-01

Emulations, defined as ongoing internal representations of potential actions and the futures those actions are expected to produce, play a critical role in directing human bodily activities. Studies of gross motor behavior, perception, allocation of attention, response to errors, interoception, and homeostatic activities, and higher cognitive reasoning suggest that the proper execution of all these functions relies on emulations. Further evidence supports the notion that reinforcement learning in humans is aimed at updating emulations, and that action selection occurs via the advancement of preferred emulations toward realization of their action and environmental prediction. Emulations are hypothesized to exist as distributed active networks of neurons in cortical and sub-cortical structures. This manuscript ties together previously unrelated theories of the role of prediction in different aspects of human information processing to create an integrated framework for cognition. PMID:21660288

A hybrid framework for quantifying the influence of data in hydrological model calibration

NASA Astrophysics Data System (ADS)

Wright, David P.; Thyer, Mark; Westra, Seth; McInerney, David

2018-06-01

Influence diagnostics aim to identify a small number of influential data points that have a disproportionate impact on the model parameters and/or predictions. The key issues with current influence diagnostic techniques are that the regression-theory approaches do not provide hydrologically relevant influence metrics, while the case-deletion approaches are computationally expensive to calculate. The main objective of this study is to introduce a new two-stage hybrid framework that overcomes these challenges, by delivering hydrologically relevant influence metrics in a computationally efficient manner. Stage one uses computationally efficient regression-theory influence diagnostics to identify the most influential points based on Cook's distance. Stage two then uses case-deletion influence diagnostics to quantify the influence of points using hydrologically relevant metrics. To illustrate the application of the hybrid framework, we conducted three experiments on 11 hydro-climatologically diverse Australian catchments using the GR4J hydrological model. The first experiment investigated how many data points from stage one need to be retained in order to reliably identify those points that have the hightest influence on hydrologically relevant metrics. We found that a choice of 30-50 is suitable for hydrological applications similar to those explored in this study (30 points identified the most influential data 98% of the time and reduced the required recalibrations by 99% for a 10 year calibration period). The second experiment found little evidence of a change in the magnitude of influence with increasing calibration period length from 1, 2, 5 to 10 years. Even for 10 years the impact of influential points can still be high (>30% influence on maximum predicted flows). The third experiment compared the standard least squares (SLS) objective function with the weighted least squares (WLS) objective function on a 10 year calibration period. In two out of three flow metrics there was evidence that SLS, with the assumption of homoscedastic residual error, identified data points with higher influence (largest changes of 40%, 10%, and 44% for the maximum, mean, and low flows, respectively) than WLS, with the assumption of heteroscedastic residual errors (largest changes of 26%, 6%, and 6% for the maximum, mean, and low flows, respectively). The hybrid framework complements existing model diagnostic tools and can be applied to a wide range of hydrological modelling scenarios.

When is an error not a prediction error? An electrophysiological investigation.

PubMed

Holroyd, Clay B; Krigolson, Olave E; Baker, Robert; Lee, Seung; Gibson, Jessica

2009-03-01

A recent theory holds that the anterior cingulate cortex (ACC) uses reinforcement learning signals conveyed by the midbrain dopamine system to facilitate flexible action selection. According to this position, the impact of reward prediction error signals on ACC modulates the amplitude of a component of the event-related brain potential called the error-related negativity (ERN). The theory predicts that ERN amplitude is monotonically related to the expectedness of the event: It is larger for unexpected outcomes than for expected outcomes. However, a recent failure to confirm this prediction has called the theory into question. In the present article, we investigated this discrepancy in three trial-and-error learning experiments. All three experiments provided support for the theory, but the effect sizes were largest when an optimal response strategy could actually be learned. This observation suggests that ACC utilizes dopamine reward prediction error signals for adaptive decision making when the optimal behavior is, in fact, learnable.

Dissociable effects of surprising rewards on learning and memory.

PubMed

Rouhani, Nina; Norman, Kenneth A; Niv, Yael

2018-03-19

Reward-prediction errors track the extent to which rewards deviate from expectations, and aid in learning. How do such errors in prediction interact with memory for the rewarding episode? Existing findings point to both cooperative and competitive interactions between learning and memory mechanisms. Here, we investigated whether learning about rewards in a high-risk context, with frequent, large prediction errors, would give rise to higher fidelity memory traces for rewarding events than learning in a low-risk context. Experiment 1 showed that recognition was better for items associated with larger absolute prediction errors during reward learning. Larger prediction errors also led to higher rates of learning about rewards. Interestingly we did not find a relationship between learning rate for reward and recognition-memory accuracy for items, suggesting that these two effects of prediction errors were caused by separate underlying mechanisms. In Experiment 2, we replicated these results with a longer task that posed stronger memory demands and allowed for more learning. We also showed improved source and sequence memory for items within the high-risk context. In Experiment 3, we controlled for the difficulty of reward learning in the risk environments, again replicating the previous results. Moreover, this control revealed that the high-risk context enhanced item-recognition memory beyond the effect of prediction errors. In summary, our results show that prediction errors boost both episodic item memory and incremental reward learning, but the two effects are likely mediated by distinct underlying systems. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Demonstration of risk based, goal driven framework for hydrological field campaigns and inverse modeling with case studies

NASA Astrophysics Data System (ADS)

Harken, B.; Geiges, A.; Rubin, Y.

2013-12-01

There are several stages in any hydrological modeling campaign, including: formulation and analysis of a priori information, data acquisition through field campaigns, inverse modeling, and forward modeling and prediction of some environmental performance metric (EPM). The EPM being predicted could be, for example, contaminant concentration, plume travel time, or aquifer recharge rate. These predictions often have significant bearing on some decision that must be made. Examples include: how to allocate limited remediation resources between multiple contaminated groundwater sites, where to place a waste repository site, and what extraction rates can be considered sustainable in an aquifer. Providing an answer to these questions depends on predictions of EPMs using forward models as well as levels of uncertainty related to these predictions. Uncertainty in model parameters, such as hydraulic conductivity, leads to uncertainty in EPM predictions. Often, field campaigns and inverse modeling efforts are planned and undertaken with reduction of parametric uncertainty as the objective. The tool of hypothesis testing allows this to be taken one step further by considering uncertainty reduction in the ultimate prediction of the EPM as the objective and gives a rational basis for weighing costs and benefits at each stage. When using the tool of statistical hypothesis testing, the EPM is cast into a binary outcome. This is formulated as null and alternative hypotheses, which can be accepted and rejected with statistical formality. When accounting for all sources of uncertainty at each stage, the level of significance of this test provides a rational basis for planning, optimization, and evaluation of the entire campaign. Case-specific information, such as consequences prediction error and site-specific costs can be used in establishing selection criteria based on what level of risk is deemed acceptable. This framework is demonstrated and discussed using various synthetic case studies. The case studies involve contaminated aquifers where a decision must be made based on prediction of when a contaminant will arrive at a given location. The EPM, in this case contaminant travel time, is cast into the hypothesis testing framework. The null hypothesis states that the contaminant plume will arrive at the specified location before a critical value of time passes, and the alternative hypothesis states that the plume will arrive after the critical time passes. Different field campaigns are analyzed based on effectiveness in reducing the probability of selecting the wrong hypothesis, which in this case corresponds to reducing uncertainty in the prediction of plume arrival time. To examine the role of inverse modeling in this framework, case studies involving both Maximum Likelihood parameter estimation and Bayesian inversion are used.

National-scale aboveground biomass geostatistical mapping with FIA inventory and GLAS data: Preparation for sparsely sampled lidar assisted forest inventory

NASA Astrophysics Data System (ADS)

Babcock, C. R.; Finley, A. O.; Andersen, H. E.; Moskal, L. M.; Morton, D. C.; Cook, B.; Nelson, R.

2017-12-01

Upcoming satellite lidar missions, such as GEDI and IceSat-2, are designed to collect laser altimetry data from space for narrow bands along orbital tracts. As a result lidar metric sets derived from these sources will not be of complete spatial coverage. This lack of complete coverage, or sparsity, means traditional regression approaches that consider lidar metrics as explanatory variables (without error) cannot be used to generate wall-to-wall maps of forest inventory variables. We implement a coregionalization framework to jointly model sparsely sampled lidar information and point-referenced forest variable measurements to create wall-to-wall maps with full probabilistic uncertainty quantification of all inputs. We inform the model with USFS Forest Inventory and Analysis (FIA) in-situ forest measurements and GLAS lidar data to spatially predict aboveground forest biomass (AGB) across the contiguous US. We cast our model within a Bayesian hierarchical framework to better model complex space-varying correlation structures among the lidar metrics and FIA data, which yields improved prediction and uncertainty assessment. To circumvent computational difficulties that arise when fitting complex geostatistical models to massive datasets, we use a Nearest Neighbor Gaussian process (NNGP) prior. Results indicate that a coregionalization modeling approach to leveraging sampled lidar data to improve AGB estimation is effective. Further, fitting the coregionalization model within a Bayesian mode of inference allows for AGB quantification across scales ranging from individual pixel estimates of AGB density to total AGB for the continental US with uncertainty. The coregionalization framework examined here is directly applicable to future spaceborne lidar acquisitions from GEDI and IceSat-2. Pairing these lidar sources with the extensive FIA forest monitoring plot network using a joint prediction framework, such as the coregionalization model explored here, offers the potential to improve forest AGB accounting certainty and provide maps for post-model fitting analysis of the spatial distribution of AGB.

Modelling Trial-by-Trial Changes in the Mismatch Negativity

PubMed Central

Lieder, Falk; Daunizeau, Jean; Garrido, Marta I.; Friston, Karl J.; Stephan, Klaas E.

2013-01-01

The mismatch negativity (MMN) is a differential brain response to violations of learned regularities. It has been used to demonstrate that the brain learns the statistical structure of its environment and predicts future sensory inputs. However, the algorithmic nature of these computations and the underlying neurobiological implementation remain controversial. This article introduces a mathematical framework with which competing ideas about the computational quantities indexed by MMN responses can be formalized and tested against single-trial EEG data. This framework was applied to five major theories of the MMN, comparing their ability to explain trial-by-trial changes in MMN amplitude. Three of these theories (predictive coding, model adjustment, and novelty detection) were formalized by linking the MMN to different manifestations of the same computational mechanism: approximate Bayesian inference according to the free-energy principle. We thereby propose a unifying view on three distinct theories of the MMN. The relative plausibility of each theory was assessed against empirical single-trial MMN amplitudes acquired from eight healthy volunteers in a roving oddball experiment. Models based on the free-energy principle provided more plausible explanations of trial-by-trial changes in MMN amplitude than models representing the two more traditional theories (change detection and adaptation). Our results suggest that the MMN reflects approximate Bayesian learning of sensory regularities, and that the MMN-generating process adjusts a probabilistic model of the environment according to prediction errors. PMID:23436989

A Hierarchical Generative Framework of Language Processing: Linking Language Perception, Interpretation, and Production Abnormalities in Schizophrenia

PubMed Central

Brown, Meredith; Kuperberg, Gina R.

2015-01-01

Language and thought dysfunction are central to the schizophrenia syndrome. They are evident in the major symptoms of psychosis itself, particularly as disorganized language output (positive thought disorder) and auditory verbal hallucinations (AVHs), and they also manifest as abnormalities in both high-level semantic and contextual processing and low-level perception. However, the literatures characterizing these abnormalities have largely been separate and have sometimes provided mutually exclusive accounts of aberrant language in schizophrenia. In this review, we propose that recent generative probabilistic frameworks of language processing can provide crucial insights that link these four lines of research. We first outline neural and cognitive evidence that real-time language comprehension and production normally involve internal generative circuits that propagate probabilistic predictions to perceptual cortices — predictions that are incrementally updated based on prediction error signals as new inputs are encountered. We then explain how disruptions to these circuits may compromise communicative abilities in schizophrenia by reducing the efficiency and robustness of both high-level language processing and low-level speech perception. We also argue that such disruptions may contribute to the phenomenology of thought-disordered speech and false perceptual inferences in the language system (i.e., AVHs). This perspective suggests a number of productive avenues for future research that may elucidate not only the mechanisms of language abnormalities in schizophrenia, but also promising directions for cognitive rehabilitation. PMID:26640435

Predicting knee replacement damage in a simulator machine using a computational model with a consistent wear factor.

PubMed

Zhao, Dong; Sakoda, Hideyuki; Sawyer, W Gregory; Banks, Scott A; Fregly, Benjamin J

2008-02-01

Wear of ultrahigh molecular weight polyethylene remains a primary factor limiting the longevity of total knee replacements (TKRs). However, wear testing on a simulator machine is time consuming and expensive, making it impractical for iterative design purposes. The objectives of this paper were first, to evaluate whether a computational model using a wear factor consistent with the TKR material pair can predict accurate TKR damage measured in a simulator machine, and second, to investigate how choice of surface evolution method (fixed or variable step) and material model (linear or nonlinear) affect the prediction. An iterative computational damage model was constructed for a commercial knee implant in an AMTI simulator machine. The damage model combined a dynamic contact model with a surface evolution model to predict how wear plus creep progressively alter tibial insert geometry over multiple simulations. The computational framework was validated by predicting wear in a cylinder-on-plate system for which an analytical solution was derived. The implant damage model was evaluated for 5 million cycles of simulated gait using damage measurements made on the same implant in an AMTI machine. Using a pin-on-plate wear factor for the same material pair as the implant, the model predicted tibial insert wear volume to within 2% error and damage depths and areas to within 18% and 10% error, respectively. Choice of material model had little influence, while inclusion of surface evolution affected damage depth and area but not wear volume predictions. Surface evolution method was important only during the initial cycles, where variable step was needed to capture rapid geometry changes due to the creep. Overall, our results indicate that accurate TKR damage predictions can be made with a computational model using a constant wear factor obtained from pin-on-plate tests for the same material pair, and furthermore, that surface evolution method matters only during the initial "break in" period of the simulation.

A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

PubMed

Ye, Min; Nagar, Swati; Korzekwa, Ken

2016-04-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Correlations between Preoperative Angle Parameters and Postoperative Unpredicted Refractive Errors after Cataract Surgery in Open Angle Glaucoma (AOD 500).

PubMed

Lee, Wonseok; Bae, Hyoung Won; Lee, Si Hyung; Kim, Chan Yun; Seong, Gong Je

2017-03-01

To assess the accuracy of intraocular lens (IOL) power prediction for cataract surgery with open angle glaucoma (OAG) and to identify preoperative angle parameters correlated with postoperative unpredicted refractive errors. This study comprised 45 eyes from 45 OAG subjects and 63 eyes from 63 non-glaucomatous cataract subjects (controls). We investigated differences in preoperative predicted refractive errors and postoperative refractive errors for each group. Preoperative predicted refractive errors were obtained by biometry (IOL-master) and compared to postoperative refractive errors measured by auto-refractometer 2 months postoperatively. Anterior angle parameters were determined using swept source optical coherence tomography. We investigated correlations between preoperative angle parameters [angle open distance (AOD); trabecular iris surface area (TISA); angle recess area (ARA); trabecular iris angle (TIA)] and postoperative unpredicted refractive errors. In patients with OAG, significant differences were noted between preoperative predicted and postoperative real refractive errors, with more myopia than predicted. No significant differences were recorded in controls. Angle parameters (AOD, ARA, TISA, and TIA) at the superior and inferior quadrant were significantly correlated with differences between predicted and postoperative refractive errors in OAG patients (-0.321 to -0.408, p<0.05). Superior quadrant AOD 500 was significantly correlated with postoperative refractive differences in multivariate linear regression analysis (β=-2.925, R²=0.404). Clinically unpredicted refractive errors after cataract surgery were more common in OAG than in controls. Certain preoperative angle parameters, especially AOD 500 at the superior quadrant, were significantly correlated with these unpredicted errors.

Correlations between Preoperative Angle Parameters and Postoperative Unpredicted Refractive Errors after Cataract Surgery in Open Angle Glaucoma (AOD 500)

PubMed Central

Lee, Wonseok; Bae, Hyoung Won; Lee, Si Hyung; Kim, Chan Yun

2017-01-01

Purpose To assess the accuracy of intraocular lens (IOL) power prediction for cataract surgery with open angle glaucoma (OAG) and to identify preoperative angle parameters correlated with postoperative unpredicted refractive errors. Materials and Methods This study comprised 45 eyes from 45 OAG subjects and 63 eyes from 63 non-glaucomatous cataract subjects (controls). We investigated differences in preoperative predicted refractive errors and postoperative refractive errors for each group. Preoperative predicted refractive errors were obtained by biometry (IOL-master) and compared to postoperative refractive errors measured by auto-refractometer 2 months postoperatively. Anterior angle parameters were determined using swept source optical coherence tomography. We investigated correlations between preoperative angle parameters [angle open distance (AOD); trabecular iris surface area (TISA); angle recess area (ARA); trabecular iris angle (TIA)] and postoperative unpredicted refractive errors. Results In patients with OAG, significant differences were noted between preoperative predicted and postoperative real refractive errors, with more myopia than predicted. No significant differences were recorded in controls. Angle parameters (AOD, ARA, TISA, and TIA) at the superior and inferior quadrant were significantly correlated with differences between predicted and postoperative refractive errors in OAG patients (-0.321 to -0.408, p<0.05). Superior quadrant AOD 500 was significantly correlated with postoperative refractive differences in multivariate linear regression analysis (β=-2.925, R2=0.404). Conclusion Clinically unpredicted refractive errors after cataract surgery were more common in OAG than in controls. Certain preoperative angle parameters, especially AOD 500 at the superior quadrant, were significantly correlated with these unpredicted errors. PMID:28120576

Long-term orbit prediction for China's Tiangong-1 spacecraft based on mean atmosphere model

NASA Astrophysics Data System (ADS)

Tang, Jingshi; Liu, Lin; Miao, Manqian

Tiangong-1 is China's test module for future space station. It has gone through three successful rendezvous and dockings with Shenzhou spacecrafts from 2011 to 2013. For the long-term management and maintenance, the orbit sometimes needs to be predicted for a long period of time. As Tiangong-1 works in a low-Earth orbit with an altitude of about 300-400 km, the error in the a priori atmosphere model contributes significantly to the rapid increase of the predicted orbit error. When the orbit is predicted for 10-20 days, the error in the a priori atmosphere model, if not properly corrected, could induce the semi-major axis error and the overall position error up to a few kilometers and several thousand kilometers respectively. In this work, we use a mean atmosphere model averaged from NRLMSIS00. The a priori reference mean density can be corrected during precise orbit determination (POD). For applications in the long-term orbit prediction, the observations are first accumulated. With sufficiently long period of observations, we are able to obtain a series of the diurnal mean densities. This series bears the recent variation of the atmosphere density and can be analyzed for various periods. After being properly fitted, the mean density can be predicted and then applied in the orbit prediction. We show that the densities predicted with this approach can serve to increase the accuracy of the predicted orbit. In several 20-day prediction tests, most predicted orbits show semi-major axis errors better than 700m and overall position errors better than 600km.

Prediction of discretization error using the error transport equation

NASA Astrophysics Data System (ADS)

Celik, Ismail B.; Parsons, Don Roscoe

2017-06-01

This study focuses on an approach to quantify the discretization error associated with numerical solutions of partial differential equations by solving an error transport equation (ETE). The goal is to develop a method that can be used to adequately predict the discretization error using the numerical solution on only one grid/mesh. The primary problem associated with solving the ETE is the formulation of the error source term which is required for accurately predicting the transport of the error. In this study, a novel approach is considered which involves fitting the numerical solution with a series of locally smooth curves and then blending them together with a weighted spline approach. The result is a continuously differentiable analytic expression that can be used to determine the error source term. Once the source term has been developed, the ETE can easily be solved using the same solver that is used to obtain the original numerical solution. The new methodology is applied to the two-dimensional Navier-Stokes equations in the laminar flow regime. A simple unsteady flow case is also considered. The discretization error predictions based on the methodology presented in this study are in good agreement with the 'true error'. While in most cases the error predictions are not quite as accurate as those from Richardson extrapolation, the results are reasonable and only require one numerical grid. The current results indicate that there is much promise going forward with the newly developed error source term evaluation technique and the ETE.

Model-free and model-based reward prediction errors in EEG.

PubMed

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

EPA'S NEW EMISSIONS MODELING FRAMEWORK

EPA Science Inventory

EPA's Office of Air Quality Planning and Standards is building a new Emissions Modeling Framework that will solve many of the long-standing difficulties of emissions modeling. The goals of the Framework are to (1) prevent bottlenecks and errors caused by emissions modeling activi...

Using Covert Response Activation to Test Latent Assumptions of Formal Decision-Making Models in Humans.

PubMed

Servant, Mathieu; White, Corey; Montagnini, Anna; Burle, Borís

2015-07-15

Most decisions that we make build upon multiple streams of sensory evidence and control mechanisms are needed to filter out irrelevant information. Sequential sampling models of perceptual decision making have recently been enriched by attentional mechanisms that weight sensory evidence in a dynamic and goal-directed way. However, the framework retains the longstanding hypothesis that motor activity is engaged only once a decision threshold is reached. To probe latent assumptions of these models, neurophysiological indices are needed. Therefore, we collected behavioral and EMG data in the flanker task, a standard paradigm to investigate decisions about relevance. Although the models captured response time distributions and accuracy data, EMG analyses of response agonist muscles challenged the assumption of independence between decision and motor processes. Those analyses revealed covert incorrect EMG activity ("partial error") in a fraction of trials in which the correct response was finally given, providing intermediate states of evidence accumulation and response activation at the single-trial level. We extended the models by allowing motor activity to occur before a commitment to a choice and demonstrated that the proposed framework captured the rate, latency, and EMG surface of partial errors, along with the speed of the correction process. In return, EMG data provided strong constraints to discriminate between competing models that made similar behavioral predictions. Our study opens new theoretical and methodological avenues for understanding the links among decision making, cognitive control, and motor execution in humans. Sequential sampling models of perceptual decision making assume that sensory information is accumulated until a criterion quantity of evidence is obtained, from where the decision terminates in a choice and motor activity is engaged. The very existence of covert incorrect EMG activity ("partial error") during the evidence accumulation process challenges this longstanding assumption. In the present work, we use partial errors to better constrain sequential sampling models at the single-trial level. Copyright © 2015 the authors 0270-6474/15/3510371-15$15.00/0.

Tracking Progress in Improving Diagnosis: A Framework for Defining Undesirable Diagnostic Events.

PubMed

Olson, Andrew P J; Graber, Mark L; Singh, Hardeep

2018-01-29

Diagnostic error is a prevalent, harmful, and costly phenomenon. Multiple national health care and governmental organizations have recently identified the need to improve diagnostic safety as a high priority. A major barrier, however, is the lack of standardized, reliable methods for measuring diagnostic safety. Given the absence of reliable and valid measures for diagnostic errors, we need methods to help establish some type of baseline diagnostic performance across health systems, as well as to enable researchers and health systems to determine the impact of interventions for improving the diagnostic process. Multiple approaches have been suggested but none widely adopted. We propose a new framework for identifying "undesirable diagnostic events" (UDEs) that health systems, professional organizations, and researchers could further define and develop to enable standardized measurement and reporting related to diagnostic safety. We propose an outline for UDEs that identifies both conditions prone to diagnostic error and the contexts of care in which these errors are likely to occur. Refinement and adoption of this framework across health systems can facilitate standardized measurement and reporting of diagnostic safety.

BEATBOX v1.0: Background Error Analysis Testbed with Box Models

NASA Astrophysics Data System (ADS)

Knote, Christoph; Barré, Jérôme; Eckl, Max

2018-02-01

The Background Error Analysis Testbed (BEATBOX) is a new data assimilation framework for box models. Based on the BOX Model eXtension (BOXMOX) to the Kinetic Pre-Processor (KPP), this framework allows users to conduct performance evaluations of data assimilation experiments, sensitivity analyses, and detailed chemical scheme diagnostics from an observation simulation system experiment (OSSE) point of view. The BEATBOX framework incorporates an observation simulator and a data assimilation system with the possibility of choosing ensemble, adjoint, or combined sensitivities. A user-friendly, Python-based interface allows for the tuning of many parameters for atmospheric chemistry and data assimilation research as well as for educational purposes, for example observation error, model covariances, ensemble size, perturbation distribution in the initial conditions, and so on. In this work, the testbed is described and two case studies are presented to illustrate the design of a typical OSSE experiment, data assimilation experiments, a sensitivity analysis, and a method for diagnosing model errors. BEATBOX is released as an open source tool for the atmospheric chemistry and data assimilation communities.

The Role of Multimodel Combination in Improving Streamflow Prediction

NASA Astrophysics Data System (ADS)

Arumugam, S.; Li, W.

2008-12-01

Model errors are the inevitable part in any prediction exercise. One approach that is currently gaining attention to reduce model errors is by optimally combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictability. In this study, we present a new approach to combine multiple hydrological models by evaluating their predictability contingent on the predictor state. We combine two hydrological models, 'abcd' model and Variable Infiltration Capacity (VIC) model, with each model's parameter being estimated by two different objective functions to develop multimodel streamflow predictions. The performance of multimodel predictions is compared with individual model predictions using correlation, root mean square error and Nash-Sutcliffe coefficient. To quantify precisely under what conditions the multimodel predictions result in improved predictions, we evaluate the proposed algorithm by testing it against streamflow generated from a known model ('abcd' model or VIC model) with errors being homoscedastic or heteroscedastic. Results from the study show that streamflow simulated from individual models performed better than multimodels under almost no model error. Under increased model error, the multimodel consistently performed better than the single model prediction in terms of all performance measures. The study also evaluates the proposed algorithm for streamflow predictions in two humid river basins from NC as well as in two arid basins from Arizona. Through detailed validation in these four sites, the study shows that multimodel approach better predicts the observed streamflow in comparison to the single model predictions.

Mapping cumulative noise from shipping to inform marine spatial planning.

PubMed

Erbe, Christine; MacGillivray, Alexander; Williams, Rob

2012-11-01

Including ocean noise in marine spatial planning requires predictions of noise levels on large spatiotemporal scales. Based on a simple sound transmission model and ship track data (Automatic Identification System, AIS), cumulative underwater acoustic energy from shipping was mapped throughout 2008 in the west Canadian Exclusive Economic Zone, showing high noise levels in critical habitats for endangered resident killer whales, exceeding limits of "good conservation status" under the EU Marine Strategy Framework Directive. Error analysis proved that rough calculations of noise occurrence and propagation can form a basis for management processes, because spending resources on unnecessary detail is wasteful and delays remedial action.

Application of Exactly Linearized Error Transport Equations to AIAA CFD Prediction Workshops

NASA Technical Reports Server (NTRS)

Derlaga, Joseph M.; Park, Michael A.; Rallabhandi, Sriram

2017-01-01

The computational fluid dynamics (CFD) prediction workshops sponsored by the AIAA have created invaluable opportunities in which to discuss the predictive capabilities of CFD in areas in which it has struggled, e.g., cruise drag, high-lift, and sonic boom pre diction. While there are many factors that contribute to disagreement between simulated and experimental results, such as modeling or discretization error, quantifying the errors contained in a simulation is important for those who make decisions based on the computational results. The linearized error transport equations (ETE) combined with a truncation error estimate is a method to quantify one source of errors. The ETE are implemented with a complex-step method to provide an exact linearization with minimal source code modifications to CFD and multidisciplinary analysis methods. The equivalency of adjoint and linearized ETE functional error correction is demonstrated. Uniformly refined grids from a series of AIAA prediction workshops demonstrate the utility of ETE for multidisciplinary analysis with a connection between estimated discretization error and (resolved or under-resolved) flow features.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

PubMed

Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

2017-06-30

Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

PubMed Central

2017-01-01

Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113

Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

NASA Astrophysics Data System (ADS)

Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

2008-11-01

The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

Quality optimization of H.264/AVC video transmission over noisy environments using a sparse regression framework

NASA Astrophysics Data System (ADS)

Pandremmenou, K.; Tziortziotis, N.; Paluri, S.; Zhang, W.; Blekas, K.; Kondi, L. P.; Kumar, S.

2015-03-01

We propose the use of the Least Absolute Shrinkage and Selection Operator (LASSO) regression method in order to predict the Cumulative Mean Squared Error (CMSE), incurred by the loss of individual slices in video transmission. We extract a number of quality-relevant features from the H.264/AVC video sequences, which are given as input to the LASSO. This method has the benefit of not only keeping a subset of the features that have the strongest effects towards video quality, but also produces accurate CMSE predictions. Particularly, we study the LASSO regression through two different architectures; the Global LASSO (G.LASSO) and Local LASSO (L.LASSO). In G.LASSO, a single regression model is trained for all slice types together, while in L.LASSO, motivated by the fact that the values for some features are closely dependent on the considered slice type, each slice type has its own regression model, in an e ort to improve LASSO's prediction capability. Based on the predicted CMSE values, we group the video slices into four priority classes. Additionally, we consider a video transmission scenario over a noisy channel, where Unequal Error Protection (UEP) is applied to all prioritized slices. The provided results demonstrate the efficiency of LASSO in estimating CMSE with high accuracy, using only a few features. les that typically contain high-entropy data, producing a footprint that is far less conspicuous than existing methods. The system uses a local web server to provide a le system, user interface and applications through an web architecture.

For how long can we predict the weather? - Insights into atmospheric predictability from global convection-allowing simulations

NASA Astrophysics Data System (ADS)

Judt, Falko

2017-04-01

A tremendous increase in computing power has facilitated the advent of global convection-resolving numerical weather prediction (NWP) models. Although this technological breakthrough allows for the seamless prediction of weather from local to global scales, the predictability of multiscale weather phenomena in these models is not very well known. To address this issue, we conducted a global high-resolution (4-km) predictability experiment using the Model for Prediction Across Scales (MPAS), a state-of-the-art global NWP model developed at the National Center for Atmospheric Research. The goals of this experiment are to investigate error growth from convective to planetary scales and to quantify the intrinsic, scale-dependent predictability limits of atmospheric motions. The globally uniform resolution of 4 km allows for the explicit treatment of organized deep moist convection, alleviating grave limitations of previous predictability studies that either used high-resolution limited-area models or global simulations with coarser grids and cumulus parameterization. Error growth is analyzed within the context of an "identical twin" experiment setup: the error is defined as the difference between a 20-day long "nature run" and a simulation that was perturbed with small-amplitude noise, but is otherwise identical. It is found that in convectively active regions, errors grow by several orders of magnitude within the first 24 h ("super-exponential growth"). The errors then spread to larger scales and begin a phase of exponential growth after 2-3 days when contaminating the baroclinic zones. After 16 days, the globally averaged error saturates—suggesting that the intrinsic limit of atmospheric predictability (in a general sense) is about two weeks, which is in line with earlier estimates. However, error growth rates differ between the tropics and mid-latitudes as well as between the troposphere and stratosphere, highlighting that atmospheric predictability is a complex problem. The comparatively slower error growth in the tropics and in the stratosphere indicates that certain weather phenomena could potentially have longer predictability than currently thought.

Improving Flood Prediction By the Assimilation of Satellite Soil Moisture in Poorly Monitored Catchments.

NASA Astrophysics Data System (ADS)

Alvarez-Garreton, C. D.; Ryu, D.; Western, A. W.; Crow, W. T.; Su, C. H.; Robertson, D. E.

2014-12-01

Flood prediction in poorly monitored catchments is among the greatest challenges faced by hydrologists. To address this challenge, an increasing number of studies in the last decade have explored methods to integrate various existing observations from ground and satellites. One approach in particular, is the assimilation of satellite soil moisture (SM-DA) into rainfall-runoff models. The rationale is that satellite soil moisture (SSM) can be used to correct model soil water states, enabling more accurate prediction of catchment response to precipitation and thus better streamflow. However, there is still no consensus on the most effective SM-DA scheme and how this might depend on catchment scale, climate characteristics, runoff mechanisms, model and SSM products used, etc. In this work, an operational SM-DA scheme was set up in the poorly monitored, large (>40,000 km2), semi-arid Warrego catchment situated in eastern Australia. We assimilated passive and active SSM products into the probability distributed model (PDM) using an ensemble Kalman filter. We explored factors influencing the SM-DA framework, including relatively new techniques to remove model-observation bias, estimate observation errors and represent model errors. Furthermore, we explored the advantages of accounting for the spatial distribution of forcing and channel routing processes within the catchment by implementing and comparing lumped and semi-distributed model setups. Flood prediction is improved by SM-DA (Figure), with a 30% reduction of the average root-mean-squared difference of the ensemble prediction, a 20% reduction of the false alarm ratio and a 40% increase of the ensemble mean Nash-Sutcliffe efficiency. SM-DA skill does not significantly change with different observation error assumptions, but the skill strongly depends on the observational bias correction technique used, and more importantly, on the performance of the open-loop model before assimilation. Our findings imply that proper pre-processing of SSM is important for the efficacy of the SM-DA and assimilation performance is critically affected by the quality of model calibration. We therefore recommend focusing efforts on these two factors, while further evaluating the trade-offs between model complexity and data availability.

Low Boom Configuration Analysis with FUN3D Adjoint Simulation Framework

NASA Technical Reports Server (NTRS)

Park, Michael A.

2011-01-01

Off-body pressure, forces, and moments for the Gulfstream Low Boom Model are computed with a Reynolds Averaged Navier Stokes solver coupled with the Spalart-Allmaras (SA) turbulence model. This is the first application of viscous output-based adaptation to reduce estimated discretization errors in off-body pressure for a wing body configuration. The output adaptation approach is compared to an a priori grid adaptation technique designed to resolve the signature on the centerline by stretching and aligning the grid to the freestream Mach angle. The output-based approach produced good predictions of centerline and off-centerline measurements. Eddy viscosity predicted by the SA turbulence model increased significantly with grid adaptation. Computed lift as a function of drag compares well with wind tunnel measurements for positive lift, but predicted lift, drag, and pitching moment as a function of angle of attack has significant differences from the measured data. The sensitivity of longitudinal forces and moment to grid refinement is much smaller than the differences between the computed and measured data.

The role of effort in influencing the effect of anxiety on performance: testing the conflicting predictions of processing efficiency theory and the conscious processing hypothesis.

PubMed

Wilson, Mark; Smith, Nickolas C; Holmes, Paul S

2007-08-01

The aim of this study was to test the conflicting predictions of processing efficiency theory (PET) and the conscious processing hypothesis (CPH) regarding effort's role in influencing the effects of anxiety on a golf putting task. Mid-handicap golfers made a series of putts to target holes under two counterbalanced conditions designed to manipulate the level of anxiety experienced. The effort exerted on each putting task was assessed though self-report, psychophysiological (heart rate variability) and behavioural (pre-putt time and glances at the target) measures. Performance was assessed by putting error. Results were generally more supportive of the predictions of PET rather than the CPH as performance was maintained for some performers despite increased state anxiety and a reduction in processing efficiency. The findings of this study support previous research suggesting that both theories offer useful theoretical frameworks for examining the relationship between anxiety and performance in sport.

Data Assimilation - Advances and Applications

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

Williams, Brian J.

2014-07-30

This presentation provides an overview of data assimilation (model calibration) for complex computer experiments. Calibration refers to the process of probabilistically constraining uncertain physics/engineering model inputs to be consistent with observed experimental data. An initial probability distribution for these parameters is updated using the experimental information. Utilization of surrogate models and empirical adjustment for model form error in code calibration form the basis for the statistical methodology considered. The role of probabilistic code calibration in supporting code validation is discussed. Incorporation of model form uncertainty in rigorous uncertainty quantification (UQ) analyses is also addressed. Design criteria used within a batchmore » sequential design algorithm are introduced for efficiently achieving predictive maturity and improved code calibration. Predictive maturity refers to obtaining stable predictive inference with calibrated computer codes. These approaches allow for augmentation of initial experiment designs for collecting new physical data. A standard framework for data assimilation is presented and techniques for updating the posterior distribution of the state variables based on particle filtering and the ensemble Kalman filter are introduced.« less

Quantification and characterization of leakage errors

NASA Astrophysics Data System (ADS)

Wood, Christopher J.; Gambetta, Jay M.

2018-03-01

We present a general framework for the quantification and characterization of leakage errors that result when a quantum system is encoded in the subspace of a larger system. To do this we introduce metrics for quantifying the coherent and incoherent properties of the resulting errors and we illustrate this framework with several examples relevant to superconducting qubits. In particular, we propose two quantities, the leakage and seepage rates, which together with average gate fidelity allow for characterizing the average performance of quantum gates in the presence of leakage and show how the randomized benchmarking protocol can be modified to enable the robust estimation of all three quantities for a Clifford gate set.

Explaining pragmatic performance in traumatic brain injury: a process perspective on communicative errors.

PubMed

Bosco, Francesca M; Angeleri, Romina; Sacco, Katiuscia; Bara, Bruno G

2015-01-01

The purpose of this study is to investigate the pragmatic abilities of individuals with traumatic brain injury (TBI). Several studies in the literature have previously reported communicative deficits in individuals with TBI, however such research has focused principally on communicative deficits in general, without providing an analysis of the errors committed in understanding and expressing communicative acts. Within the theoretical framework of Cognitive Pragmatics theory and Cooperative principle we focused on intermediate communicative errors that occur in both the comprehension and the production of various pragmatic phenomena, expressed through both linguistic and extralinguistic communicative modalities. To investigate the pragmatic abilities of individuals with TBI. A group of 30 individuals with TBI and a matched control group took part in the experiment. They were presented with a series of videotaped vignettes depicting everyday communicative exchanges, and were tested on the comprehension and production of various kinds of communicative acts (standard communicative act, deceit and irony). The participants' answers were evaluated as correct or incorrect. Incorrect answers were then further evaluated with regard to the presence of different intermediate errors. Individuals with TBI performed worse than control participants on all the tasks investigated when considering correct versus incorrect answers. Furthermore, a series of logistic regression analyses showed that group membership (TBI versus controls) significantly predicted the occurrence of intermediate errors. This result holds in both the comprehension and production tasks, and in both linguistic and extralinguistic modalities. Participants with TBI tend to have difficulty in managing different types of communicative acts, and they make more intermediate errors than the control participants. Intermediate errors concern the comprehension and production of the expression act, the comprehension of the actors' meaning, as well as the respect of the Cooperative principle. © 2014 Royal College of Speech and Language Therapists.

A Novel Extreme Learning Control Framework of Unmanned Surface Vehicles.

PubMed

Wang, Ning; Sun, Jing-Chao; Er, Meng Joo; Liu, Yan-Cheng

2016-05-01

In this paper, an extreme learning control (ELC) framework using the single-hidden-layer feedforward network (SLFN) with random hidden nodes for tracking an unmanned surface vehicle suffering from unknown dynamics and external disturbances is proposed. By combining tracking errors with derivatives, an error surface and transformed states are defined to encapsulate unknown dynamics and disturbances into a lumped vector field of transformed states. The lumped nonlinearity is further identified accurately by an extreme-learning-machine-based SLFN approximator which does not require a priori system knowledge nor tuning input weights. Only output weights of the SLFN need to be updated by adaptive projection-based laws derived from the Lyapunov approach. Moreover, an error compensator is incorporated to suppress approximation residuals, and thereby contributing to the robustness and global asymptotic stability of the closed-loop ELC system. Simulation studies and comprehensive comparisons demonstrate that the ELC framework achieves high accuracy in both tracking and approximation.

Preschool speech error patterns predict articulation and phonological awareness outcomes in children with histories of speech sound disorders.

PubMed

Preston, Jonathan L; Hull, Margaret; Edwards, Mary Louise

2013-05-01

To determine if speech error patterns in preschoolers with speech sound disorders (SSDs) predict articulation and phonological awareness (PA) outcomes almost 4 years later. Twenty-five children with histories of preschool SSDs (and normal receptive language) were tested at an average age of 4;6 (years;months) and were followed up at age 8;3. The frequency of occurrence of preschool distortion errors, typical substitution and syllable structure errors, and atypical substitution and syllable structure errors was used to predict later speech sound production, PA, and literacy outcomes. Group averages revealed below-average school-age articulation scores and low-average PA but age-appropriate reading and spelling. Preschool speech error patterns were related to school-age outcomes. Children for whom >10% of their speech sound errors were atypical had lower PA and literacy scores at school age than children who produced <10% atypical errors. Preschoolers who produced more distortion errors were likely to have lower school-age articulation scores than preschoolers who produced fewer distortion errors. Different preschool speech error patterns predict different school-age clinical outcomes. Many atypical speech sound errors in preschoolers may be indicative of weak phonological representations, leading to long-term PA weaknesses. Preschoolers' distortions may be resistant to change over time, leading to persisting speech sound production problems.

Linear regression models for solvent accessibility prediction in proteins.

PubMed

Wagner, Michael; Adamczak, Rafał; Porollo, Aleksey; Meller, Jarosław

2005-04-01

The relative solvent accessibility (RSA) of an amino acid residue in a protein structure is a real number that represents the solvent exposed surface area of this residue in relative terms. The problem of predicting the RSA from the primary amino acid sequence can therefore be cast as a regression problem. Nevertheless, RSA prediction has so far typically been cast as a classification problem. Consequently, various machine learning techniques have been used within the classification framework to predict whether a given amino acid exceeds some (arbitrary) RSA threshold and would thus be predicted to be "exposed," as opposed to "buried." We have recently developed novel methods for RSA prediction using nonlinear regression techniques which provide accurate estimates of the real-valued RSA and outperform classification-based approaches with respect to commonly used two-class projections. However, while their performance seems to provide a significant improvement over previously published approaches, these Neural Network (NN) based methods are computationally expensive to train and involve several thousand parameters. In this work, we develop alternative regression models for RSA prediction which are computationally much less expensive, involve orders-of-magnitude fewer parameters, and are still competitive in terms of prediction quality. In particular, we investigate several regression models for RSA prediction using linear L1-support vector regression (SVR) approaches as well as standard linear least squares (LS) regression. Using rigorously derived validation sets of protein structures and extensive cross-validation analysis, we compare the performance of the SVR with that of LS regression and NN-based methods. In particular, we show that the flexibility of the SVR (as encoded by metaparameters such as the error insensitivity and the error penalization terms) can be very beneficial to optimize the prediction accuracy for buried residues. We conclude that the simple and computationally much more efficient linear SVR performs comparably to nonlinear models and thus can be used in order to facilitate further attempts to design more accurate RSA prediction methods, with applications to fold recognition and de novo protein structure prediction methods.

Very-short-term wind power prediction by a hybrid model with single- and multi-step approaches

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

Very-short-term wind power prediction (VSTWPP) has played an essential role for the operation of electric power systems. This paper aims at improving and applying a hybrid method of VSTWPP based on historical data. The hybrid method is combined by multiple linear regressions and least square (MLR&LS), which is intended for reducing prediction errors. The predicted values are obtained through two sub-processes:1) transform the time-series data of actual wind power into the power ratio, and then predict the power ratio;2) use the predicted power ratio to predict the wind power. Besides, the proposed method can include two prediction approaches: single-step prediction (SSP) and multi-step prediction (MSP). WPP is tested comparatively by auto-regressive moving average (ARMA) model from the predicted values and errors. The validity of the proposed hybrid method is confirmed in terms of error analysis by using probability density function (PDF), mean absolute percent error (MAPE) and means square error (MSE). Meanwhile, comparison of the correlation coefficients between the actual values and the predicted values for different prediction times and window has confirmed that MSP approach by using the hybrid model is the most accurate while comparing to SSP approach and ARMA. The MLR&LS is accurate and promising for solving problems in WPP.

Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery

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

Vedam, S.; Docef, A.; Fix, M.

2005-06-15

The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effectsmore » of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.« less

How infants' reaches reveal principles of sensorimotor decision making

NASA Astrophysics Data System (ADS)

Dineva, Evelina; Schöner, Gregor

2018-01-01

In Piaget's classical A-not-B-task, infants repeatedly make a sensorimotor decision to reach to one of two cued targets. Perseverative errors are induced by switching the cue from A to B, while spontaneous errors are unsolicited reaches to B when only A is cued. We argue that theoretical accounts of sensorimotor decision-making fail to address how motor decisions leave a memory trace that may impact future sensorimotor decisions. Instead, in extant neural models, perseveration is caused solely by the history of stimulation. We present a neural dynamic model of sensorimotor decision-making within the framework of Dynamic Field Theory, in which a dynamic instability amplifies fluctuations in neural activation into macroscopic, stable neural activation states that leave memory traces. The model predicts perseveration, but also a tendency to repeat spontaneous errors. To test the account, we pool data from several A-not-B experiments. A conditional probabilities analysis accounts quantitatively how motor decisions depend on the history of reaching. The results provide evidence for the interdependence among subsequent reaching decisions that is explained by the model, showing that by amplifying small differences in activation and affecting learning, decisions have consequences beyond the individual behavioural act.

The cost of misremembering: Inferring the loss function in visual working memory.

PubMed

Sims, Chris R

2015-03-04

Visual working memory (VWM) is a highly limited storage system. A basic consequence of this fact is that visual memories cannot perfectly encode or represent the veridical structure of the world. However, in natural tasks, some memory errors might be more costly than others. This raises the intriguing possibility that the nature of memory error reflects the costs of committing different kinds of errors. Many existing theories assume that visual memories are noise-corrupted versions of afferent perceptual signals. However, this additive noise assumption oversimplifies the problem. Implicit in the behavioral phenomena of visual working memory is the concept of a loss function: a mathematical entity that describes the relative cost to the organism of making different types of memory errors. An optimally efficient memory system is one that minimizes the expected loss according to a particular loss function, while subject to a constraint on memory capacity. This paper describes a novel theoretical framework for characterizing visual working memory in terms of its implicit loss function. Using inverse decision theory, the empirical loss function is estimated from the results of a standard delayed recall visual memory experiment. These results are compared to the predicted behavior of a visual working memory system that is optimally efficient for a previously identified natural task, gaze correction following saccadic error. Finally, the approach is compared to alternative models of visual working memory, and shown to offer a superior account of the empirical data across a range of experimental datasets. © 2015 ARVO.

De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets

NASA Astrophysics Data System (ADS)

Hemati, Maziar S.; Rowley, Clarence W.; Deem, Eric A.; Cattafesta, Louis N.

2017-08-01

The dynamic mode decomposition (DMD)—a popular method for performing data-driven Koopman spectral analysis—has gained increased popularity for extracting dynamically meaningful spatiotemporal descriptions of fluid flows from snapshot measurements. Often times, DMD descriptions can be used for predictive purposes as well, which enables informed decision-making based on DMD model forecasts. Despite its widespread use and utility, DMD can fail to yield accurate dynamical descriptions when the measured snapshot data are imprecise due to, e.g., sensor noise. Here, we express DMD as a two-stage algorithm in order to isolate a source of systematic error. We show that DMD's first stage, a subspace projection step, systematically introduces bias errors by processing snapshots asymmetrically. To remove this systematic error, we propose utilizing an augmented snapshot matrix in a subspace projection step, as in problems of total least-squares, in order to account for the error present in all snapshots. The resulting unbiased and noise-aware total DMD (TDMD) formulation reduces to standard DMD in the absence of snapshot errors, while the two-stage perspective generalizes the de-biasing framework to other related methods as well. TDMD's performance is demonstrated in numerical and experimental fluids examples. In particular, in the analysis of time-resolved particle image velocimetry data for a separated flow, TDMD outperforms standard DMD by providing dynamical interpretations that are consistent with alternative analysis techniques. Further, TDMD extracts modes that reveal detailed spatial structures missed by standard DMD.

Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques.

PubMed

Goo, Yeung-Ja James; Chi, Der-Jang; Shen, Zong-De

2016-01-01

The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samples of this study include 48 GCD listed companies and 124 NGCD (non-GCD) listed companies from 2002 to 2013 in the TEJ database. We conduct fivefold cross validation in order to identify the prediction accuracy. According to the empirical results, the prediction accuracy of the LASSO-NN model is 88.96 % (Type I error rate is 12.22 %; Type II error rate is 7.50 %), the prediction accuracy of the LASSO-CART model is 88.75 % (Type I error rate is 13.61 %; Type II error rate is 14.17 %), and the prediction accuracy of the LASSO-SVM model is 89.79 % (Type I error rate is 10.00 %; Type II error rate is 15.83 %).

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

Newman, Jennifer F.; Clifton, Andrew

Currently, cup anemometers on meteorological towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability; however, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install meteorological towers at potential sites. As a result, remote-sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. Although lidars can accurately estimate mean wind speeds and wind directions, there is still a large amount ofmore » uncertainty surrounding the measurement of turbulence using these devices. Errors in lidar turbulence estimates are caused by a variety of factors, including instrument noise, volume averaging, and variance contamination, in which the magnitude of these factors is highly dependent on measurement height and atmospheric stability. As turbulence has a large impact on wind power production, errors in turbulence measurements will translate into errors in wind power prediction. The impact of using lidars rather than cup anemometers for wind power prediction must be understood if lidars are to be considered a viable alternative to cup anemometers.In this poster, the sensitivity of power prediction error to typical lidar turbulence measurement errors is assessed. Turbulence estimates from a vertically profiling WINDCUBE v2 lidar are compared to high-resolution sonic anemometer measurements at field sites in Oklahoma and Colorado to determine the degree of lidar turbulence error that can be expected under different atmospheric conditions. These errors are then incorporated into a power prediction model to estimate the sensitivity of power prediction error to turbulence measurement error. Power prediction models, including the standard binning method and a random forest method, were developed using data from the aeroelastic simulator FAST for a 1.5 MW turbine. The impact of lidar turbulence error on the predicted power from these different models is examined to determine the degree of turbulence measurement accuracy needed for accurate power prediction.« less

Economic and health risk trade-offs of swim closures at a Lake Michigan beach

USGS Publications Warehouse

Rabinovici, Sharyl M.; Bernknopf, Richard L.; Wein, Anne M.; Coursey, Don L.; Whitman, Richard L.

2004-01-01

This paper presents a framework for analyzing the economic, health, and recreation implications of swim closures related to high fecal indicator bacteria (FIB) levels. The framework utilizes benefit transfer policy analysis to provide a practical procedure for estimating the effectiveness of recreational water quality policies. Evaluation criteria include the rates of intended and unintended management outcomes, whether the chosen protocols generate closures with positive net economic benefits to swimmers, and the number of predicted illnesses the policy is able to prevent. We demonstrate the framework through a case study of a Lake Michigan freshwater beach using existing water quality and visitor data from 1998 to 2001. We find that a typical closure causes a net economic loss among would-be swimmers totaling $1274-37 030/ day, depending on the value assumptions used. Unnecessary closures, caused by high indicator variability and a 24-h time delay between when samples are taken and the management decision can be made, occurred on 14 (12%) out of 118 monitored summer days. Days with high FIB levels when the swim area is open are also common but do relatively little economic harm in comparison. Also, even if the closure policy could be implemented daily and perfectly without error, only about 42% of predicted illnesses would be avoided. These conclusions were sensitive to the relative values and risk preferences that swimmers have for recreation access and avoiding health effects, suggesting a need for further study of the impacts of recreational water quality policies on individuals.

Exploring behavioural determinants relating to health professional reporting of medication errors: a qualitative study using the Theoretical Domains Framework.

PubMed

Alqubaisi, Mai; Tonna, Antonella; Strath, Alison; Stewart, Derek

2016-07-01

Effective and efficient medication reporting processes are essential in promoting patient safety. Few qualitative studies have explored reporting of medication errors by health professionals, and none have made reference to behavioural theories. The objective was to describe and understand the behavioural determinants of health professional reporting of medication errors in the United Arab Emirates (UAE). This was a qualitative study comprising face-to-face, semi-structured interviews within three major medical/surgical hospitals of Abu Dhabi, the UAE. Health professionals were sampled purposively in strata of profession and years of experience. The semi-structured interview schedule focused on behavioural determinants around medication error reporting, facilitators, barriers and experiences. The Theoretical Domains Framework (TDF; a framework of theories of behaviour change) was used as a coding framework. Ethical approval was obtained from a UK university and all participating hospital ethics committees. Data saturation was achieved after interviewing ten nurses, ten pharmacists and nine physicians. Whilst it appeared that patient safety and organisational improvement goals and intentions were behavioural determinants which facilitated reporting, there were key determinants which deterred reporting. These included the beliefs of the consequences of reporting (lack of any feedback following reporting and impacting professional reputation, relationships and career progression), emotions (fear and worry) and issues related to the environmental context (time taken to report). These key behavioural determinants which negatively impact error reporting can facilitate the development of an intervention, centring on organisational safety and reporting culture, to enhance reporting effectiveness and efficiency.

Simulations using patient data to evaluate systematic errors that may occur in 4D treatment planning: a proof of concept study.

PubMed

St James, Sara; Seco, Joao; Mishra, Pankaj; Lewis, John H

2013-09-01

The purpose of this work is to present a framework to evaluate the accuracy of four-dimensional treatment planning in external beam radiation therapy using measured patient data and digital phantoms. To accomplish this, 4D digital phantoms of two model patients were created using measured patient lung tumor positions. These phantoms were used to simulate a four-dimensional computed tomography image set, which in turn was used to create a 4D Monte Carlo (4DMC) treatment plan. The 4DMC plan was evaluated by simulating the delivery of the treatment plan over approximately 5 min of tumor motion measured from the same patient on a different day. Unique phantoms accounting for the patient position (tumor position and thorax position) at 2 s intervals were used to represent the model patients on the day of treatment delivery and the delivered dose to the tumor was determined using Monte Carlo simulations. For Patient 1, the tumor was adequately covered with 95.2% of the tumor receiving the prescribed dose. For Patient 2, the tumor was not adequately covered and only 74.3% of the tumor received the prescribed dose. This study presents a framework to evaluate 4D treatment planning methods and demonstrates a potential limitation of 4D treatment planning methods. When systematic errors are present, including when the imaging study used for treatment planning does not represent all potential tumor locations during therapy, the treatment planning methods may not adequately predict the dose to the tumor. This is the first example of a simulation study based on patient tumor trajectories where systematic errors that occur due to an inaccurate estimate of tumor motion are evaluated.

Error disclosure: a new domain for safety culture assessment.

PubMed

Etchegaray, Jason M; Gallagher, Thomas H; Bell, Sigall K; Dunlap, Ben; Thomas, Eric J

2012-07-01

To (1) develop and test survey items that measure error disclosure culture, (2) examine relationships among error disclosure culture, teamwork culture and safety culture and (3) establish predictive validity for survey items measuring error disclosure culture. All clinical faculty from six health institutions (four medical schools, one cancer centre and one health science centre) in The University of Texas System were invited to anonymously complete an electronic survey containing questions about safety culture and error disclosure. The authors found two factors to measure error disclosure culture: one factor is focused on the general culture of error disclosure and the second factor is focused on trust. Both error disclosure culture factors were unique from safety culture and teamwork culture (correlations were less than r=0.85). Also, error disclosure general culture and error disclosure trust culture predicted intent to disclose a hypothetical error to a patient (r=0.25, p<0.001 and r=0.16, p<0.001, respectively) while teamwork and safety culture did not predict such an intent (r=0.09, p=NS and r=0.12, p=NS). Those who received prior error disclosure training reported significantly higher levels of error disclosure general culture (t=3.7, p<0.05) and error disclosure trust culture (t=2.9, p<0.05). The authors created and validated a new measure of error disclosure culture that predicts intent to disclose an error better than other measures of healthcare culture. This measure fills an existing gap in organisational assessments by assessing transparent communication after medical error, an important aspect of culture.

Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

NASA Technical Reports Server (NTRS)

Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan

2013-01-01

The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

Statistical analysis of modeling error in structural dynamic systems

NASA Technical Reports Server (NTRS)

Hasselman, T. K.; Chrostowski, J. D.

1990-01-01

The paper presents a generic statistical model of the (total) modeling error for conventional space structures in their launch configuration. Modeling error is defined as the difference between analytical prediction and experimental measurement. It is represented by the differences between predicted and measured real eigenvalues and eigenvectors. Comparisons are made between pre-test and post-test models. Total modeling error is then subdivided into measurement error, experimental error and 'pure' modeling error, and comparisons made between measurement error and total modeling error. The generic statistical model presented in this paper is based on the first four global (primary structure) modes of four different structures belonging to the generic category of Conventional Space Structures (specifically excluding large truss-type space structures). As such, it may be used to evaluate the uncertainty of predicted mode shapes and frequencies, sinusoidal response, or the transient response of other structures belonging to the same generic category.

Error management for musicians: an interdisciplinary conceptual framework

PubMed Central

Kruse-Weber, Silke; Parncutt, Richard

2014-01-01

Musicians tend to strive for flawless performance and perfection, avoiding errors at all costs. Dealing with errors while practicing or performing is often frustrating and can lead to anger and despair, which can explain musicians’ generally negative attitude toward errors and the tendency to aim for flawless learning in instrumental music education. But even the best performances are rarely error-free, and research in general pedagogy and psychology has shown that errors provide useful information for the learning process. Research in instrumental pedagogy is still neglecting error issues; the benefits of risk management (before the error) and error management (during and after the error) are still underestimated. It follows that dealing with errors is a key aspect of music practice at home, teaching, and performance in public. And yet, to be innovative, or to make their performance extraordinary, musicians need to risk errors. Currently, most music students only acquire the ability to manage errors implicitly – or not at all. A more constructive, creative, and differentiated culture of errors would balance error tolerance and risk-taking against error prevention in ways that enhance music practice and music performance. The teaching environment should lay the foundation for the development of such an approach. In this contribution, we survey recent research in aviation, medicine, economics, psychology, and interdisciplinary decision theory that has demonstrated that specific error-management training can promote metacognitive skills that lead to better adaptive transfer and better performance skills. We summarize how this research can be applied to music, and survey-relevant research that is specifically tailored to the needs of musicians, including generic guidelines for risk and error management in music teaching and performance. On this basis, we develop a conceptual framework for risk management that can provide orientation for further music education and musicians at all levels. PMID:25120501

Error management for musicians: an interdisciplinary conceptual framework.

PubMed

Kruse-Weber, Silke; Parncutt, Richard

2014-01-01

Musicians tend to strive for flawless performance and perfection, avoiding errors at all costs. Dealing with errors while practicing or performing is often frustrating and can lead to anger and despair, which can explain musicians' generally negative attitude toward errors and the tendency to aim for flawless learning in instrumental music education. But even the best performances are rarely error-free, and research in general pedagogy and psychology has shown that errors provide useful information for the learning process. Research in instrumental pedagogy is still neglecting error issues; the benefits of risk management (before the error) and error management (during and after the error) are still underestimated. It follows that dealing with errors is a key aspect of music practice at home, teaching, and performance in public. And yet, to be innovative, or to make their performance extraordinary, musicians need to risk errors. Currently, most music students only acquire the ability to manage errors implicitly - or not at all. A more constructive, creative, and differentiated culture of errors would balance error tolerance and risk-taking against error prevention in ways that enhance music practice and music performance. The teaching environment should lay the foundation for the development of such an approach. In this contribution, we survey recent research in aviation, medicine, economics, psychology, and interdisciplinary decision theory that has demonstrated that specific error-management training can promote metacognitive skills that lead to better adaptive transfer and better performance skills. We summarize how this research can be applied to music, and survey-relevant research that is specifically tailored to the needs of musicians, including generic guidelines for risk and error management in music teaching and performance. On this basis, we develop a conceptual framework for risk management that can provide orientation for further music education and musicians at all levels.

Efficient Methods to Assimilate Satellite Retrievals Based on Information Content. Part 2; Suboptimal Retrieval Assimilation

NASA Technical Reports Server (NTRS)

Joiner, J.; Dee, D. P.

1998-01-01

One of the outstanding problems in data assimilation has been and continues to be how best to utilize satellite data while balancing the tradeoff between accuracy and computational cost. A number of weather prediction centers have recently achieved remarkable success in improving their forecast skill by changing the method by which satellite data are assimilated into the forecast model from the traditional approach of assimilating retrievals to the direct assimilation of radiances in a variational framework. The operational implementation of such a substantial change in methodology involves a great number of technical details, e.g., pertaining to quality control procedures, systematic error correction techniques, and tuning of the statistical parameters in the analysis algorithm. Although there are clear theoretical advantages to the direct radiance assimilation approach, it is not obvious at all to what extent the improvements that have been obtained so far can be attributed to the change in methodology, or to various technical aspects of the implementation. The issue is of interest because retrieval assimilation retains many practical and logistical advantages which may become even more significant in the near future when increasingly high-volume data sources become available. The central question we address here is: how much improvement can we expect from assimilating radiances rather than retrievals, all other things being equal? We compare the two approaches in a simplified one-dimensional theoretical framework, in which problems related to quality control and systematic error correction are conveniently absent. By assuming a perfect radiative transfer model and perfect knowledge of radiance and background error covariances, we are able to formulate a nonlinear local error analysis for each assimilation method. Direct radiance assimilation is optimal in this idealized context, while the traditional method of assimilating retrievals is suboptimal because it ignores the cross-covariances between background errors and retrieval errors. We show that interactive retrieval assimilation (where the same background used for assimilation is also used in the retrieval step) is equivalent to direct assimilation of radiances with suboptimal analysis weights. We illustrate and extend these theoretical arguments with several one-dimensional assimilation experiments, where we estimate vertical atmospheric profiles using simulated data from both the High-resolution InfraRed Sounder 2 (HIRS2) and the future Atmospheric InfraRed Sounder (AIRS).

Performance of a coupled lagged ensemble weather and river runoff prediction model system for the Alpine Ammer River catchment

NASA Astrophysics Data System (ADS)

Smiatek, G.; Kunstmann, H.; Werhahn, J.

2012-04-01

The Ammer River catchment located in the Bavarian Ammergau Alps and alpine forelands, Germany, represents with elevations reaching 2185 m and annual mean precipitation between1100 and 2000 mm a very demanding test ground for a river runoff prediction system. Large flooding events in 1999 and 2005 motivated the development of a physically based prediction tool in this area. Such a tool is the coupled high resolution numerical weather and river runoff forecasting system AM-POE that is being studied in several configurations in various experiments starting from the year 2005. Corner stones of the coupled system are the hydrological water balance model WaSiM-ETH run at 100 m grid resolution, the numerical weather prediction model (NWP) MM5 driven at 3.5 km grid cell resolution and the Perl Object Environment (POE) framework. POE implements the input data download from various sources, the input data provision via SOAP based WEB services as well as the runs of the hydrology model both with observed and with NWP predicted meteorology input. The one way coupled system utilizes a lagged ensemble prediction system (EPS) taking into account combination of recent and previous NWP forecasts. Results obtained in the years 2005-2011 reveal that river runoff simulations depict high correlation with observed runoff when driven with monitored observations in hindcast experiments. The ability to runoff forecasts is depending on lead times in the lagged ensemble prediction and shows still limitations resulting from errors in timing and total amount of the predicted precipitation in the complex mountainous area. The presentation describes the system implementation, and demonstrates the application of the POE framework in networking, distributed computing and in the setup of various experiments as well as long term results of the system application in the years 2005 - 2011.

The many worlds hypothesis of dopamine prediction error: implications of a parallel circuit architecture in the basal ganglia.

PubMed

Lau, Brian; Monteiro, Tiago; Paton, Joseph J

2017-10-01

Computational models of reinforcement learning (RL) strive to produce behavior that maximises reward, and thus allow software or robots to behave adaptively [1]. At the core of RL models is a learned mapping between 'states'-situations or contexts that an agent might encounter in the world-and actions. A wealth of physiological and anatomical data suggests that the basal ganglia (BG) is important for learning these mappings [2,3]. However, the computations performed by specific circuits are unclear. In this brief review, we highlight recent work concerning the anatomy and physiology of BG circuits that suggest refinements in our understanding of computations performed by the basal ganglia. We focus on one important component of basal ganglia circuitry, midbrain dopamine neurons, drawing attention to data that has been cast as supporting or departing from the RL framework that has inspired experiments in basal ganglia research over the past two decades. We suggest that the parallel circuit architecture of the BG might be expected to produce variability in the response properties of different dopamine neurons, and that variability in response profile may not reflect variable functions, but rather different arguments that serve as inputs to a common function: the computation of prediction error. Copyright © 2017 Elsevier Ltd. All rights reserved.

Preschool speech error patterns predict articulation and phonological awareness outcomes in children with histories of speech sound disorders

PubMed Central

Preston, Jonathan L.; Hull, Margaret; Edwards, Mary Louise

2012-01-01

Purpose To determine if speech error patterns in preschoolers with speech sound disorders (SSDs) predict articulation and phonological awareness (PA) outcomes almost four years later. Method Twenty-five children with histories of preschool SSDs (and normal receptive language) were tested at an average age of 4;6 and followed up at 8;3. The frequency of occurrence of preschool distortion errors, typical substitution and syllable structure errors, and atypical substitution and syllable structure errors were used to predict later speech sound production, PA, and literacy outcomes. Results Group averages revealed below-average school-age articulation scores and low-average PA, but age-appropriate reading and spelling. Preschool speech error patterns were related to school-age outcomes. Children for whom more than 10% of their speech sound errors were atypical had lower PA and literacy scores at school-age than children who produced fewer than 10% atypical errors. Preschoolers who produced more distortion errors were likely to have lower school-age articulation scores. Conclusions Different preschool speech error patterns predict different school-age clinical outcomes. Many atypical speech sound errors in preschool may be indicative of weak phonological representations, leading to long-term PA weaknesses. Preschool distortions may be resistant to change over time, leading to persisting speech sound production problems. PMID:23184137

Coherent evolution of parahydrogen induced polarisation using laser pump, NMR probe spectroscopy: Theoretical framework and experimental observation.

PubMed

Halse, Meghan E; Procacci, Barbara; Henshaw, Sarah-Louise; Perutz, Robin N; Duckett, Simon B

2017-05-01

We recently reported a pump-probe method that uses a single laser pulse to introduce parahydrogen (p-H 2 ) into a metal dihydride complex and then follows the time-evolution of the p-H 2 -derived nuclear spin states by NMR. We present here a theoretical framework to describe the oscillatory behaviour of the resultant hyperpolarised NMR signals using a product operator formalism. We consider the cases where the p-H 2 -derived protons form part of an AX, AXY, AXYZ or AA'XX' spin system in the product molecule. We use this framework to predict the patterns for 2D pump-probe NMR spectra, where the indirect dimension represents the evolution during the pump-probe delay and the positions of the cross-peaks depend on the difference in chemical shift of the p-H 2 -derived protons and the difference in their couplings to other nuclei. The evolution of the NMR signals of the p-H 2 -derived protons, as well as the transfer of hyperpolarisation to other NMR-active nuclei in the product, is described. The theoretical framework is tested experimentally for a set of ruthenium dihydride complexes representing the different spin systems. Theoretical predictions and experimental results agree to within experimental error for all features of the hyperpolarised 1 H and 31 P pump-probe NMR spectra. Thus we establish the laser pump, NMR probe approach as a robust way to directly observe and quantitatively analyse the coherent evolution of p-H 2 -derived spin order over micro-to-millisecond timescales. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Robust cubature Kalman filter for GNSS/INS with missing observations and colored measurement noise.

PubMed

Cui, Bingbo; Chen, Xiyuan; Tang, Xihua; Huang, Haoqian; Liu, Xiao

2018-01-01

In order to improve the accuracy of GNSS/INS working in GNSS-denied environment, a robust cubature Kalman filter (RCKF) is developed by considering colored measurement noise and missing observations. First, an improved cubature Kalman filter (CKF) is derived by considering colored measurement noise, where the time-differencing approach is applied to yield new observations. Then, after analyzing the disadvantages of existing methods, the measurement augment in processing colored noise is translated into processing the uncertainties of CKF, and new sigma point update framework is utilized to account for the bounded model uncertainties. By reusing the diffused sigma points and approximation residual in the prediction stage of CKF, the RCKF is developed and its error performance is analyzed theoretically. Results of numerical experiment and field test reveal that RCKF is more robust than CKF and extended Kalman filter (EKF), and compared with EKF, the heading error of land vehicle is reduced by about 72.4%. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics.

PubMed

Xu, Bin; Yang, Daipeng; Shi, Zhongke; Pan, Yongping; Chen, Badong; Sun, Fuchun

2017-09-25

This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.

Somato-dendritic Synaptic Plasticity and Error-backpropagation in Active Dendrites

PubMed Central

Schiess, Mathieu; Urbanczik, Robert; Senn, Walter

2016-01-01

In the last decade dendrites of cortical neurons have been shown to nonlinearly combine synaptic inputs by evoking local dendritic spikes. It has been suggested that these nonlinearities raise the computational power of a single neuron, making it comparable to a 2-layer network of point neurons. But how these nonlinearities can be incorporated into the synaptic plasticity to optimally support learning remains unclear. We present a theoretically derived synaptic plasticity rule for supervised and reinforcement learning that depends on the timing of the presynaptic, the dendritic and the postsynaptic spikes. For supervised learning, the rule can be seen as a biological version of the classical error-backpropagation algorithm applied to the dendritic case. When modulated by a delayed reward signal, the same plasticity is shown to maximize the expected reward in reinforcement learning for various coding scenarios. Our framework makes specific experimental predictions and highlights the unique advantage of active dendrites for implementing powerful synaptic plasticity rules that have access to downstream information via backpropagation of action potentials. PMID:26841235

Real-time prediction and gating of respiratory motion in 3D space using extended Kalman filters and Gaussian process regression network

NASA Astrophysics Data System (ADS)

Bukhari, W.; Hong, S.-M.

2016-03-01

The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the radiation treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting respiratory motion in 3D space and realizing a gating function without pre-specifying a particular phase of the patient’s breathing cycle. The algorithm, named EKF-GPRN+ , first employs an extended Kalman filter (EKF) independently along each coordinate to predict the respiratory motion and then uses a Gaussian process regression network (GPRN) to correct the prediction error of the EKF in 3D space. The GPRN is a nonparametric Bayesian algorithm for modeling input-dependent correlations between the output variables in multi-output regression. Inference in GPRN is intractable and we employ variational inference with mean field approximation to compute an approximate predictive mean and predictive covariance matrix. The approximate predictive mean is used to correct the prediction error of the EKF. The trace of the approximate predictive covariance matrix is utilized to capture the uncertainty in EKF-GPRN+ prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification enables us to pause the treatment beam over such instances. EKF-GPRN+ implements a gating function by using simple calculations based on the trace of the predictive covariance matrix. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPRN+ . The experimental results show that the EKF-GPRN+ algorithm reduces the patient-wise prediction error to 38%, 40% and 40% in root-mean-square, compared to no prediction, at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The EKF-GPRN+ algorithm can further reduce the prediction error by employing the gating function, albeit at the cost of reduced duty cycle. The error reduction allows the clinical target volume to planning target volume (CTV-PTV) margin to be reduced, leading to decreased normal-tissue toxicity and possible dose escalation. The CTV-PTV margin is also evaluated to quantify clinical benefits of EKF-GPRN+ prediction.

TOPEX/POSEIDON orbit maintenance maneuver design

NASA Technical Reports Server (NTRS)

Bhat, R. S.; Frauenholz, R. B.; Cannell, Patrick E.

1990-01-01

The Ocean Topography Experiment (TOPEX/POSEIDON) mission orbit requirements are outlined, as well as its control and maneuver spacing requirements including longitude and time targeting. A ground-track prediction model dealing with geopotential, luni-solar gravity, and atmospheric-drag perturbations is considered. Targeting with all modeled perturbations is discussed, and such ground-track prediction errors as initial semimajor axis, orbit-determination, maneuver-execution, and atmospheric-density modeling errors are assessed. A longitude targeting strategy for two extreme situations is investigated employing all modeled perturbations and prediction errors. It is concluded that atmospheric-drag modeling errors are the prevailing ground-track prediction error source early in the mission during high solar flux, and that low solar-flux levels expected late in the experiment stipulate smaller maneuver magnitudes.

The impact of experimental measurement errors on long-term viscoelastic predictions. [of structural materials

NASA Technical Reports Server (NTRS)

Tuttle, M. E.; Brinson, H. F.

1986-01-01

The impact of flight error in measured viscoelastic parameters on subsequent long-term viscoelastic predictions is numerically evaluated using the Schapery nonlinear viscoelastic model. Of the seven Schapery parameters, the results indicated that long-term predictions were most sensitive to errors in the power law parameter n. Although errors in the other parameters were significant as well, errors in n dominated all other factors at long times. The process of selecting an appropriate short-term test cycle so as to insure an accurate long-term prediction was considered, and a short-term test cycle was selected using material properties typical for T300/5208 graphite-epoxy at 149 C. The process of selection is described, and its individual steps are itemized.

Comparison of various error functions in predicting the optimum isotherm by linear and non-linear regression analysis for the sorption of basic red 9 by activated carbon.

PubMed

Kumar, K Vasanth; Porkodi, K; Rocha, F

2008-01-15

A comparison of linear and non-linear regression method in selecting the optimum isotherm was made to the experimental equilibrium data of basic red 9 sorption by activated carbon. The r(2) was used to select the best fit linear theoretical isotherm. In the case of non-linear regression method, six error functions namely coefficient of determination (r(2)), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), the average relative error (ARE), sum of the errors squared (ERRSQ) and sum of the absolute errors (EABS) were used to predict the parameters involved in the two and three parameter isotherms and also to predict the optimum isotherm. Non-linear regression was found to be a better way to obtain the parameters involved in the isotherms and also the optimum isotherm. For two parameter isotherm, MPSD was found to be the best error function in minimizing the error distribution between the experimental equilibrium data and predicted isotherms. In the case of three parameter isotherm, r(2) was found to be the best error function to minimize the error distribution structure between experimental equilibrium data and theoretical isotherms. The present study showed that the size of the error function alone is not a deciding factor to choose the optimum isotherm. In addition to the size of error function, the theory behind the predicted isotherm should be verified with the help of experimental data while selecting the optimum isotherm. A coefficient of non-determination, K(2) was explained and was found to be very useful in identifying the best error function while selecting the optimum isotherm.

Dopamine prediction error responses integrate subjective value from different reward dimensions

PubMed Central

Lak, Armin; Stauffer, William R.; Schultz, Wolfram

2014-01-01

Prediction error signals enable us to learn through experience. These experiences include economic choices between different rewards that vary along multiple dimensions. Therefore, an ideal way to reinforce economic choice is to encode a prediction error that reflects the subjective value integrated across these reward dimensions. Previous studies demonstrated that dopamine prediction error responses reflect the value of singular reward attributes that include magnitude, probability, and delay. Obviously, preferences between rewards that vary along one dimension are completely determined by the manipulated variable. However, it is unknown whether dopamine prediction error responses reflect the subjective value integrated from different reward dimensions. Here, we measured the preferences between rewards that varied along multiple dimensions, and as such could not be ranked according to objective metrics. Monkeys chose between rewards that differed in amount, risk, and type. Because their choices were complete and transitive, the monkeys chose “as if” they integrated different rewards and attributes into a common scale of value. The prediction error responses of single dopamine neurons reflected the integrated subjective value inferred from the choices, rather than the singular reward attributes. Specifically, amount, risk, and reward type modulated dopamine responses exactly to the extent that they influenced economic choices, even when rewards were vastly different, such as liquid and food. This prediction error response could provide a direct updating signal for economic values. PMID:24453218

Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Schwartz, C. S.

2017-12-01

Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

A theoretical framework to predict the most likely ion path in particle imaging.

PubMed

Collins-Fekete, Charles-Antoine; Volz, Lennart; Portillo, Stephen K N; Beaulieu, Luc; Seco, Joao

2017-03-07

In this work, a generic rigorous Bayesian formalism is introduced to predict the most likely path of any ion crossing a medium between two detection points. The path is predicted based on a combination of the particle scattering in the material and measurements of its initial and final position, direction and energy. The path estimate's precision is compared to the Monte Carlo simulated path. Every ion from hydrogen to carbon is simulated in two scenarios, (1) where the range is fixed and (2) where the initial velocity is fixed. In the scenario where the range is kept constant, the maximal root-mean-square error between the estimated path and the Monte Carlo path drops significantly between the proton path estimate (0.50 mm) and the helium path estimate (0.18 mm), but less so up to the carbon path estimate (0.09 mm). However, this scenario is identified as the configuration that maximizes the dose while minimizing the path resolution. In the scenario where the initial velocity is fixed, the maximal root-mean-square error between the estimated path and the Monte Carlo path drops significantly between the proton path estimate (0.29 mm) and the helium path estimate (0.09 mm) but increases for heavier ions up to carbon (0.12 mm). As a result, helium is found to be the particle with the most accurate path estimate for the lowest dose, potentially leading to tomographic images of higher spatial resolution.

Finite element simulation of interactions between pelvic organs: predictive model of the prostate motion in the context of radiotherapy.

PubMed

Boubaker, Mohamed Bader; Haboussi, Mohamed; Ganghoffer, Jean-François; Aletti, Pierre

2009-08-25

The setting up of predictive models of the pelvic organ motion and deformation may prove an efficient tool in the framework of prostate cancer radiotherapy, in order to deliver doses more accurately and efficiently to the clinical target volume (CTV). A finite element (FE) model of the prostate, rectum and bladder motion has been developed, investigating more specifically the influence of the rectum and bladder repletions on the gland motion. The required organ geometries are obtained after processing the computed tomography (CT) images, using specific softwares. Due to their structural characteristics, a 3D shell discretization is adopted for the rectum and the bladder, whereas a volume discretization is adopted for the prostate. As for the mechanical behavior modelling, first order Ogden hyperelastic constitutive laws for both the rectum and bladder are identified. The prostate is comparatively considered as more rigid and is accordingly modelled as an elastic tissue undergoing small strains. A FE model is then created, accounting for boundary and contact conditions, internal and applied loadings being selected as close as possible to available anatomic data. The order of magnitude of the prostate motion predicted by the FE simulations is similar to the measurements done on a deceased person, accounting for the delineation errors, with a relative error around 8%. Differences are essentially due to uncertainties in the constitutive parameters, pointing towards the need for the setting up of direct measurement of the organs mechanical behavior.

A new Method for the Estimation of Initial Condition Uncertainty Structures in Mesoscale Models

NASA Astrophysics Data System (ADS)

Keller, J. D.; Bach, L.; Hense, A.

2012-12-01

The estimation of fast growing error modes of a system is a key interest of ensemble data assimilation when assessing uncertainty in initial conditions. Over the last two decades three methods (and variations of these methods) have evolved for global numerical weather prediction models: ensemble Kalman filter, singular vectors and breeding of growing modes (or now ensemble transform). While the former incorporates a priori model error information and observation error estimates to determine ensemble initial conditions, the latter two techniques directly address the error structures associated with Lyapunov vectors. However, in global models these structures are mainly associated with transient global wave patterns. When assessing initial condition uncertainty in mesoscale limited area models, several problems regarding the aforementioned techniques arise: (a) additional sources of uncertainty on the smaller scales contribute to the error and (b) error structures from the global scale may quickly move through the model domain (depending on the size of the domain). To address the latter problem, perturbation structures from global models are often included in the mesoscale predictions as perturbed boundary conditions. However, the initial perturbations (when used) are often generated with a variant of an ensemble Kalman filter which does not necessarily focus on the large scale error patterns. In the framework of the European regional reanalysis project of the Hans-Ertel-Center for Weather Research we use a mesoscale model with an implemented nudging data assimilation scheme which does not support ensemble data assimilation at all. In preparation of an ensemble-based regional reanalysis and for the estimation of three-dimensional atmospheric covariance structures, we implemented a new method for the assessment of fast growing error modes for mesoscale limited area models. The so-called self-breeding is development based on the breeding of growing modes technique. Initial perturbations are integrated forward for a short time period and then rescaled and added to the initial state again. Iterating this rapid breeding cycle provides estimates for the initial uncertainty structure (or local Lyapunov vectors) given a specific norm. To avoid that all ensemble perturbations converge towards the leading local Lyapunov vector we apply an ensemble transform variant to orthogonalize the perturbations in the sub-space spanned by the ensemble. By choosing different kind of norms to measure perturbation growth, this technique allows for estimating uncertainty patterns targeted at specific sources of errors (e.g. convection, turbulence). With case study experiments we show applications of the self-breeding method for different sources of uncertainty and different horizontal scales.

How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

PubMed

Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C

2014-03-01

Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward prediction errors and the changes in amplitude of these prediction errors at the time of choice presentation and reward delivery. Our results provide further support that the computations that underlie human learning and decision-making follow reinforcement learning principles.

A two-phase sampling design for increasing detections of rare species in occupancy surveys

USGS Publications Warehouse

Pacifici, Krishna; Dorazio, Robert M.; Dorazio, Michael J.

2012-01-01

1. Occupancy estimation is a commonly used tool in ecological studies owing to the ease at which data can be collected and the large spatial extent that can be covered. One major obstacle to using an occupancy-based approach is the complications associated with designing and implementing an efficient survey. These logistical challenges become magnified when working with rare species when effort can be wasted in areas with none or very few individuals. 2. Here, we develop a two-phase sampling approach that mitigates these problems by using a design that places more effort in areas with higher predicted probability of occurrence. We compare our new sampling design to traditional single-season occupancy estimation under a range of conditions and population characteristics. We develop an intuitive measure of predictive error to compare the two approaches and use simulations to assess the relative accuracy of each approach. 3. Our two-phase approach exhibited lower predictive error rates compared to the traditional single-season approach in highly spatially correlated environments. The difference was greatest when detection probability was high (0·75) regardless of the habitat or sample size. When the true occupancy rate was below 0·4 (0·05-0·4), we found that allocating 25% of the sample to the first phase resulted in the lowest error rates. 4. In the majority of scenarios, the two-phase approach showed lower error rates compared to the traditional single-season approach suggesting our new approach is fairly robust to a broad range of conditions and design factors and merits use under a wide variety of settings. 5. Synthesis and applications. Conservation and management of rare species are a challenging task facing natural resource managers. It is critical for studies involving rare species to efficiently allocate effort and resources as they are usually of a finite nature. We believe our approach provides a framework for optimal allocation of effort while maximizing the information content of the data in an attempt to provide the highest conservation value per unit of effort.

Motion-induced error reduction by combining Fourier transform profilometry with phase-shifting profilometry.

PubMed

Li, Beiwen; Liu, Ziping; Zhang, Song

2016-10-03

We propose a hybrid computational framework to reduce motion-induced measurement error by combining the Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP). The proposed method is composed of three major steps: Step 1 is to extract continuous relative phase maps for each isolated object with single-shot FTP method and spatial phase unwrapping; Step 2 is to obtain an absolute phase map of the entire scene using PSP method, albeit motion-induced errors exist on the extracted absolute phase map; and Step 3 is to shift the continuous relative phase maps from Step 1 to generate final absolute phase maps for each isolated object by referring to the absolute phase map with error from Step 2. Experiments demonstrate the success of the proposed computational framework for measuring multiple isolated rapidly moving objects.

Predicting seasonal influenza transmission using functional regression models with temporal dependence.

PubMed

Oviedo de la Fuente, Manuel; Febrero-Bande, Manuel; Muñoz, María Pilar; Domínguez, Àngela

2018-01-01

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure [Formula: see text] was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.

Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor

NASA Astrophysics Data System (ADS)

Taverniers, Søren; Tartakovsky, Daniel M.

2017-11-01

Predictions of the total energy deposited into a brain tumor through X-ray irradiation are notoriously error-prone. We investigate how this predictive uncertainty is affected by uncertainty in both the location of the region occupied by a dose-enhancing iodinated contrast agent and the agent's concentration. This is done within the probabilistic framework in which these uncertain parameters are modeled as random variables. We employ the stochastic collocation (SC) method to estimate statistical moments of the deposited energy in terms of statistical moments of the random inputs, and the global sensitivity analysis (GSA) to quantify the relative importance of uncertainty in these parameters on the overall predictive uncertainty. A nonlinear radiation-diffusion equation dramatically magnifies the coefficient of variation of the uncertain parameters, yielding a large coefficient of variation for the predicted energy deposition. This demonstrates that accurate prediction of the energy deposition requires a proper treatment of even small parametric uncertainty. Our analysis also reveals that SC outperforms standard Monte Carlo, but its relative efficiency decreases as the number of uncertain parameters increases from one to three. A robust GSA ameliorates this problem by reducing this number.

Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance-covariance matrix.

PubMed

Holmes, John B; Dodds, Ken G; Lee, Michael A

2017-03-02

An important issue in genetic evaluation is the comparability of random effects (breeding values), particularly between pairs of animals in different contemporary groups. This is usually referred to as genetic connectedness. While various measures of connectedness have been proposed in the literature, there is general agreement that the most appropriate measure is some function of the prediction error variance-covariance matrix. However, obtaining the prediction error variance-covariance matrix is computationally demanding for large-scale genetic evaluations. Many alternative statistics have been proposed that avoid the computational cost of obtaining the prediction error variance-covariance matrix, such as counts of genetic links between contemporary groups, gene flow matrices, and functions of the variance-covariance matrix of estimated contemporary group fixed effects. In this paper, we show that a correction to the variance-covariance matrix of estimated contemporary group fixed effects will produce the exact prediction error variance-covariance matrix averaged by contemporary group for univariate models in the presence of single or multiple fixed effects and one random effect. We demonstrate the correction for a series of models and show that approximations to the prediction error matrix based solely on the variance-covariance matrix of estimated contemporary group fixed effects are inappropriate in certain circumstances. Our method allows for the calculation of a connectedness measure based on the prediction error variance-covariance matrix by calculating only the variance-covariance matrix of estimated fixed effects. Since the number of fixed effects in genetic evaluation is usually orders of magnitudes smaller than the number of random effect levels, the computational requirements for our method should be reduced.

Medial-Frontal Stimulation Enhances Learning in Schizophrenia by Restoring Prediction Error Signaling.

PubMed

Reinhart, Robert M G; Zhu, Julia; Park, Sohee; Woodman, Geoffrey F

2015-09-02

Posterror learning, associated with medial-frontal cortical recruitment in healthy subjects, is compromised in neuropsychiatric disorders. Here we report novel evidence for the mechanisms underlying learning dysfunctions in schizophrenia. We show that, by noninvasively passing direct current through human medial-frontal cortex, we could enhance the event-related potential related to learning from mistakes (i.e., the error-related negativity), a putative index of prediction error signaling in the brain. Following this causal manipulation of brain activity, the patients learned a new task at a rate that was indistinguishable from healthy individuals. Moreover, the severity of delusions interacted with the efficacy of the stimulation to improve learning. Our results demonstrate a causal link between disrupted prediction error signaling and inefficient learning in schizophrenia. These findings also demonstrate the feasibility of nonpharmacological interventions to address cognitive deficits in neuropsychiatric disorders. When there is a difference between what we expect to happen and what we actually experience, our brains generate a prediction error signal, so that we can map stimuli to responses and predict outcomes accurately. Theories of schizophrenia implicate abnormal prediction error signaling in the cognitive deficits of the disorder. Here, we combine noninvasive brain stimulation with large-scale electrophysiological recordings to establish a causal link between faulty prediction error signaling and learning deficits in schizophrenia. We show that it is possible to improve learning rate, as well as the neural signature of prediction error signaling, in patients to a level quantitatively indistinguishable from that of healthy subjects. The results provide mechanistic insight into schizophrenia pathophysiology and suggest a future therapy for this condition. Copyright © 2015 the authors 0270-6474/15/3512232-09$15.00/0.

Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa.

PubMed

DeGuzman, Marisa; Shott, Megan E; Yang, Tony T; Riederer, Justin; Frank, Guido K W

2017-06-01

Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Female adolescents with anorexia nervosa (N=21; mean age, 16.4 years [SD=1.9]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 15.2 years [SD=2.4]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs.

Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa

PubMed Central

DeGuzman, Marisa; Shott, Megan E.; Yang, Tony T.; Riederer, Justin; Frank, Guido K.W.

2017-01-01

Objective Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Method Female adolescents with anorexia nervosa (N=21; mean age, 15.2 years [SD=2.4]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 16.4 years [SD=1.9]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Results Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Conclusions Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs. PMID:28231717

Application of modified extended method in CREAM for safety inspector in coal mines

NASA Astrophysics Data System (ADS)

Wang, Jinhe; Zhang, Xiaohong; Zeng, Jianchao

2018-01-01

Safety inspector often performs duties in circumstances contributes to the oc currence of human failures. Therefore, the paper aims at quantifying human failure pro bability (HFP) of safety inspector during the coal mine operation with cognitive reliabi lity and error analysis method (CREAM). Whereas, some shortcomings of this approa ch that lacking considering the applicability of the common performance condition (C PC), and the subjective of evaluating CPC level which weaken the accuracy of the qua ntitative prediction results. A modified extended method in CREAM which is able to a ddress these difficulties with a CPC framework table is proposed, and the proposed me thodology is demonstrated by the virtue of a coal-mine accident example. The results a re expected to be useful in predicting HFP of safety inspector and contribute to the enh ancement of coal mine safety.

Development of an OSSE Framework for a Global Atmospheric Data Assimilation System

NASA Technical Reports Server (NTRS)

Gelaro, Ronald; Errico, Ronald M.; Prive, N.

2012-01-01

Observing system simulation experiments (OSSEs) are powerful tools for estimating the usefulness of various configurations of envisioned observing systems and data assimilation techniques. Their utility stems from their being conducted in an entirely simulated context, utilizing simulated observations having simulated errors and drawn from a simulation of the earth's environment. Observations are generated by applying physically based algorithms to the simulated state, such as performed during data assimilation or using other appropriate algorithms. Adding realistic instrument plus representativeness errors, including their biases and correlations, can be critical for obtaining realistic assessments of the impact of a proposed observing system or analysis technique. If estimates of the expected accuracy of proposed observations are realistic, then the OSSE can be also used to learn how best to utilize the new information, accelerating its transition to operations once the real data are available. As with any inferences from simulations, however, it is first imperative that some baseline OSSEs are performed and well validated against corresponding results obtained with a real observing system. This talk provides an overview of, and highlights critical issues related to, the development of an OSSE framework for the tropospheric weather prediction component of the NASA GEOS-5 global atmospheric data assimilation system. The framework includes all existing observations having significant impact on short-term forecast skill. Its validity has been carefully assessed using a range of metrics that can be evaluated in both the OSSE and real contexts, including adjoint-based estimates of observation impact. A preliminary application to the Aeolus Doppler wind lidar mission, scheduled for launch by the European Space Agency in 2014, has also been investigated.

Response Surface Modeling Using Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2001-01-01

A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

Effect of correlated observation error on parameters, predictions, and uncertainty

USGS Publications Warehouse

Tiedeman, Claire; Green, Christopher T.

2013-01-01

Correlations among observation errors are typically omitted when calculating observation weights for model calibration by inverse methods. We explore the effects of omitting these correlations on estimates of parameters, predictions, and uncertainties. First, we develop a new analytical expression for the difference in parameter variance estimated with and without error correlations for a simple one-parameter two-observation inverse model. Results indicate that omitting error correlations from both the weight matrix and the variance calculation can either increase or decrease the parameter variance, depending on the values of error correlation (ρ) and the ratio of dimensionless scaled sensitivities (rdss). For small ρ, the difference in variance is always small, but for large ρ, the difference varies widely depending on the sign and magnitude of rdss. Next, we consider a groundwater reactive transport model of denitrification with four parameters and correlated geochemical observation errors that are computed by an error-propagation approach that is new for hydrogeologic studies. We compare parameter estimates, predictions, and uncertainties obtained with and without the error correlations. Omitting the correlations modestly to substantially changes parameter estimates, and causes both increases and decreases of parameter variances, consistent with the analytical expression. Differences in predictions for the models calibrated with and without error correlations can be greater than parameter differences when both are considered relative to their respective confidence intervals. These results indicate that including observation error correlations in weighting for nonlinear regression can have important effects on parameter estimates, predictions, and their respective uncertainties.

Students' Mathematical Work on Absolute Value: Focusing on Conceptions, Errors and Obstacles

ERIC Educational Resources Information Center

Elia, Iliada; Özel, Serkan; Gagatsis, Athanasios; Panaoura, Areti; Özel, Zeynep Ebrar Yetkiner

2016-01-01

This study investigates students' conceptions of absolute value (AV), their performance in various items on AV, their errors in these items and the relationships between students' conceptions and their performance and errors. The Mathematical Working Space (MWS) is used as a framework for studying students' mathematical work on AV and the…

Reward Prediction Errors in Drug Addiction and Parkinson's Disease: from Neurophysiology to Neuroimaging.

PubMed

García-García, Isabel; Zeighami, Yashar; Dagher, Alain

2017-06-01

Surprises are important sources of learning. Cognitive scientists often refer to surprises as "reward prediction errors," a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson's disease. By increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson's disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes. The present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.

Punishing an error improves learning: the influence of punishment magnitude on error-related neural activity and subsequent learning.

PubMed

Hester, Robert; Murphy, Kevin; Brown, Felicity L; Skilleter, Ashley J

2010-11-17

Punishing an error to shape subsequent performance is a major tenet of individual and societal level behavioral interventions. Recent work examining error-related neural activity has identified that the magnitude of activity in the posterior medial frontal cortex (pMFC) is predictive of learning from an error, whereby greater activity in this region predicts adaptive changes in future cognitive performance. It remains unclear how punishment influences error-related neural mechanisms to effect behavior change, particularly in key regions such as pMFC, which previous work has demonstrated to be insensitive to punishment. Using an associative learning task that provided monetary reward and punishment for recall performance, we observed that when recall errors were categorized by subsequent performance--whether the failure to accurately recall a number-location association was corrected at the next presentation of the same trial--the magnitude of error-related pMFC activity predicted future correction. However, the pMFC region was insensitive to the magnitude of punishment an error received and it was the left insula cortex that predicted learning from the most aversive outcomes. These findings add further evidence to the hypothesis that error-related pMFC activity may reflect more than a prediction error in representing the value of an outcome. The novel role identified here for the insular cortex in learning from punishment appears particularly compelling for our understanding of psychiatric and neurologic conditions that feature both insular cortex dysfunction and a diminished capacity for learning from negative feedback or punishment.

Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

NASA Astrophysics Data System (ADS)

Guo, Qianjian; Fan, Shuo; Xu, Rufeng; Cheng, Xiang; Zhao, Guoyong; Yang, Jianguo

2017-05-01

Aiming at the problem of low machining accuracy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA(grey relational analysis) method is introduced into the selection of temperature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN (artificial neural network) model is presented, and ABC(artificial bee colony) algorithm is introduced to train the link weights of ANN, a new ABC-NN(Artificial bee colony-based neural network) modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR (least squares regression), ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 μm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.

A predictability study of Lorenz's 28-variable model as a dynamical system

NASA Technical Reports Server (NTRS)

Krishnamurthy, V.

1993-01-01

The dynamics of error growth in a two-layer nonlinear quasi-geostrophic model has been studied to gain an understanding of the mathematical theory of atmospheric predictability. The growth of random errors of varying initial magnitudes has been studied, and the relation between this classical approach and the concepts of the nonlinear dynamical systems theory has been explored. The local and global growths of random errors have been expressed partly in terms of the properties of an error ellipsoid and the Liapunov exponents determined by linear error dynamics. The local growth of small errors is initially governed by several modes of the evolving error ellipsoid but soon becomes dominated by the longest axis. The average global growth of small errors is exponential with a growth rate consistent with the largest Liapunov exponent. The duration of the exponential growth phase depends on the initial magnitude of the errors. The subsequent large errors undergo a nonlinear growth with a steadily decreasing growth rate and attain saturation that defines the limit of predictability. The degree of chaos and the largest Liapunov exponent show considerable variation with change in the forcing, which implies that the time variation in the external forcing can introduce variable character to the predictability.

Parameter prediction based on Improved Process neural network and ARMA error compensation in Evaporation Process

NASA Astrophysics Data System (ADS)

Qian, Xiaoshan

2018-01-01

The traditional model of evaporation process parameters have continuity and cumulative characteristics of the prediction error larger issues, based on the basis of the process proposed an adaptive particle swarm neural network forecasting method parameters established on the autoregressive moving average (ARMA) error correction procedure compensated prediction model to predict the results of the neural network to improve prediction accuracy. Taking a alumina plant evaporation process to analyze production data validation, and compared with the traditional model, the new model prediction accuracy greatly improved, can be used to predict the dynamic process of evaporation of sodium aluminate solution components.

Prediction-error variance in Bayesian model updating: a comparative study

NASA Astrophysics Data System (ADS)

Asadollahi, Parisa; Li, Jian; Huang, Yong

2017-04-01

In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes' Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model class level produces more robust results especially when the number of measurement is small.

Seasonal to interannual Arctic sea ice predictability in current global climate models

NASA Astrophysics Data System (ADS)

Tietsche, S.; Day, J. J.; Guemas, V.; Hurlin, W. J.; Keeley, S. P. E.; Matei, D.; Msadek, R.; Collins, M.; Hawkins, E.

2014-02-01

We establish the first intermodel comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea ice extent and volume, there is potential predictive skill for lead times of up to 3 years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.

Attention in the predictive mind.

PubMed

Ransom, Madeleine; Fazelpour, Sina; Mole, Christopher

2017-01-01

It has recently become popular to suggest that cognition can be explained as a process of Bayesian prediction error minimization. Some advocates of this view propose that attention should be understood as the optimization of expected precisions in the prediction-error signal (Clark, 2013, 2016; Feldman & Friston, 2010; Hohwy, 2012, 2013). This proposal successfully accounts for several attention-related phenomena. We claim that it cannot account for all of them, since there are certain forms of voluntary attention that it cannot accommodate. We therefore suggest that, although the theory of Bayesian prediction error minimization introduces some powerful tools for the explanation of mental phenomena, its advocates have been wrong to claim that Bayesian prediction error minimization is 'all the brain ever does'. Copyright © 2016 Elsevier Inc. All rights reserved.

The evolution of Crew Resource Management training in commercial aviation

NASA Technical Reports Server (NTRS)

Helmreich, R. L.; Merritt, A. C.; Wilhelm, J. A.

1999-01-01

In this study, we describe changes in the nature of Crew Resource Management (CRM) training in commercial aviation, including its shift from cockpit to crew resource management. Validation of the impact of CRM is discussed. Limitations of CRM, including lack of cross-cultural generality are considered. An overarching framework that stresses error management to increase acceptance of CRM concepts is presented. The error management approach defines behavioral strategies taught in CRM as error countermeasures that are employed to avoid error, to trap errors committed, and to mitigate the consequences of error.

Evaluation and Applications of the Prediction of Intensity Model Error (PRIME) Model

NASA Astrophysics Data System (ADS)

Bhatia, K. T.; Nolan, D. S.; Demaria, M.; Schumacher, A.

2015-12-01

Forecasters and end users of tropical cyclone (TC) intensity forecasts would greatly benefit from a reliable expectation of model error to counteract the lack of consistency in TC intensity forecast performance. As a first step towards producing error predictions to accompany each TC intensity forecast, Bhatia and Nolan (2013) studied the relationship between synoptic parameters, TC attributes, and forecast errors. In this study, we build on previous results of Bhatia and Nolan (2013) by testing the ability of the Prediction of Intensity Model Error (PRIME) model to forecast the absolute error and bias of four leading intensity models available for guidance in the Atlantic basin. PRIME forecasts are independently evaluated at each 12-hour interval from 12 to 120 hours during the 2007-2014 Atlantic hurricane seasons. The absolute error and bias predictions of PRIME are compared to their respective climatologies to determine their skill. In addition to these results, we will present the performance of the operational version of PRIME run during the 2015 hurricane season. PRIME verification results show that it can reliably anticipate situations where particular models excel, and therefore could lead to a more informed protocol for hurricane evacuations and storm preparations. These positive conclusions suggest that PRIME forecasts also have the potential to lower the error in the original intensity forecasts of each model. As a result, two techniques are proposed to develop a post-processing procedure for a multimodel ensemble based on PRIME. The first approach is to inverse-weight models using PRIME absolute error predictions (higher predicted absolute error corresponds to lower weights). The second multimodel ensemble applies PRIME bias predictions to each model's intensity forecast and the mean of the corrected models is evaluated. The forecasts of both of these experimental ensembles are compared to those of the equal-weight ICON ensemble, which currently provides the most reliable forecasts in the Atlantic basin.

Local-search based prediction of medical image registration error

NASA Astrophysics Data System (ADS)

Saygili, Görkem

2018-03-01

Medical image registration is a crucial task in many different medical imaging applications. Hence, considerable amount of work has been published recently that aim to predict the error in a registration without any human effort. If provided, these error predictions can be used as a feedback to the registration algorithm to further improve its performance. Recent methods generally start with extracting image-based and deformation-based features, then apply feature pooling and finally train a Random Forest (RF) regressor to predict the real registration error. Image-based features can be calculated after applying a single registration but provide limited accuracy whereas deformation-based features such as variation of deformation vector field may require up to 20 registrations which is a considerably high time-consuming task. This paper proposes to use extracted features from a local search algorithm as image-based features to estimate the error of a registration. The proposed method comprises a local search algorithm to find corresponding voxels between registered image pairs and based on the amount of shifts and stereo confidence measures, it predicts the amount of registration error in millimetres densely using a RF regressor. Compared to other algorithms in the literature, the proposed algorithm does not require multiple registrations, can be efficiently implemented on a Graphical Processing Unit (GPU) and can still provide highly accurate error predictions in existence of large registration error. Experimental results with real registrations on a public dataset indicate a substantially high accuracy achieved by using features from the local search algorithm.

Engineering Change Management Method Framework in Mechanical Engineering

NASA Astrophysics Data System (ADS)

Stekolschik, Alexander

2016-11-01

Engineering changes make an impact on different process chains in and outside the company, and lead to most error costs and time shifts. In fact, 30 to 50 per cent of development costs result from technical changes. Controlling engineering change processes can help us to avoid errors and risks, and contribute to cost optimization and a shorter time to market. This paper presents a method framework for controlling engineering changes at mechanical engineering companies. The developed classification of engineering changes and accordingly process requirements build the basis for the method framework. The developed method framework comprises two main areas: special data objects managed in different engineering IT tools and process framework. Objects from both areas are building blocks that can be selected to the overall business process based on the engineering process type and change classification. The process framework contains steps for the creation of change objects (both for overall change and for parts), change implementation, and release. Companies can select singleprocess building blocks from the framework, depending on the product development process and change impact. The developed change framework has been implemented at a division (10,000 employees) of a big German mechanical engineering company.

Predictive and Feedback Performance Errors are Signaled in the Simple Spike Discharge of Individual Purkinje Cells

PubMed Central

Popa, Laurentiu S.; Hewitt, Angela L.; Ebner, Timothy J.

2012-01-01

The cerebellum has been implicated in processing motor errors required for online control of movement and motor learning. The dominant view is that Purkinje cell complex spike discharge signals motor errors. This study investigated whether errors are encoded in the simple spike discharge of Purkinje cells in monkeys trained to manually track a pseudo-randomly moving target. Four task error signals were evaluated based on cursor movement relative to target movement. Linear regression analyses based on firing residuals ensured that the modulation with a specific error parameter was independent of the other error parameters and kinematics. The results demonstrate that simple spike firing in lobules IV–VI is significantly correlated with position, distance and directional errors. Independent of the error signals, the same Purkinje cells encode kinematics. The strongest error modulation occurs at feedback timing. However, in 72% of cells at least one of the R2 temporal profiles resulting from regressing firing with individual errors exhibit two peak R2 values. For these bimodal profiles, the first peak is at a negative τ (lead) and a second peak at a positive τ (lag), implying that Purkinje cells encode both prediction and feedback about an error. For the majority of the bimodal profiles, the signs of the regression coefficients or preferred directions reverse at the times of the peaks. The sign reversal results in opposing simple spike modulation for the predictive and feedback components. Dual error representations may provide the signals needed to generate sensory prediction errors used to update a forward internal model. PMID:23115173

The Dopamine Prediction Error: Contributions to Associative Models of Reward Learning

PubMed Central

Nasser, Helen M.; Calu, Donna J.; Schoenbaum, Geoffrey; Sharpe, Melissa J.

2017-01-01

Phasic activity of midbrain dopamine neurons is currently thought to encapsulate the prediction-error signal described in Sutton and Barto’s (1981) model-free reinforcement learning algorithm. This phasic signal is thought to contain information about the quantitative value of reward, which transfers to the reward-predictive cue after learning. This is argued to endow the reward-predictive cue with the value inherent in the reward, motivating behavior toward cues signaling the presence of reward. Yet theoretical and empirical research has implicated prediction-error signaling in learning that extends far beyond a transfer of quantitative value to a reward-predictive cue. Here, we review the research which demonstrates the complexity of how dopaminergic prediction errors facilitate learning. After briefly discussing the literature demonstrating that phasic dopaminergic signals can act in the manner described by Sutton and Barto (1981), we consider how these signals may also influence attentional processing across multiple attentional systems in distinct brain circuits. Then, we discuss how prediction errors encode and promote the development of context-specific associations between cues and rewards. Finally, we consider recent evidence that shows dopaminergic activity contains information about causal relationships between cues and rewards that reflect information garnered from rich associative models of the world that can be adapted in the absence of direct experience. In discussing this research we hope to support the expansion of how dopaminergic prediction errors are thought to contribute to the learning process beyond the traditional concept of transferring quantitative value. PMID:28275359

An MEG signature corresponding to an axiomatic model of reward prediction error.

PubMed

Talmi, Deborah; Fuentemilla, Lluis; Litvak, Vladimir; Duzel, Emrah; Dolan, Raymond J

2012-01-02

Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data. Copyright © 2011 Elsevier Inc. All rights reserved.

Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas

PubMed Central

Kesorn, Kraisak; Ongruk, Phatsavee; Chompoosri, Jakkrawarn; Phumee, Atchara; Thavara, Usavadee; Tawatsin, Apiwat; Siriyasatien, Padet

2015-01-01

Background In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings Areas with high incidence of dengue outbreaks in central Thailand were studied. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Ae. aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. In this research, we applied the SVM with the radial basis function (RBF) kernel to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions The infection rates of the Ae. aegypti female mosquitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. We demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE). PMID:25961289

A Case-Series Test of the Interactive Two-step Model of Lexical Access: Predicting Word Repetition from Picture Naming

PubMed Central

Dell, Gary S.; Martin, Nadine; Schwartz, Myrna F.

2010-01-01

Lexical access in language production, and particularly pathologies of lexical access, are often investigated by examining errors in picture naming and word repetition. In this article, we test a computational approach to lexical access, the two-step interactive model, by examining whether the model can quantitatively predict the repetition-error patterns of 65 aphasic subjects from their naming errors. The model’s characterizations of the subjects’ naming errors were taken from the companion paper to this one (Schwartz, Dell, N. Martin, Gahl & Sobel, 2006), and their repetition was predicted from the model on the assumption that naming involves two error prone steps, word and phonological retrieval, whereas repetition only creates errors in the second of these steps. A version of the model in which lexical-semantic and lexical-phonological connections could be independently lesioned was generally successful in predicting repetition for the aphasics. An analysis of the few cases in which model predictions were inaccurate revealed the role of input phonology in the repetition task. PMID:21085621

Data Structures for Extreme Scale Computing

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

Kahan, Simon

As computing problems of national importance grow, the government meets the increased demand by funding the development of ever larger systems. The overarching goal of the work supported in part by this grant is to increase efficiency of programming and performing computations on these large computing systems. In past work, we have demonstrated that some of these computations once thought to require expensive hardware designs and/or complex, special-purpose programming may be executed efficiently on low-cost commodity cluster computing systems using a general-purpose “latency-tolerant” programming framework. One important developed application of the ideas underlying this framework is graph database technology supportingmore » social network pattern matching used by US intelligence agencies to more quickly identify potential terrorist threats. This database application has been spun out by the Pacific Northwest National Laboratory, a Department of Energy Laboratory, into a commercial start-up, Trovares Inc. We explore an alternative application of the same underlying ideas to a well-studied challenge arising in engineering: solving unstructured sparse linear equations. Solving these equations is key to predicting the behavior of large electronic circuits before they are fabricated. Predicting that behavior ahead of fabrication means that designs can optimized and errors corrected ahead of the expense of manufacture.« less

A pose estimation method for unmanned ground vehicles in GPS denied environments

NASA Astrophysics Data System (ADS)

Tamjidi, Amirhossein; Ye, Cang

2012-06-01

This paper presents a pose estimation method based on the 1-Point RANSAC EKF (Extended Kalman Filter) framework. The method fuses the depth data from a LIDAR and the visual data from a monocular camera to estimate the pose of a Unmanned Ground Vehicle (UGV) in a GPS denied environment. Its estimation framework continuy updates the vehicle's 6D pose state and temporary estimates of the extracted visual features' 3D positions. In contrast to the conventional EKF-SLAM (Simultaneous Localization And Mapping) frameworks, the proposed method discards feature estimates from the extended state vector once they are no longer observed for several steps. As a result, the extended state vector always maintains a reasonable size that is suitable for online calculation. The fusion of laser and visual data is performed both in the feature initialization part of the EKF-SLAM process and in the motion prediction stage. A RANSAC pose calculation procedure is devised to produce pose estimate for the motion model. The proposed method has been successfully tested on the Ford campus's LIDAR-Vision dataset. The results are compared with the ground truth data of the dataset and the estimation error is ~1.9% of the path length.

Real-time geometry-aware augmented reality in minimally invasive surgery.

PubMed

Chen, Long; Tang, Wen; John, Nigel W

2017-10-01

The potential of augmented reality (AR) technology to assist minimally invasive surgery (MIS) lies in its computational performance and accuracy in dealing with challenging MIS scenes. Even with the latest hardware and software technologies, achieving both real-time and accurate augmented information overlay in MIS is still a formidable task. In this Letter, the authors present a novel real-time AR framework for MIS that achieves interactive geometric aware AR in endoscopic surgery with stereo views. The authors' framework tracks the movement of the endoscopic camera and simultaneously reconstructs a dense geometric mesh of the MIS scene. The movement of the camera is predicted by minimising the re-projection error to achieve a fast tracking performance, while the three-dimensional mesh is incrementally built by a dense zero mean normalised cross-correlation stereo-matching method to improve the accuracy of the surface reconstruction. The proposed system does not require any prior template or pre-operative scan and can infer the geometric information intra-operatively in real time. With the geometric information available, the proposed AR framework is able to interactively add annotations, localisation of tumours and vessels, and measurement labelling with greater precision and accuracy compared with the state-of-the-art approaches.

Uncertainty quantification in volumetric Particle Image Velocimetry

NASA Astrophysics Data System (ADS)

Bhattacharya, Sayantan; Charonko, John; Vlachos, Pavlos

2016-11-01

Particle Image Velocimetry (PIV) uncertainty quantification is challenging due to coupled sources of elemental uncertainty and complex data reduction procedures in the measurement chain. Recent developments in this field have led to uncertainty estimation methods for planar PIV. However, no framework exists for three-dimensional volumetric PIV. In volumetric PIV the measurement uncertainty is a function of reconstructed three-dimensional particle location that in turn is very sensitive to the accuracy of the calibration mapping function. Furthermore, the iterative correction to the camera mapping function using triangulated particle locations in space (volumetric self-calibration) has its own associated uncertainty due to image noise and ghost particle reconstructions. Here we first quantify the uncertainty in the triangulated particle position which is a function of particle detection and mapping function uncertainty. The location uncertainty is then combined with the three-dimensional cross-correlation uncertainty that is estimated as an extension of the 2D PIV uncertainty framework. Finally the overall measurement uncertainty is quantified using an uncertainty propagation equation. The framework is tested with both simulated and experimental cases. For the simulated cases the variation of estimated uncertainty with the elemental volumetric PIV error sources are also evaluated. The results show reasonable prediction of standard uncertainty with good coverage.

Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy

PubMed Central

Xu, Yupeng; Yan, Ke; Kim, Jinman; Wang, Xiuying; Li, Changyang; Su, Li; Yu, Suqin; Xu, Xun; Feng, Dagan David

2017-01-01

Worldwide, polypoidal choroidal vasculopathy (PCV) is a common vision-threatening exudative maculopathy, and pigment epithelium detachment (PED) is an important clinical characteristic. Thus, precise and efficient PED segmentation is necessary for PCV clinical diagnosis and treatment. We propose a dual-stage learning framework via deep neural networks (DNN) for automated PED segmentation in PCV patients to avoid issues associated with manual PED segmentation (subjectivity, manual segmentation errors, and high time consumption).The optical coherence tomography scans of fifty patients were quantitatively evaluated with different algorithms and clinicians. Dual-stage DNN outperformed existing PED segmentation methods for all segmentation accuracy parameters, including true positive volume fraction (85.74 ± 8.69%), dice similarity coefficient (85.69 ± 8.08%), positive predictive value (86.02 ± 8.99%) and false positive volume fraction (0.38 ± 0.18%). Dual-stage DNN achieves accurate PED quantitative information, works with multiple types of PEDs and agrees well with manual delineation, suggesting that it is a potential automated assistant for PCV management. PMID:28966847

Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy.

PubMed

Xu, Yupeng; Yan, Ke; Kim, Jinman; Wang, Xiuying; Li, Changyang; Su, Li; Yu, Suqin; Xu, Xun; Feng, Dagan David

2017-09-01

Worldwide, polypoidal choroidal vasculopathy (PCV) is a common vision-threatening exudative maculopathy, and pigment epithelium detachment (PED) is an important clinical characteristic. Thus, precise and efficient PED segmentation is necessary for PCV clinical diagnosis and treatment. We propose a dual-stage learning framework via deep neural networks (DNN) for automated PED segmentation in PCV patients to avoid issues associated with manual PED segmentation (subjectivity, manual segmentation errors, and high time consumption).The optical coherence tomography scans of fifty patients were quantitatively evaluated with different algorithms and clinicians. Dual-stage DNN outperformed existing PED segmentation methods for all segmentation accuracy parameters, including true positive volume fraction (85.74 ± 8.69%), dice similarity coefficient (85.69 ± 8.08%), positive predictive value (86.02 ± 8.99%) and false positive volume fraction (0.38 ± 0.18%). Dual-stage DNN achieves accurate PED quantitative information, works with multiple types of PEDs and agrees well with manual delineation, suggesting that it is a potential automated assistant for PCV management.

Bayesian Approach to the Joint Inversion of Gravity and Magnetic Data, with Application to the Ismenius Area of Mars

NASA Technical Reports Server (NTRS)

Jewell, Jeffrey B.; Raymond, C.; Smrekar, S.; Millbury, C.

2004-01-01

This viewgraph presentation reviews a Bayesian approach to the inversion of gravity and magnetic data with specific application to the Ismenius Area of Mars. Many inverse problems encountered in geophysics and planetary science are well known to be non-unique (i.e. inversion of gravity the density structure of a body). In hopes of reducing the non-uniqueness of solutions, there has been interest in the joint analysis of data. An example is the joint inversion of gravity and magnetic data, with the assumption that the same physical anomalies generate both the observed magnetic and gravitational anomalies. In this talk, we formulate the joint analysis of different types of data in a Bayesian framework and apply the formalism to the inference of the density and remanent magnetization structure for a local region in the Ismenius area of Mars. The Bayesian approach allows prior information or constraints in the solutions to be incorporated in the inversion, with the "best" solutions those whose forward predictions most closely match the data while remaining consistent with assumed constraints. The application of this framework to the inversion of gravity and magnetic data on Mars reveals two typical challenges - the forward predictions of the data have a linear dependence on some of the quantities of interest, and non-linear dependence on others (termed the "linear" and "non-linear" variables, respectively). For observations with Gaussian noise, a Bayesian approach to inversion for "linear" variables reduces to a linear filtering problem, with an explicitly computable "error" matrix. However, for models whose forward predictions have non-linear dependencies, inference is no longer given by such a simple linear problem, and moreover, the uncertainty in the solution is no longer completely specified by a computable "error matrix". It is therefore important to develop methods for sampling from the full Bayesian posterior to provide a complete and statistically consistent picture of model uncertainty, and what has been learned from observations. We will discuss advanced numerical techniques, including Monte Carlo Markov

An Approach to a Comprehensive Test Framework for Analysis and Evaluation of Text Line Segmentation Algorithms

PubMed Central

Brodic, Darko; Milivojevic, Dragan R.; Milivojevic, Zoran N.

2011-01-01

The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures. PMID:22164106

An approach to a comprehensive test framework for analysis and evaluation of text line segmentation algorithms.

PubMed

Brodic, Darko; Milivojevic, Dragan R; Milivojevic, Zoran N

2011-01-01

The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures.

An Investigation of Students' Errors in Logarithms

ERIC Educational Resources Information Center

Ganesan, Raman; Dindyal, Jaguthsing

2014-01-01

In this study we set out to investigate the errors made by students in logarithms. A test with 16 items was administered to 89 Secondary three students (Year 9). The errors made by the students were categorized using four categories from a framework by Movshovitz-Hadar, Zaslavsky, and Inbar (1987). It was found that students in the top third were…

Taking the Error Term of the Factor Model into Account: The Factor Score Predictor Interval

ERIC Educational Resources Information Center

Beauducel, Andre

2013-01-01

The problem of factor score indeterminacy implies that the factor and the error scores cannot be completely disentangled in the factor model. It is therefore proposed to compute Harman's factor score predictor that contains an additive combination of factor and error variance. This additive combination is discussed in the framework of classical…

Suppressing my memories by listening to yours: The effect of socially triggered context-based prediction error on memory.

PubMed

Vlasceanu, Madalina; Drach, Rae; Coman, Alin

2018-05-03

The mind is a prediction machine. In most situations, it has expectations as to what might happen. But when predictions are invalidated by experience (i.e., prediction errors), the memories that generate these predictions are suppressed. Here, we explore the effect of prediction error on listeners' memories following social interaction. We find that listening to a speaker recounting experiences similar to one's own triggers prediction errors on the part of the listener that lead to the suppression of her memories. This effect, we show, is sensitive to a perspective-taking manipulation, such that individuals who are instructed to take the perspective of the speaker experience memory suppression, whereas individuals who undergo a low-perspective-taking manipulation fail to show a mnemonic suppression effect. We discuss the relevance of these findings for our understanding of the bidirectional influences between cognition and social contexts, as well as for the real-world situations that involve memory-based predictions.

Forecasting influenza in Hong Kong with Google search queries and statistical model fusion

PubMed Central

Ramirez Ramirez, L. Leticia; Nezafati, Kusha; Zhang, Qingpeng; Tsui, Kwok-Leung

2017-01-01

Background The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources. Methods Four individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates include Google search queries, meteorological data, and previously recorded offline ILI. To our knowledge, this is the first study that introduces deep learning methodology into surveillance of infectious diseases and investigates its predictive utility. Furthermore, to exploit the strength from each individual forecasting models, we use statistical model fusion, using Bayesian model averaging (BMA), which allows a systematic integration of multiple forecast scenarios. For each model, an adaptive approach is used to capture the recent relationship between ILI and covariates. Results DL with FNN appears to deliver the most competitive predictive performance among the four considered individual models. Combing all four models in a comprehensive BMA framework allows to further improve such predictive evaluation metrics as root mean squared error (RMSE) and mean absolute predictive error (MAPE). Nevertheless, DL with FNN remains the preferred method for predicting locations of influenza peaks. Conclusions The proposed approach can be viewed a feasible alternative to forecast ILI in Hong Kong or other countries where ILI has no constant seasonal trend and influenza data resources are limited. The proposed methodology is easily tractable and computationally efficient. PMID:28464015

EEG oscillatory patterns are associated with error prediction during music performance and are altered in musician's dystonia.

PubMed

Ruiz, María Herrojo; Strübing, Felix; Jabusch, Hans-Christian; Altenmüller, Eckart

2011-04-15

Skilled performance requires the ability to monitor ongoing behavior, detect errors in advance and modify the performance accordingly. The acquisition of fast predictive mechanisms might be possible due to the extensive training characterizing expertise performance. Recent EEG studies on piano performance reported a negative event-related potential (ERP) triggered in the ACC 70 ms before performance errors (pitch errors due to incorrect keypress). This ERP component, termed pre-error related negativity (pre-ERN), was assumed to reflect processes of error detection in advance. However, some questions remained to be addressed: (i) Does the electrophysiological marker prior to errors reflect an error signal itself or is it related instead to the implementation of control mechanisms? (ii) Does the posterior frontomedial cortex (pFMC, including ACC) interact with other brain regions to implement control adjustments following motor prediction of an upcoming error? (iii) Can we gain insight into the electrophysiological correlates of error prediction and control by assessing the local neuronal synchronization and phase interaction among neuronal populations? (iv) Finally, are error detection and control mechanisms defective in pianists with musician's dystonia (MD), a focal task-specific dystonia resulting from dysfunction of the basal ganglia-thalamic-frontal circuits? Consequently, we investigated the EEG oscillatory and phase synchronization correlates of error detection and control during piano performances in healthy pianists and in a group of pianists with MD. In healthy pianists, the main outcomes were increased pre-error theta and beta band oscillations over the pFMC and 13-15 Hz phase synchronization, between the pFMC and the right lateral prefrontal cortex, which predicted corrective mechanisms. In MD patients, the pattern of phase synchronization appeared in a different frequency band (6-8 Hz) and correlated with the severity of the disorder. The present findings shed new light on the neural mechanisms, which might implement motor prediction by means of forward control processes, as they function in healthy pianists and in their altered form in patients with MD. Copyright © 2010 Elsevier Inc. All rights reserved.

Thermal-mechanical behavior of high precision composite mirrors

NASA Technical Reports Server (NTRS)

Kuo, C. P.; Lou, M. C.; Rapp, D.

1993-01-01

Composite mirror panels were designed, constructed, analyzed, and tested in the framework of a NASA precision segmented reflector task. The deformations of the reflector surface during the exposure to space enviroments were predicted using a finite element model. The composite mirror panels have graphite-epoxy or graphite-cyanate facesheets, separated by an aluminum or a composite honeycomb core. It is pointed out that in order to carry out detailed modeling of composite mirrors with high accuracy, it is necessary to have temperature dependent properties of the materials involved and the type and magnitude of manufacturing errors and material nonuniformities. The structural modeling and analysis efforts addressed the impact of key design and materials parameters on the performance of mirrors.

The free-energy principle: a unified brain theory?

PubMed

Friston, Karl

2010-02-01

A free-energy principle has been proposed recently that accounts for action, perception and learning. This Review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories - optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework.

Hyperspectral data compression using a Wiener filter predictor

NASA Astrophysics Data System (ADS)

Villeneuve, Pierre V.; Beaven, Scott G.; Stocker, Alan D.

2013-09-01

The application of compression to hyperspectral image data is a significant technical challenge. A primary bottleneck in disseminating data products to the tactical user community is the limited communication bandwidth between the airborne sensor and the ground station receiver. This report summarizes the newly-developed "Z-Chrome" algorithm for lossless compression of hyperspectral image data. A Wiener filter prediction framework is used as a basis for modeling new image bands from already-encoded bands. The resulting residual errors are then compressed using available state-of-the-art lossless image compression functions. Compression performance is demonstrated using a large number of test data collected over a wide variety of scene content from six different airborne and spaceborne sensors .

Hyperspectral image classification based on local binary patterns and PCANet

NASA Astrophysics Data System (ADS)

Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang

2018-04-01

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

Optimal mapping of terrestrial gamma dose rates using geological parent material and aerogeophysical survey data.

PubMed

Rawlins, B G; Scheib, C; Tyler, A N; Beamish, D

2012-12-01

Regulatory authorities need ways to estimate natural terrestrial gamma radiation dose rates (nGy h⁻¹) across the landscape accurately, to assess its potential deleterious health effects. The primary method for estimating outdoor dose rate is to use an in situ detector supported 1 m above the ground, but such measurements are costly and cannot capture the landscape-scale variation in dose rates which are associated with changes in soil and parent material mineralogy. We investigate the potential for improving estimates of terrestrial gamma dose rates across Northern Ireland (13,542 km²) using measurements from 168 sites and two sources of ancillary data: (i) a map based on a simplified classification of soil parent material, and (ii) dose estimates from a national-scale, airborne radiometric survey. We used the linear mixed modelling framework in which the two ancillary variables were included in separate models as fixed effects, plus a correlation structure which captures the spatially correlated variance component. We used a cross-validation procedure to determine the magnitude of the prediction errors for the different models. We removed a random subset of 10 terrestrial measurements and formed the model from the remainder (n = 158), and then used the model to predict values at the other 10 sites. We repeated this procedure 50 times. The measurements of terrestrial dose vary between 1 and 103 (nGy h⁻¹). The median absolute model prediction errors (nGy h⁻¹) for the three models declined in the following order: no ancillary data (10.8) > simple geological classification (8.3) > airborne radiometric dose (5.4) as a single fixed effect. Estimates of airborne radiometric gamma dose rate can significantly improve the spatial prediction of terrestrial dose rate.

Evaluation of anthropogenic influence in probabilistic forecasting of coastal change

NASA Astrophysics Data System (ADS)

Hapke, C. J.; Wilson, K.; Adams, P. N.

2014-12-01

Prediction of large scale coastal behavior is especially challenging in areas of pervasive human activity. Many coastal zones on the Gulf and Atlantic coasts are moderately to highly modified through the use of soft sediment and hard stabilization techniques. These practices have the potential to alter sediment transport and availability, as well as reshape the beach profile, ultimately transforming the natural evolution of the coastal system. We present the results of a series of probabilistic models, designed to predict the observed geomorphic response to high wave events at Fire Island, New York. The island comprises a variety of land use types, including inhabited communities with modified beaches, where beach nourishment and artificial dune construction (scraping) occur, unmodified zones, and protected national seashore. This variation in land use presents an opportunity for comparison of model accuracy across highly modified and rarely modified stretches of coastline. Eight models with basic and expanded structures were developed, resulting in sixteen models, informed with observational data from Fire Island. The basic model type does not include anthropogenic modification. The expanded model includes records of nourishment and scraping, designed to quantify the improved accuracy when anthropogenic activity is represented. Modification was included as frequency of occurrence divided by the time since the most recent event, to distinguish between recent and historic events. All but one model reported improved predictive accuracy from the basic to expanded form. The addition of nourishment and scraping parameters resulted in a maximum reduction in predictive error of 36%. The seven improved models reported an average 23% reduction in error. These results indicate that it is advantageous to incorporate the human forcing into a coastal hazards probability model framework.

Explicit simulation of ice particle habits in a Numerical Weather Prediction Model

NASA Astrophysics Data System (ADS)

Hashino, Tempei

2007-05-01

This study developed a scheme for explicit simulation of ice particle habits in Numerical Weather Prediction (NWP) Models. The scheme is called Spectral Ice Habit Prediction System (SHIPS), and the goal is to retain growth history of ice particles in the Eulerian dynamics framework. It diagnoses characteristics of ice particles based on a series of particle property variables (PPVs) that reflect history of microphysieal processes and the transport between mass bins and air parcels in space. Therefore, categorization of ice particles typically used in bulk microphysical parameterization and traditional bin models is not necessary, so that errors that stem from the categorization can be avoided. SHIPS predicts polycrystals as well as hexagonal monocrystals based on empirically derived habit frequency and growth rate, and simulates the habit-dependent aggregation and riming processes by use of the stochastic collection equation with predicted PPVs. Idealized two dimensional simulations were performed with SHIPS in a NWP model. The predicted spatial distribution of ice particle habits and types, and evolution of particle size distributions showed good quantitative agreement with observation This comprehensive model of ice particle properties, distributions, and evolution in clouds can be used to better understand problems facing wide range of research disciplines, including microphysics processes, radiative transfer in a cloudy atmosphere, data assimilation, and weather modification.

Distributions in the error space: goal-directed movements described in time and state-space representations.

PubMed

Fisher, Moria E; Huang, Felix C; Wright, Zachary A; Patton, James L

2014-01-01

Manipulation of error feedback has been of great interest to recent studies in motor control and rehabilitation. Typically, motor adaptation is shown as a change in performance with a single scalar metric for each trial, yet such an approach might overlook details about how error evolves through the movement. We believe that statistical distributions of movement error through the extent of the trajectory can reveal unique patterns of adaption and possibly reveal clues to how the motor system processes information about error. This paper describes different possible ordinate domains, focusing on representations in time and state-space, used to quantify reaching errors. We hypothesized that the domain with the lowest amount of variability would lead to a predictive model of reaching error with the highest accuracy. Here we showed that errors represented in a time domain demonstrate the least variance and allow for the highest predictive model of reaching errors. These predictive models will give rise to more specialized methods of robotic feedback and improve previous techniques of error augmentation.

Poster - 49: Assessment of Synchrony respiratory compensation error for CyberKnife liver treatment

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

Liu, Ming; Cygler,

The goal of this work is to quantify respiratory motion compensation errors for liver tumor patients treated by the CyberKnife system with Synchrony tracking, to identify patients with the smallest tracking errors and to eventually help coach patient’s breathing patterns to minimize dose delivery errors. The accuracy of CyberKnife Synchrony respiratory motion compensation was assessed for 37 patients treated for liver lesions by analyzing data from system logfiles. A predictive model is used to modulate the direction of individual beams during dose delivery based on the positions of internally implanted fiducials determined using an orthogonal x-ray imaging system and themore » current location of LED external markers. For each x-ray pair acquired, system logfiles report the prediction error, the difference between the measured and predicted fiducial positions, and the delivery error, which is an estimate of the statistical error in the model overcoming the latency between x-ray acquisition and robotic repositioning. The total error was calculated at the time of each x-ray pair, for the number of treatment fractions and the number of patients, giving the average respiratory motion compensation error in three dimensions. The 99{sup th} percentile for the total radial error is 3.85 mm, with the highest contribution of 2.79 mm in superior/inferior (S/I) direction. The absolute mean compensation error is 1.78 mm radially with a 1.27 mm contribution in the S/I direction. Regions of high total error may provide insight into features predicting groups of patients with larger or smaller total errors.« less

CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

DOE PAGES

Van Weverberg, K.; Morcrette, C. J.; Petch, J.; ...

2018-02-28

Many Numerical Weather Prediction (NWP) and climate models exhibit too warm lower tropospheres near the midlatitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on the net radiation biases in nine model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, water vapor, and aerosols are quantified, using an array of radiation measurement stationsmore » near the Atmospheric Radiation Measurement Southern Great Plains site. Furthermore, an in-depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although nonnegligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud radiative effects dominate in the cloud-related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.« less

CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

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

Van Weverberg, K.; Morcrette, C. J.; Petch, J.

Many Numerical Weather Prediction (NWP) and climate models exhibit too warm lower tropospheres near the midlatitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on the net radiation biases in nine model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, water vapor, and aerosols are quantified, using an array of radiation measurement stationsmore » near the Atmospheric Radiation Measurement Southern Great Plains site. Furthermore, an in-depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although nonnegligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud radiative effects dominate in the cloud-related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.« less

CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

NASA Astrophysics Data System (ADS)

Van Weverberg, K.; Morcrette, C. J.; Petch, J.; Klein, S. A.; Ma, H.-Y.; Zhang, C.; Xie, S.; Tang, Q.; Gustafson, W. I.; Qian, Y.; Berg, L. K.; Liu, Y.; Huang, M.; Ahlgrimm, M.; Forbes, R.; Bazile, E.; Roehrig, R.; Cole, J.; Merryfield, W.; Lee, W.-S.; Cheruy, F.; Mellul, L.; Wang, Y.-C.; Johnson, K.; Thieman, M. M.

2018-04-01

Many Numerical Weather Prediction (NWP) and climate models exhibit too warm lower tropospheres near the midlatitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on the net radiation biases in nine model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, water vapor, and aerosols are quantified, using an array of radiation measurement stations near the Atmospheric Radiation Measurement Southern Great Plains site. Furthermore, an in-depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although nonnegligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud radiative effects dominate in the cloud-related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.

Toward a Framework for Systematic Error Modeling of NASA Spaceborne Radar with NOAA/NSSL Ground Radar-Based National Mosaic QPE

NASA Technical Reports Server (NTRS)

Kirstettier, Pierre-Emmanual; Honh, Y.; Gourley, J. J.; Chen, S.; Flamig, Z.; Zhang, J.; Howard, K.; Schwaller, M.; Petersen, W.; Amitai, E.

2011-01-01

Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving space-born passive and active microwave measurement") for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a three-month data sample in the southern part of US. The primary contribution of this study is the presentation of the detailed steps required to derive trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relics on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors arc revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall rate estimates from other sensors onboard low-earth orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.

Explaining neural signals in human visual cortex with an associative learning model.

PubMed

Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction

NASA Astrophysics Data System (ADS)

Kavetski, Dmitri; Clark, Martyn P.

2010-10-01

Despite the widespread use of conceptual hydrological models in environmental research and operations, they remain frequently implemented using numerically unreliable methods. This paper considers the impact of the time stepping scheme on model analysis (sensitivity analysis, parameter optimization, and Markov chain Monte Carlo-based uncertainty estimation) and prediction. It builds on the companion paper (Clark and Kavetski, 2010), which focused on numerical accuracy, fidelity, and computational efficiency. Empirical and theoretical analysis of eight distinct time stepping schemes for six different hydrological models in 13 diverse basins demonstrates several critical conclusions. (1) Unreliable time stepping schemes, in particular, fixed-step explicit methods, suffer from troublesome numerical artifacts that severely deform the objective function of the model. These deformations are not rare isolated instances but can arise in any model structure, in any catchment, and under common hydroclimatic conditions. (2) Sensitivity analysis can be severely contaminated by numerical errors, often to the extent that it becomes dominated by the sensitivity of truncation errors rather than the model equations. (3) Robust time stepping schemes generally produce "better behaved" objective functions, free of spurious local optima, and with sufficient numerical continuity to permit parameter optimization using efficient quasi Newton methods. When implemented within a multistart framework, modern Newton-type optimizers are robust even when started far from the optima and provide valuable diagnostic insights not directly available from evolutionary global optimizers. (4) Unreliable time stepping schemes lead to inconsistent and biased inferences of the model parameters and internal states. (5) Even when interactions between hydrological parameters and numerical errors provide "the right result for the wrong reason" and the calibrated model performance appears adequate, unreliable time stepping schemes make the model unnecessarily fragile in predictive mode, undermining validation assessments and operational use. Erroneous or misleading conclusions of model analysis and prediction arising from numerical artifacts in hydrological models are intolerable, especially given that robust numerics are accepted as mainstream in other areas of science and engineering. We hope that the vivid empirical findings will encourage the conceptual hydrological community to close its Pandora's box of numerical problems, paving the way for more meaningful model application and interpretation.

An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

USGS Publications Warehouse

Troutman, Brent M.

1982-01-01

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.

Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

PubMed

Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

2018-03-01

Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error <30% and correlation (r) was at least 0.9339 in the same pool of healthy subjects. A 3-concentration-time points limited sampling model predicts the exposure of saroglitazar (ie, AUC 0-t ) within predefined acceptable bias and imprecision limit. Same model was also used to predict AUC 0-∞ . The same limited sampling model was found to predict the exposure of saroglitazar sulfoxide within predefined criteria. This model can find utility during late-phase clinical development of saroglitazar in the patient population. Copyright © 2018 Elsevier HS Journals, Inc. All rights reserved.

Complementary roles for amygdala and periaqueductal gray in temporal-difference fear learning.

PubMed

Cole, Sindy; McNally, Gavan P

2009-01-01

Pavlovian fear conditioning is not a unitary process. At the neurobiological level multiple brain regions and neurotransmitters contribute to fear learning. At the behavioral level many variables contribute to fear learning including the physical salience of the events being learned about, the direction and magnitude of predictive error, and the rate at which these are learned about. These experiments used a serial compound conditioning design to determine the roles of basolateral amygdala (BLA) NMDA receptors and ventrolateral midbrain periaqueductal gray (vlPAG) mu-opioid receptors (MOR) in predictive fear learning. Rats received a three-stage design, which arranged for both positive and negative prediction errors producing bidirectional changes in fear learning within the same subjects during the test stage. Intra-BLA infusion of the NR2B receptor antagonist Ifenprodil prevented all learning. In contrast, intra-vlPAG infusion of the MOR antagonist CTAP enhanced learning in response to positive predictive error but impaired learning in response to negative predictive error--a pattern similar to Hebbian learning and an indication that fear learning had been divorced from predictive error. These findings identify complementary but dissociable roles for amygdala NMDA receptors and vlPAG MOR in temporal-difference predictive fear learning.

A Regularized Volumetric Fusion Framework for Large-Scale 3D Reconstruction

NASA Astrophysics Data System (ADS)

Rajput, Asif; Funk, Eugen; Börner, Anko; Hellwich, Olaf

2018-07-01

Modern computational resources combined with low-cost depth sensing systems have enabled mobile robots to reconstruct 3D models of surrounding environments in real-time. Unfortunately, low-cost depth sensors are prone to produce undesirable estimation noise in depth measurements which result in either depth outliers or introduce surface deformations in the reconstructed model. Conventional 3D fusion frameworks integrate multiple error-prone depth measurements over time to reduce noise effects, therefore additional constraints such as steady sensor movement and high frame-rates are required for high quality 3D models. In this paper we propose a generic 3D fusion framework with controlled regularization parameter which inherently reduces noise at the time of data fusion. This allows the proposed framework to generate high quality 3D models without enforcing additional constraints. Evaluation of the reconstructed 3D models shows that the proposed framework outperforms state of art techniques in terms of both absolute reconstruction error and processing time.

Human factors analysis and classification system-HFACS.

DOT National Transportation Integrated Search

2000-02-01

Human error has been implicated in 70 to 80% of all civil and military aviation accidents. Yet, most accident : reporting systems are not designed around any theoretical framework of human error. As a result, most : accident databases are not conduci...

Accuracy of travel time distribution (TTD) models as affected by TTD complexity, observation errors, and model and tracer selection

USGS Publications Warehouse

Green, Christopher T.; Zhang, Yong; Jurgens, Bryant C.; Starn, J. Jeffrey; Landon, Matthew K.

2014-01-01

Analytical models of the travel time distribution (TTD) from a source area to a sample location are often used to estimate groundwater ages and solute concentration trends. The accuracies of these models are not well known for geologically complex aquifers. In this study, synthetic datasets were used to quantify the accuracy of four analytical TTD models as affected by TTD complexity, observation errors, model selection, and tracer selection. Synthetic TTDs and tracer data were generated from existing numerical models with complex hydrofacies distributions for one public-supply well and 14 monitoring wells in the Central Valley, California. Analytical TTD models were calibrated to synthetic tracer data, and prediction errors were determined for estimates of TTDs and conservative tracer (NO3−) concentrations. Analytical models included a new, scale-dependent dispersivity model (SDM) for two-dimensional transport from the watertable to a well, and three other established analytical models. The relative influence of the error sources (TTD complexity, observation error, model selection, and tracer selection) depended on the type of prediction. Geological complexity gave rise to complex TTDs in monitoring wells that strongly affected errors of the estimated TTDs. However, prediction errors for NO3− and median age depended more on tracer concentration errors. The SDM tended to give the most accurate estimates of the vertical velocity and other predictions, although TTD model selection had minor effects overall. Adding tracers improved predictions if the new tracers had different input histories. Studies using TTD models should focus on the factors that most strongly affect the desired predictions.

Similarities in error processing establish a link between saccade prediction at baseline and adaptation performance.

PubMed

Wong, Aaron L; Shelhamer, Mark

2014-05-01

Adaptive processes are crucial in maintaining the accuracy of body movements and rely on error storage and processing mechanisms. Although classically studied with adaptation paradigms, evidence of these ongoing error-correction mechanisms should also be detectable in other movements. Despite this connection, current adaptation models are challenged when forecasting adaptation ability with measures of baseline behavior. On the other hand, we have previously identified an error-correction process present in a particular form of baseline behavior, the generation of predictive saccades. This process exhibits long-term intertrial correlations that decay gradually (as a power law) and are best characterized with the tools of fractal time series analysis. Since this baseline task and adaptation both involve error storage and processing, we sought to find a link between the intertrial correlations of the error-correction process in predictive saccades and the ability of subjects to alter their saccade amplitudes during an adaptation task. Here we find just such a relationship: the stronger the intertrial correlations during prediction, the more rapid the acquisition of adaptation. This reinforces the links found previously between prediction and adaptation in motor control and suggests that current adaptation models are inadequate to capture the complete dynamics of these error-correction processes. A better understanding of the similarities in error processing between prediction and adaptation might provide the means to forecast adaptation ability with a baseline task. This would have many potential uses in physical therapy and the general design of paradigms of motor adaptation. Copyright © 2014 the American Physiological Society.

Disambiguating ventral striatum fMRI-related bold signal during reward prediction in schizophrenia

PubMed Central

Morris, R W; Vercammen, A; Lenroot, R; Moore, L; Langton, J M; Short, B; Kulkarni, J; Curtis, J; O'Donnell, M; Weickert, C S; Weickert, T W

2012-01-01

Reward detection, surprise detection and prediction-error signaling have all been proposed as roles for the ventral striatum (vStr). Previous neuroimaging studies of striatal function in schizophrenia have found attenuated neural responses to reward-related prediction errors; however, as prediction errors represent a discrepancy in mesolimbic neural activity between expected and actual events, it is critical to examine responses to both expected and unexpected rewards (URs) in conjunction with expected and UR omissions in order to clarify the nature of ventral striatal dysfunction in schizophrenia. In the present study, healthy adults and people with schizophrenia were tested with a reward-related prediction-error task during functional magnetic resonance imaging to determine whether schizophrenia is associated with altered neural responses in the vStr to rewards, surprise prediction errors or all three factors. In healthy adults, we found neural responses in the vStr were correlated more specifically with prediction errors than to surprising events or reward stimuli alone. People with schizophrenia did not display the normal differential activation between expected and URs, which was partially due to exaggerated ventral striatal responses to expected rewards (right vStr) but also included blunted responses to unexpected outcomes (left vStr). This finding shows that neural responses, which typically are elicited by surprise, can also occur to well-predicted events in schizophrenia and identifies aberrant activity in the vStr as a key node of dysfunction in the neural circuitry used to differentiate expected and unexpected feedback in schizophrenia. PMID:21709684

The Incorporation and Initialization of Cloud Water/ice in AN Operational Forecast Model

NASA Astrophysics Data System (ADS)

Zhao, Qingyun

Quantitative precipitation forecasts have been one of the weakest aspects of numerical weather prediction models. Theoretical studies show that the errors in precipitation calculation can arise from three sources: errors in the large-scale forecasts of primary variables, errors in the crude treatment of condensation/evaporation and precipitation processes, and errors in the model initial conditions. A new precipitation parameterization scheme has been developed to investigate the forecast value of improved precipitation physics via the introduction of cloud water and cloud ice into a numerical prediction model. The main feature of this scheme is the explicit calculation of cloud water and cloud ice in both the convective and stratiform precipitation parameterization. This scheme has been applied to the eta model at the National Meteorological Center. Four extensive tests have been performed. The statistical results showed a significant improvement in the model precipitation forecasts. Diagnostic studies suggest that the inclusion of cloud ice is important in transferring water vapor to precipitation and in the enhancement of latent heat release; the latter subsequently affects the vertical motion field significantly. Since three-dimensional cloud data is absent from the analysis/assimilation system for most numerical models, a method has been proposed to incorporate observed precipitation and nephanalysis data into the data assimilation system to obtain the initial cloud field for the eta model. In this scheme, the initial moisture and vertical motion fields are also improved at the same time as cloud initialization. The physical initialization is performed in a dynamical initialization framework that uses the Newtonian dynamical relaxation method to nudge the model's wind and mass fields toward analyses during a 12-hour data assimilation period. Results from a case study showed that a realistic cloud field was produced by this method at the end of the data assimilation period. Precipitation forecasts have been significantly improved as a result of the improved initial cloud, moisture and vertical motion fields.

Simulation Framework to Estimate the Performance of CO2 and O2 Sensing from Space and Airborne Platforms for the ASCENDS Mission Requirements Analysis

NASA Technical Reports Server (NTRS)

Plitau, Denis; Prasad, Narasimha S.

2012-01-01

The Active Sensing of CO2 Emissions over Nights Days and Seasons (ASCENDS) mission recommended by the NRC Decadal Survey has a desired accuracy of 0.3% in carbon dioxide mixing ratio (XCO2) retrievals requiring careful selection and optimization of the instrument parameters. NASA Langley Research Center (LaRC) is investigating 1.57 micron carbon dioxide as well as the 1.26-1.27 micron oxygen bands for our proposed ASCENDS mission requirements investigation. Simulation studies are underway for these bands to select optimum instrument parameters. The simulations are based on a multi-wavelength lidar modeling framework being developed at NASA LaRC to predict the performance of CO2 and O2 sensing from space and airborne platforms. The modeling framework consists of a lidar simulation module and a line-by-line calculation component with interchangeable lineshape routines to test the performance of alternative lineshape models in the simulations. As an option the line-by-line radiative transfer model (LBLRTM) program may also be used for line-by-line calculations. The modeling framework is being used to perform error analysis, establish optimum measurement wavelengths as well as to identify the best lineshape models to be used in CO2 and O2 retrievals. Several additional programs for HITRAN database management and related simulations are planned to be included in the framework. The description of the modeling framework with selected results of the simulation studies for CO2 and O2 sensing is presented in this paper.

Predicting and interpreting identification errors in military vehicle training using multidimensional scaling.

PubMed

Bohil, Corey J; Higgins, Nicholas A; Keebler, Joseph R

2014-01-01

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification. Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.

Impact of SST Anomaly Events over the Kuroshio-Oyashio Extension on the "Summer Prediction Barrier"

NASA Astrophysics Data System (ADS)

Wu, Yujie; Duan, Wansuo

2018-04-01

The "summer prediction barrier" (SPB) of SST anomalies (SSTA) over the Kuroshio-Oyashio Extension (KOE) refers to the phenomenon that prediction errors of KOE-SSTA tend to increase rapidly during boreal summer, resulting in large prediction uncertainties. The fast error growth associated with the SPB occurs in the mature-to-decaying transition phase, which is usually during the August-September-October (ASO) season, of the KOE-SSTA events to be predicted. Thus, the role of KOE-SSTA evolutionary characteristics in the transition phase in inducing the SPB is explored by performing perfect model predictability experiments in a coupled model, indicating that the SSTA events with larger mature-to-decaying transition rates (Category-1) favor a greater possibility of yielding a more significant SPB than those events with smaller transition rates (Category-2). The KOE-SSTA events in Category-1 tend to have more significant anomalous Ekman pumping in their transition phase, resulting in larger prediction errors of vertical oceanic temperature advection associated with the SSTA events. Consequently, Category-1 events possess faster error growth and larger prediction errors. In addition, the anomalous Ekman upwelling (downwelling) in the ASO season also causes SSTA cooling (warming), accelerating the transition rates of warm (cold) KOE-SSTA events. Therefore, the SSTA transition rate and error growth rate are both related with the anomalous Ekman pumping of the SSTA events to be predicted in their transition phase. This may explain why the SSTA events transferring more rapidly from the mature to decaying phase tend to have a greater possibility of yielding a more significant SPB.

Suppression of Striatal Prediction Errors by the Prefrontal Cortex in Placebo Hypoalgesia.

PubMed

Schenk, Lieven A; Sprenger, Christian; Onat, Selim; Colloca, Luana; Büchel, Christian

2017-10-04

Classical learning theories predict extinction after the discontinuation of reinforcement through prediction errors. However, placebo hypoalgesia, although mediated by associative learning, has been shown to be resistant to extinction. We tested the hypothesis that this is mediated by the suppression of prediction error processing through the prefrontal cortex (PFC). We compared pain modulation through treatment cues (placebo hypoalgesia, treatment context) with pain modulation through stimulus intensity cues (stimulus context) during functional magnetic resonance imaging in 48 male and female healthy volunteers. During acquisition, our data show that expectations are correctly learned and that this is associated with prediction error signals in the ventral striatum (VS) in both contexts. However, in the nonreinforced test phase, pain modulation and expectations of pain relief persisted to a larger degree in the treatment context, indicating that the expectations were not correctly updated in the treatment context. Consistently, we observed significantly stronger neural prediction error signals in the VS in the stimulus context compared with the treatment context. A connectivity analysis revealed negative coupling between the anterior PFC and the VS in the treatment context, suggesting that the PFC can suppress the expression of prediction errors in the VS. Consistent with this, a participant's conceptual views and beliefs about treatments influenced the pain modulation only in the treatment context. Our results indicate that in placebo hypoalgesia contextual treatment information engages prefrontal conceptual processes, which can suppress prediction error processing in the VS and lead to reduced updating of treatment expectancies, resulting in less extinction of placebo hypoalgesia. SIGNIFICANCE STATEMENT In aversive and appetitive reinforcement learning, learned effects show extinction when reinforcement is discontinued. This is thought to be mediated by prediction errors (i.e., the difference between expectations and outcome). Although reinforcement learning has been central in explaining placebo hypoalgesia, placebo hypoalgesic effects show little extinction and persist after the discontinuation of reinforcement. Our results support the idea that conceptual treatment beliefs bias the neural processing of expectations in a treatment context compared with a more stimulus-driven processing of expectations with stimulus intensity cues. We provide evidence that this is associated with the suppression of prediction error processing in the ventral striatum by the prefrontal cortex. This provides a neural basis for persisting effects in reinforcement learning and placebo hypoalgesia. Copyright © 2017 the authors 0270-6474/17/379715-09$15.00/0.

Latin hypercube approach to estimate uncertainty in ground water vulnerability

USGS Publications Warehouse

Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.

2007-01-01

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.

Constant pH Molecular Dynamics of Proteins in Explicit Solvent with Proton Tautomerism

PubMed Central

Goh, Garrett B.; Hulbert, Benjamin S.; Zhou, Huiqing; Brooks, Charles L.

2015-01-01

pH is a ubiquitous regulator of biological activity, including protein-folding, protein-protein interactions and enzymatic activity. Existing constant pH molecular dynamics (CPHMD) models that were developed to address questions related to the pH-dependent properties of proteins are largely based on implicit solvent models. However, implicit solvent models are known to underestimate the desolvation energy of buried charged residues, increasing the error associated with predictions that involve internal ionizable residue that are important in processes like hydrogen transport and electron transfer. Furthermore, discrete water and ions cannot be modeled in implicit solvent, which are important in systems like membrane proteins and ion channels. We report on an explicit solvent constant pH molecular dynamics framework based on multi-site λ-dynamics (CPHMDMSλD). In the CPHMDMSλD framework, we performed seamless alchemical transitions between protonation and tautomeric states using multi-site λ-dynamics, and designed novel biasing potentials to ensure that the physical end-states are predominantly sampled. We show that explicit solvent CPHMDMSλD simulations model realistic pH-dependent properties of proteins such as the Hen-Egg White Lysozyme (HEWL), binding domain of 2-oxoglutarate dehydrogenase (BBL) and N-terminal domain of ribosomal L9 (NTL9), and the pKa predictions are in excellent agreement with experimental values, with a RMSE ranging from 0.72 to 0.84 pKa units. With the recent development of the explicit solvent CPHMDMSλD framework for nucleic acids, accurate modeling of pH-dependent properties of both major class of biomolecules – proteins and nucleic acids is now possible. PMID:24375620

Using Fault Trees to Advance Understanding of Diagnostic Errors.

PubMed

Rogith, Deevakar; Iyengar, M Sriram; Singh, Hardeep

2017-11-01

Diagnostic errors annually affect at least 5% of adults in the outpatient setting in the United States. Formal analytic techniques are only infrequently used to understand them, in part because of the complexity of diagnostic processes and clinical work flows involved. In this article, diagnostic errors were modeled using fault tree analysis (FTA), a form of root cause analysis that has been successfully used in other high-complexity, high-risk contexts. How factors contributing to diagnostic errors can be systematically modeled by FTA to inform error understanding and error prevention is demonstrated. A team of three experts reviewed 10 published cases of diagnostic error and constructed fault trees. The fault trees were modeled according to currently available conceptual frameworks characterizing diagnostic error. The 10 trees were then synthesized into a single fault tree to identify common contributing factors and pathways leading to diagnostic error. FTA is a visual, structured, deductive approach that depicts the temporal sequence of events and their interactions in a formal logical hierarchy. The visual FTA enables easier understanding of causative processes and cognitive and system factors, as well as rapid identification of common pathways and interactions in a unified fashion. In addition, it enables calculation of empirical estimates for causative pathways. Thus, fault trees might provide a useful framework for both quantitative and qualitative analysis of diagnostic errors. Future directions include establishing validity and reliability by modeling a wider range of error cases, conducting quantitative evaluations, and undertaking deeper exploration of other FTA capabilities. Copyright © 2017 The Joint Commission. Published by Elsevier Inc. All rights reserved.

Evaluation of Acoustic Doppler Current Profiler measurements of river discharge

USGS Publications Warehouse

Morlock, S.E.

1996-01-01

The standard deviations of the ADCP measurements ranged from approximately 1 to 6 percent and were generally higher than the measurement errors predicted by error-propagation analysis of ADCP instrument performance. These error-prediction methods assume that the largest component of ADCP discharge measurement error is instrument related. The larger standard deviations indicate that substantial portions of measurement error may be attributable to sources unrelated to ADCP electronics or signal processing and are functions of the field environment.

A Framework for Performing Verification and Validation in Reuse Based Software Engineering

NASA Technical Reports Server (NTRS)

Addy, Edward A.

1997-01-01

Verification and Validation (V&V) is currently performed during application development for many systems, especially safety-critical and mission- critical systems. The V&V process is intended to discover errors, especially errors related to critical processing, as early as possible during the development process. The system application provides the context under which the software artifacts are validated. This paper describes a framework that extends V&V from an individual application system to a product line of systems that are developed within an architecture-based software engineering environment. This framework includes the activities of traditional application-level V&V, and extends these activities into domain engineering and into the transition between domain engineering and application engineering. The framework includes descriptions of the types of activities to be performed during each of the life-cycle phases, and provides motivation for the activities.

Real Time 3D Facial Movement Tracking Using a Monocular Camera

PubMed Central

Dong, Yanchao; Wang, Yanming; Yue, Jiguang; Hu, Zhencheng

2016-01-01

The paper proposes a robust framework for 3D facial movement tracking in real time using a monocular camera. It is designed to estimate the 3D face pose and local facial animation such as eyelid movement and mouth movement. The framework firstly utilizes the Discriminative Shape Regression method to locate the facial feature points on the 2D image and fuses the 2D data with a 3D face model using Extended Kalman Filter to yield 3D facial movement information. An alternating optimizing strategy is adopted to fit to different persons automatically. Experiments show that the proposed framework could track the 3D facial movement across various poses and illumination conditions. Given the real face scale the framework could track the eyelid with an error of 1 mm and mouth with an error of 2 mm. The tracking result is reliable for expression analysis or mental state inference. PMID:27463714

Real Time 3D Facial Movement Tracking Using a Monocular Camera.

PubMed

Dong, Yanchao; Wang, Yanming; Yue, Jiguang; Hu, Zhencheng

2016-07-25

The paper proposes a robust framework for 3D facial movement tracking in real time using a monocular camera. It is designed to estimate the 3D face pose and local facial animation such as eyelid movement and mouth movement. The framework firstly utilizes the Discriminative Shape Regression method to locate the facial feature points on the 2D image and fuses the 2D data with a 3D face model using Extended Kalman Filter to yield 3D facial movement information. An alternating optimizing strategy is adopted to fit to different persons automatically. Experiments show that the proposed framework could track the 3D facial movement across various poses and illumination conditions. Given the real face scale the framework could track the eyelid with an error of 1 mm and mouth with an error of 2 mm. The tracking result is reliable for expression analysis or mental state inference.

Embedded Model Error Representation and Propagation in Climate Models

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Thornton, P. E.

2017-12-01

Over the last decade, parametric uncertainty quantification (UQ) methods have reached a level of maturity, while the same can not be said about representation and quantification of structural or model errors. Lack of characterization of model errors, induced by physical assumptions, phenomenological parameterizations or constitutive laws, is a major handicap in predictive science. In particular, e.g. in climate models, significant computational resources are dedicated to model calibration without gaining improvement in predictive skill. Neglecting model errors during calibration/tuning will lead to overconfident and biased model parameters. At the same time, the most advanced methods accounting for model error merely correct output biases, augmenting model outputs with statistical error terms that can potentially violate physical laws, or make the calibrated model ineffective for extrapolative scenarios. This work will overview a principled path for representing and quantifying model errors, as well as propagating them together with the rest of the predictive uncertainty budget, including data noise, parametric uncertainties and surrogate-related errors. Namely, the model error terms will be embedded in select model components rather than as external corrections. Such embedding ensures consistency with physical constraints on model predictions, and renders calibrated model predictions meaningful and robust with respect to model errors. Besides, in the presence of observational data, the approach can effectively differentiate model structural deficiencies from those of data acquisition. The methodology is implemented in UQ Toolkit (www.sandia.gov/uqtoolkit), relying on a host of available forward and inverse UQ tools. We will demonstrate the application of the technique on few application of interest, including ACME Land Model calibration via a wide range of measurements obtained at select sites.

Knowledge acquisition is governed by striatal prediction errors.

PubMed

Pine, Alex; Sadeh, Noa; Ben-Yakov, Aya; Dudai, Yadin; Mendelsohn, Avi

2018-04-26

Discrepancies between expectations and outcomes, or prediction errors, are central to trial-and-error learning based on reward and punishment, and their neurobiological basis is well characterized. It is not known, however, whether the same principles apply to declarative memory systems, such as those supporting semantic learning. Here, we demonstrate with fMRI that the brain parametrically encodes the degree to which new factual information violates expectations based on prior knowledge and beliefs-most prominently in the ventral striatum, and cortical regions supporting declarative memory encoding. These semantic prediction errors determine the extent to which information is incorporated into long-term memory, such that learning is superior when incoming information counters strong incorrect recollections, thereby eliciting large prediction errors. Paradoxically, by the same account, strong accurate recollections are more amenable to being supplanted by misinformation, engendering false memories. These findings highlight a commonality in brain mechanisms and computational rules that govern declarative and nondeclarative learning, traditionally deemed dissociable.

Flight Evaluation of Center-TRACON Automation System Trajectory Prediction Process

NASA Technical Reports Server (NTRS)

Williams, David H.; Green, Steven M.

1998-01-01

Two flight experiments (Phase 1 in October 1992 and Phase 2 in September 1994) were conducted to evaluate the accuracy of the Center-TRACON Automation System (CTAS) trajectory prediction process. The Transport Systems Research Vehicle (TSRV) Boeing 737 based at Langley Research Center flew 57 arrival trajectories that included cruise and descent segments; at the same time, descent clearance advisories from CTAS were followed. Actual trajectories of the airplane were compared with the trajectories predicted by the CTAS trajectory synthesis algorithms and airplane Flight Management System (FMS). Trajectory prediction accuracy was evaluated over several levels of cockpit automation that ranged from a conventional cockpit to performance-based FMS vertical navigation (VNAV). Error sources and their magnitudes were identified and measured from the flight data. The major source of error during these tests was found to be the predicted winds aloft used by CTAS. The most significant effect related to flight guidance was the cross-track and turn-overshoot errors associated with conventional VOR guidance. FMS lateral navigation (LNAV) guidance significantly reduced both the cross-track and turn-overshoot error. Pilot procedures and VNAV guidance were found to significantly reduce the vertical profile errors associated with atmospheric and airplane performance model errors.

Space-Time Error Representation and Estimation in Navier-Stokes Calculations

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2006-01-01

The mathematical framework for a-posteriori error estimation of functionals elucidated by Eriksson et al. [7] and Becker and Rannacher [3] is revisited in a space-time context. Using these theories, a hierarchy of exact and approximate error representation formulas are presented for use in error estimation and mesh adaptivity. Numerical space-time results for simple model problems as well as compressible Navier-Stokes flow at Re = 300 over a 2D circular cylinder are then presented to demonstrate elements of the error representation theory for time-dependent problems.

Error Propagation Analysis in the SAE Architecture Analysis and Design Language (AADL) and the EDICT Tool Framework

NASA Technical Reports Server (NTRS)

LaValley, Brian W.; Little, Phillip D.; Walter, Chris J.

2011-01-01

This report documents the capabilities of the EDICT tools for error modeling and error propagation analysis when operating with models defined in the Architecture Analysis & Design Language (AADL). We discuss our experience using the EDICT error analysis capabilities on a model of the Scalable Processor-Independent Design for Enhanced Reliability (SPIDER) architecture that uses the Reliable Optical Bus (ROBUS). Based on these experiences we draw some initial conclusions about model based design techniques for error modeling and analysis of highly reliable computing architectures.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

Surprise beyond prediction error

PubMed Central

Chumbley, Justin R; Burke, Christopher J; Stephan, Klaas E; Friston, Karl J; Tobler, Philippe N; Fehr, Ernst

2014-01-01

Surprise drives learning. Various neural “prediction error” signals are believed to underpin surprise-based reinforcement learning. Here, we report a surprise signal that reflects reinforcement learning but is neither un/signed reward prediction error (RPE) nor un/signed state prediction error (SPE). To exclude these alternatives, we measured surprise responses in the absence of RPE and accounted for a host of potential SPE confounds. This new surprise signal was evident in ventral striatum, primary sensory cortex, frontal poles, and amygdala. We interpret these findings via a normative model of surprise. PMID:24700400

Homeostatic Regulation of Memory Systems and Adaptive Decisions

PubMed Central

Mizumori, Sheri JY; Jo, Yong Sang

2013-01-01

While it is clear that many brain areas process mnemonic information, understanding how their interactions result in continuously adaptive behaviors has been a challenge. A homeostatic-regulated prediction model of memory is presented that considers the existence of a single memory system that is based on a multilevel coordinated and integrated network (from cells to neural systems) that determines the extent to which events and outcomes occur as predicted. The “multiple memory systems of the brain” have in common output that signals errors in the prediction of events and/or their outcomes, although these signals differ in terms of what the error signal represents (e.g., hippocampus: context prediction errors vs. midbrain/striatum: reward prediction errors). The prefrontal cortex likely plays a pivotal role in the coordination of prediction analysis within and across prediction brain areas. By virtue of its widespread control and influence, and intrinsic working memory mechanisms. Thus, the prefrontal cortex supports the flexible processing needed to generate adaptive behaviors and predict future outcomes. It is proposed that prefrontal cortex continually and automatically produces adaptive responses according to homeostatic regulatory principles: prefrontal cortex may serve as a controller that is intrinsically driven to maintain in prediction areas an experience-dependent firing rate set point that ensures adaptive temporally and spatially resolved neural responses to future prediction errors. This same drive by prefrontal cortex may also restore set point firing rates after deviations (i.e. prediction errors) are detected. In this way, prefrontal cortex contributes to reducing uncertainty in prediction systems. An emergent outcome of this homeostatic view may be the flexible and adaptive control that prefrontal cortex is known to implement (i.e. working memory) in the most challenging of situations. Compromise to any of the prediction circuits should result in rigid and suboptimal decision making and memory as seen in addiction and neurological disease. © 2013 The Authors. Hippocampus Published by Wiley Periodicals, Inc. PMID:23929788

Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination

NASA Astrophysics Data System (ADS)

Li, Weihua; Sankarasubramanian, A.

2012-12-01

Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, "abcd" model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., "abcd" model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs better than all single models and optimal model combination scheme, MM-O, in predicting the monthly flows as well as the flows during wetter months.

Homeostatic regulation of memory systems and adaptive decisions.

PubMed

Mizumori, Sheri J Y; Jo, Yong Sang

2013-11-01

While it is clear that many brain areas process mnemonic information, understanding how their interactions result in continuously adaptive behaviors has been a challenge. A homeostatic-regulated prediction model of memory is presented that considers the existence of a single memory system that is based on a multilevel coordinated and integrated network (from cells to neural systems) that determines the extent to which events and outcomes occur as predicted. The "multiple memory systems of the brain" have in common output that signals errors in the prediction of events and/or their outcomes, although these signals differ in terms of what the error signal represents (e.g., hippocampus: context prediction errors vs. midbrain/striatum: reward prediction errors). The prefrontal cortex likely plays a pivotal role in the coordination of prediction analysis within and across prediction brain areas. By virtue of its widespread control and influence, and intrinsic working memory mechanisms. Thus, the prefrontal cortex supports the flexible processing needed to generate adaptive behaviors and predict future outcomes. It is proposed that prefrontal cortex continually and automatically produces adaptive responses according to homeostatic regulatory principles: prefrontal cortex may serve as a controller that is intrinsically driven to maintain in prediction areas an experience-dependent firing rate set point that ensures adaptive temporally and spatially resolved neural responses to future prediction errors. This same drive by prefrontal cortex may also restore set point firing rates after deviations (i.e. prediction errors) are detected. In this way, prefrontal cortex contributes to reducing uncertainty in prediction systems. An emergent outcome of this homeostatic view may be the flexible and adaptive control that prefrontal cortex is known to implement (i.e. working memory) in the most challenging of situations. Compromise to any of the prediction circuits should result in rigid and suboptimal decision making and memory as seen in addiction and neurological disease. Copyright © 2013 Wiley Periodicals, Inc.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, Martin; Giebel, Gregor; Skov Nielsen, Torben; Hahmann, Andrea; Sørensen, Poul; Madsen, Henrik

2013-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the title "Integrated Wind Power Planning Tool". The goal is to integrate a mesoscale numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. Using the state-of-the-art mesoscale NWP model Weather Research & Forecasting model (WRF) the forecast error is sought quantified in dependence of the time scale involved. This task constitutes a preparative study for later implementation of features accounting for NWP forecast errors in the DTU Wind Energy maintained Corwind code - a long term wind power planning tool. Within the framework of PSO 10464 research related to operational short term wind power prediction will be carried out, including a comparison of forecast quality at different mesoscale NWP model resolutions and development of a statistical wind power prediction tool taking input from WRF. The short term prediction part of the project is carried out in collaboration with ENFOR A/S; a Danish company that specialises in forecasting and optimisation for the energy sector. The integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated short term prediction tool constitutes scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator. The need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2025 from the current 20%.

The accuracy of ab initio calculations without ab initio calculations for charged systems: Kriging predictions of atomistic properties for ions in aqueous solutions

NASA Astrophysics Data System (ADS)

Di Pasquale, Nicodemo; Davie, Stuart J.; Popelier, Paul L. A.

2018-06-01

Using the machine learning method kriging, we predict the energies of atoms in ion-water clusters, consisting of either Cl- or Na+ surrounded by a number of water molecules (i.e., without Na+Cl- interaction). These atomic energies are calculated following the topological energy partitioning method called Interacting Quantum Atoms (IQAs). Kriging predicts atomic properties (in this case IQA energies) by a model that has been trained over a small set of geometries with known property values. The results presented here are part of the development of an advanced type of force field, called FFLUX, which offers quantum mechanical information to molecular dynamics simulations without the limiting computational cost of ab initio calculations. The results reported for the prediction of the IQA components of the energy in the test set exhibit an accuracy of a few kJ/mol, corresponding to an average error of less than 5%, even when a large cluster of water molecules surrounding an ion is considered. Ions represent an important chemical system and this work shows that they can be correctly taken into account in the framework of the FFLUX force field.

SWMF Global Magnetosphere Simulations of January 2005: Geomagnetic Indices and Cross-Polar Cap Potential

NASA Astrophysics Data System (ADS)

Haiducek, John D.; Welling, Daniel T.; Ganushkina, Natalia Y.; Morley, Steven K.; Ozturk, Dogacan Su

2017-12-01

We simulated the entire month of January 2005 using the Space Weather Modeling Framework (SWMF) with observed solar wind data as input. We conducted this simulation with and without an inner magnetosphere model and tested two different grid resolutions. We evaluated the model's accuracy in predicting Kp, SYM-H, AL, and cross-polar cap potential (CPCP). We find that the model does an excellent job of predicting the SYM-H index, with a root-mean-square error (RMSE) of 17-18 nT. Kp is predicted well during storm time conditions but overpredicted during quiet times by a margin of 1 to 1.7 Kp units. AL is predicted reasonably well on average, with an RMSE of 230-270 nT. However, the model reaches the largest negative AL values significantly less often than the observations. The model tended to overpredict CPCP, with RMSE values on the order of 46-48 kV. We found the results to be insensitive to grid resolution, with the exception of the rate of occurrence for strongly negative AL values. The use of the inner magnetosphere component, however, affected results significantly, with all quantities except CPCP improved notably when the inner magnetosphere model was on.

A probabilistic neural network based approach for predicting the output power of wind turbines

NASA Astrophysics Data System (ADS)

Tabatabaei, Sajad

2017-03-01

Finding the authentic predicting tools of eliminating the uncertainty of wind speed forecasts is highly required while wind power sources are strongly penetrating. Recently, traditional predicting models of generating point forecasts have no longer been trustee. Thus, the present paper aims at utilising the concept of prediction intervals (PIs) to assess the uncertainty of wind power generation in power systems. Besides, this paper uses a newly introduced non-parametric approach called lower upper bound estimation (LUBE) to build the PIs since the forecasting errors are unable to be modelled properly by applying distribution probability functions. In the present proposed LUBE method, a PI combination-based fuzzy framework is used to overcome the performance instability of neutral networks (NNs) used in LUBE. In comparison to other methods, this formulation more suitably has satisfied the PI coverage and PI normalised average width (PINAW). Since this non-linear problem has a high complexity, a new heuristic-based optimisation algorithm comprising a novel modification is introduced to solve the aforesaid problems. Based on data sets taken from a wind farm in Australia, the feasibility and satisfying performance of the suggested method have been investigated.

Evaluation of blocking performance in ensemble seasonal integrations

NASA Astrophysics Data System (ADS)

Casado, M. J.; Doblas-Reyes, F. J.; Pastor, M. A.

2003-04-01

EVALUATION OF BLOCKING PERFOMANCE IN ENSEMBLE SEASONAL INTEGRATIONS M. J. Casado (1), F. J. Doblas-Reyes (2), A. Pastor (1) (1) I Instituto Nacional de Meteorología, c/Leonardo Prieto Castro,8,28071 ,Madrid,Spain, mjcasado@inm.es (2) ECMWF, Shinfield Park,RG2 9AX, Reading, UK, f.doblas-reyes@ecmwf.int Climate models have shown a robust inability to reliably predict blocking onset and frequency. This systematic error has been evaluated using multi-model ensemble seasonal integrations carried out in the framework of the Prediction Of climate Variations On Seasonal and interanual Timescales (PROVOST) project and compared to a blocking features assessment of the NCEP re-analyses. The PROVOST GCMs are able to adequately reproduce the spatial NCEP teleconnection patterns over the Northern Hemisphere, being notorious the great spatial correlation coefficient with some of the corresponding NCEP patterns. In spite of that, the different models show a consistent underestimation of blocking frequency which may impact on the ability to predict the seasonal amplitude of the leading modes of variability over the Northern Hemisphere.

Incorporating atmospheric uncertainties into estimates of the detection capability of the IMS infrasound network

NASA Astrophysics Data System (ADS)

Le Pichon, Alexis; Ceranna, Lars; Taillepied, Doriane

2015-04-01

To monitor compliance with the Comprehensive Nuclear-Test-Ban Treaty (CTBT), a dedicated network is being deployed. Multi-year observations recorded by the International Monitoring System (IMS) infrasound network confirm that its detection capability is highly variable in space and time. Today, numerical modeling techniques provide a basis to better understand the role of different factors describing the source and the atmosphere that influence propagation predictions. Previous studies estimated the radiated source energy from remote observations using frequency dependent attenuation relation and state-of-the-art specifications of the stratospheric wind. In order to account for a realistic description of the dynamic structure of the atmosphere, model predictions are further enhanced by wind and temperature error distributions as measured in the framework of the ARISE project (http://arise-project.eu/). In the context of the future verification of the CTBT, these predictions quantify uncertainties in the spatial and temporal variability of the IMS infrasound network performance in higher resolution, and will be helpful for the design and prioritizing maintenance of any arbitrary infrasound monitoring network.

Incorporating atmospheric uncertainties into estimates of the detection capability of the IMS infrasound network

NASA Astrophysics Data System (ADS)

Le Pichon, Alexis; Blanc, Elisabeth; Rüfenacht, Rolf; Kämpfer, Niklaus; Keckhut, Philippe; Hauchecorne, Alain; Ceranna, Lars; Pilger, Christoph; Ross, Ole

2014-05-01

To monitor compliance with the Comprehensive Nuclear-Test-Ban Treaty (CTBT), a dedicated network is being deployed. Multi-year observations recorded by the International Monitoring System (IMS) infrasound network confirm that its detection capability is highly variable in space and time. Today, numerical modeling techniques provide a basis to better understand the role of different factors describing the source and the atmosphere that influence propagation predictions. Previous studies estimated the radiated source energy from remote observations using frequency dependent attenuation relation and state-of-the-art specifications of the stratospheric wind. In order to account for a realistic description of the dynamic structure of the atmosphere, model predictions are further enhanced by wind and temperature error distributions as measured in the framework of the ARISE project (http://arise-project.eu/). In the context of the future verification of the CTBT, these predictions quantify uncertainties in the spatial and temporal variability of the IMS infrasound network performance in higher resolution, and will be helpful for the design and prioritizing maintenance of any arbitrary infrasound monitoring network.

Error begat error: design error analysis and prevention in social infrastructure projects.

PubMed

Love, Peter E D; Lopez, Robert; Edwards, David J; Goh, Yang M

2012-09-01

Design errors contribute significantly to cost and schedule growth in social infrastructure projects and to engineering failures, which can result in accidents and loss of life. Despite considerable research that has addressed their error causation in construction projects they still remain prevalent. This paper identifies the underlying conditions that contribute to design errors in social infrastructure projects (e.g. hospitals, education, law and order type buildings). A systemic model of error causation is propagated and subsequently used to develop a learning framework for design error prevention. The research suggests that a multitude of strategies should be adopted in congruence to prevent design errors from occurring and so ensure that safety and project performance are ameliorated. Copyright © 2011. Published by Elsevier Ltd.

Competition between learned reward and error outcome predictions in anterior cingulate cortex.

PubMed

Alexander, William H; Brown, Joshua W

2010-02-15

The anterior cingulate cortex (ACC) is implicated in performance monitoring and cognitive control. Non-human primate studies of ACC show prominent reward signals, but these are elusive in human studies, which instead show mainly conflict and error effects. Here we demonstrate distinct appetitive and aversive activity in human ACC. The error likelihood hypothesis suggests that ACC activity increases in proportion to the likelihood of an error, and ACC is also sensitive to the consequence magnitude of the predicted error. Previous work further showed that error likelihood effects reach a ceiling as the potential consequences of an error increase, possibly due to reductions in the average reward. We explored this issue by independently manipulating reward magnitude of task responses and error likelihood while controlling for potential error consequences in an Incentive Change Signal Task. The fMRI results ruled out a modulatory effect of expected reward on error likelihood effects in favor of a competition effect between expected reward and error likelihood. Dynamic causal modeling showed that error likelihood and expected reward signals are intrinsic to the ACC rather than received from elsewhere. These findings agree with interpretations of ACC activity as signaling both perceptions of risk and predicted reward. Copyright 2009 Elsevier Inc. All rights reserved.

Bayesian randomized clinical trials: From fixed to adaptive design.

PubMed

Yin, Guosheng; Lam, Chi Kin; Shi, Haolun

2017-08-01

Randomized controlled studies are the gold standard for phase III clinical trials. Using α-spending functions to control the overall type I error rate, group sequential methods are well established and have been dominating phase III studies. Bayesian randomized design, on the other hand, can be viewed as a complement instead of competitive approach to the frequentist methods. For the fixed Bayesian design, the hypothesis testing can be cast in the posterior probability or Bayes factor framework, which has a direct link to the frequentist type I error rate. Bayesian group sequential design relies upon Bayesian decision-theoretic approaches based on backward induction, which is often computationally intensive. Compared with the frequentist approaches, Bayesian methods have several advantages. The posterior predictive probability serves as a useful and convenient tool for trial monitoring, and can be updated at any time as the data accrue during the trial. The Bayesian decision-theoretic framework possesses a direct link to the decision making in the practical setting, and can be modeled more realistically to reflect the actual cost-benefit analysis during the drug development process. Other merits include the possibility of hierarchical modeling and the use of informative priors, which would lead to a more comprehensive utilization of information from both historical and longitudinal data. From fixed to adaptive design, we focus on Bayesian randomized controlled clinical trials and make extensive comparisons with frequentist counterparts through numerical studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Intrinsic Connectivity Provides the Baseline Framework for Variability in Motor Performance: A Multivariate Fusion Analysis of Low- and High-Frequency Resting-State Oscillations and Antisaccade Performance.

PubMed

Jamadar, Sharna D; Egan, Gary F; Calhoun, Vince D; Johnson, Beth; Fielding, Joanne

2016-07-01

Intrinsic brain activity provides the functional framework for the brain's full repertoire of behavioral responses; that is, a common mechanism underlies intrinsic and extrinsic neural activity, with extrinsic activity building upon the underlying baseline intrinsic activity. The generation of a motor movement in response to sensory stimulation is one of the most fundamental functions of the central nervous system. Since saccadic eye movements are among our most stereotyped motor responses, we hypothesized that individual variability in the ability to inhibit a prepotent saccade and make a voluntary antisaccade would be related to individual variability in intrinsic connectivity. Twenty-three individuals completed the antisaccade task and resting-state functional magnetic resonance imaging (fMRI). A multivariate analysis of covariance identified relationships between fMRI oscillations (0.01-0.2 Hz) of resting-state networks determined using high-dimensional independent component analysis and antisaccade performance (latency, error rate). Significant multivariate relationships between antisaccade latency and directional error rate were obtained in independent components across the entire brain. Some of the relationships were obtained in components that overlapped substantially with the task; however, many were obtained in components that showed little overlap with the task. The current results demonstrate that even in the absence of a task, spectral power in regions showing little overlap with task activity predicts an individual's performance on a saccade task.

Dynamics Under Location Uncertainty: Model Derivation, Modified Transport and Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Resseguier, V.; Memin, E.; Chapron, B.; Fox-Kemper, B.

2017-12-01

In order to better observe and predict geophysical flows, ensemble-based data assimilation methods are of high importance. In such methods, an ensemble of random realizations represents the variety of the simulated flow's likely behaviors. For this purpose, randomness needs to be introduced in a suitable way and physically-based stochastic subgrid parametrizations are promising paths. This talk will propose a new kind of such a parametrization referred to as modeling under location uncertainty. The fluid velocity is decomposed into a resolved large-scale component and an aliased small-scale one. The first component is possibly random but time-correlated whereas the second is white-in-time but spatially-correlated and possibly inhomogeneous and anisotropic. With such a velocity, the material derivative of any - possibly active - tracer is modified. Three new terms appear: a correction of the large-scale advection, a multiplicative noise and a possibly heterogeneous and anisotropic diffusion. This parameterization naturally ensures attractive properties such as energy conservation for each realization. Additionally, this stochastic material derivative and the associated Reynolds' transport theorem offer a systematic method to derive stochastic models. In particular, we will discuss the consequences of the Quasi-Geostrophic assumptions in our framework. Depending on the turbulence amount, different models with different physical behaviors are obtained. Under strong turbulence assumptions, a simplified diagnosis of frontolysis and frontogenesis at the surface of the ocean is possible in this framework. A Surface Quasi-Geostrophic (SQG) model with a weaker noise influence has also been simulated. A single realization better represents small scales than a deterministic SQG model at the same resolution. Moreover, an ensemble accurately predicts extreme events, bifurcations as well as the amplitudes and the positions of the simulation errors. Figure 1 highlights this last result and compares it to the strong error underestimation of an ensemble simulated from the deterministic dynamic with random initial conditions.

A Modified MinMax k-Means Algorithm Based on PSO.

PubMed

Wang, Xiaoyan; Bai, Yanping

The MinMax k -means algorithm is widely used to tackle the effect of bad initialization by minimizing the maximum intraclustering errors. Two parameters, including the exponent parameter and memory parameter, are involved in the executive process. Since different parameters have different clustering errors, it is crucial to choose appropriate parameters. In the original algorithm, a practical framework is given. Such framework extends the MinMax k -means to automatically adapt the exponent parameter to the data set. It has been believed that if the maximum exponent parameter has been set, then the programme can reach the lowest intraclustering errors. However, our experiments show that this is not always correct. In this paper, we modified the MinMax k -means algorithm by PSO to determine the proper values of parameters which can subject the algorithm to attain the lowest clustering errors. The proposed clustering method is tested on some favorite data sets in several different initial situations and is compared to the k -means algorithm and the original MinMax k -means algorithm. The experimental results indicate that our proposed algorithm can reach the lowest clustering errors automatically.

Computational approaches to schizophrenia: A perspective on negative symptoms.

PubMed

Deserno, Lorenz; Heinz, Andreas; Schlagenhauf, Florian

2017-08-01

Schizophrenia is a heterogeneous spectrum disorder often associated with detrimental negative symptoms. In recent years, computational approaches to psychiatry have attracted growing attention. Negative symptoms have shown some overlap with general cognitive impairments and were also linked to impaired motivational processing in brain circuits implementing reward prediction. In this review, we outline how computational approaches may help to provide a better understanding of negative symptoms in terms of the potentially underlying behavioural and biological mechanisms. First, we describe the idea that negative symptoms could arise from a failure to represent reward expectations to enable flexible behavioural adaptation. It has been proposed that these impairments arise from a failure to use prediction errors to update expectations. Important previous studies focused on processing of so-called model-free prediction errors where learning is determined by past rewards only. However, learning and decision-making arise from multiple cognitive mechanisms functioning simultaneously, and dissecting them via well-designed tasks in conjunction with computational modelling is a promising avenue. Second, we move on to a proof-of-concept example on how generative models of functional imaging data from a cognitive task enable the identification of subgroups of patients mapping on different levels of negative symptoms. Combining the latter approach with behavioural studies regarding learning and decision-making may allow the identification of key behavioural and biological parameters distinctive for different dimensions of negative symptoms versus a general cognitive impairment. We conclude with an outlook on how this computational framework could, at some point, enrich future clinical studies. Copyright © 2016. Published by Elsevier B.V.

Skeletal Correlates for Body Mass Estimation in Modern and Fossil Flying Birds

PubMed Central

Field, Daniel J.; Lynner, Colton; Brown, Christian; Darroch, Simon A. F.

2013-01-01

Scaling relationships between skeletal dimensions and body mass in extant birds are often used to estimate body mass in fossil crown-group birds, as well as in stem-group avialans. However, useful statistical measurements for constraining the precision and accuracy of fossil mass estimates are rarely provided, which prevents the quantification of robust upper and lower bound body mass estimates for fossils. Here, we generate thirteen body mass correlations and associated measures of statistical robustness using a sample of 863 extant flying birds. By providing robust body mass regressions with upper- and lower-bound prediction intervals for individual skeletal elements, we address the longstanding problem of body mass estimation for highly fragmentary fossil birds. We demonstrate that the most precise proxy for estimating body mass in the overall dataset, measured both as coefficient determination of ordinary least squares regression and percent prediction error, is the maximum diameter of the coracoid’s humeral articulation facet (the glenoid). We further demonstrate that this result is consistent among the majority of investigated avian orders (10 out of 18). As a result, we suggest that, in the majority of cases, this proxy may provide the most accurate estimates of body mass for volant fossil birds. Additionally, by presenting statistical measurements of body mass prediction error for thirteen different body mass regressions, this study provides a much-needed quantitative framework for the accurate estimation of body mass and associated ecological correlates in fossil birds. The application of these regressions will enhance the precision and robustness of many mass-based inferences in future paleornithological studies. PMID:24312392

A Decision Theoretic Approach to Evaluate Radiation Detection Algorithms

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

Nobles, Mallory A.; Sego, Landon H.; Cooley, Scott K.

2013-07-01

There are a variety of sensor systems deployed at U.S. border crossings and ports of entry that scan for illicit nuclear material. In this work, we develop a framework for comparing the performance of detection algorithms that interpret the output of these scans and determine when secondary screening is needed. We optimize each algorithm to minimize its risk, or expected loss. We measure an algorithm’s risk by considering its performance over a sample, the probability distribution of threat sources, and the consequence of detection errors. While it is common to optimize algorithms by fixing one error rate and minimizing another,more » our framework allows one to simultaneously consider multiple types of detection errors. Our framework is flexible and easily adapted to many different assumptions regarding the probability of a vehicle containing illicit material, and the relative consequences of a false positive and false negative errors. Our methods can therefore inform decision makers of the algorithm family and parameter values which best reduce the threat from illicit nuclear material, given their understanding of the environment at any point in time. To illustrate the applicability of our methods, in this paper, we compare the risk from two families of detection algorithms and discuss the policy implications of our results.« less

Comparison of Optimal Design Methods in Inverse Problems

PubMed Central

Banks, H. T.; Holm, Kathleen; Kappel, Franz

2011-01-01

Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher Information Matrix (FIM). A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criteria with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model [13], the standard harmonic oscillator model [13] and a popular glucose regulation model [16, 19, 29]. PMID:21857762

Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania.

PubMed

Mauya, Ernest William; Hansen, Endre Hofstad; Gobakken, Terje; Bollandsås, Ole Martin; Malimbwi, Rogers Ernest; Næsset, Erik

2015-12-01

Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent research in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework. The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R 2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000 m 2 . The variance ratio of field-based estimates relative to model-assisted variance ranged from 1.7 to 7.7. This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory.

Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design

NASA Astrophysics Data System (ADS)

Leube, P. C.; Geiges, A.; Nowak, W.

2012-02-01

Incorporating hydro(geo)logical data, such as head and tracer data, into stochastic models of (subsurface) flow and transport helps to reduce prediction uncertainty. Because of financial limitations for investigation campaigns, information needs toward modeling or prediction goals should be satisfied efficiently and rationally. Optimal design techniques find the best one among a set of investigation strategies. They optimize the expected impact of data on prediction confidence or related objectives prior to data collection. We introduce a new optimal design method, called PreDIA(gnosis) (Preposterior Data Impact Assessor). PreDIA derives the relevant probability distributions and measures of data utility within a fully Bayesian, generalized, flexible, and accurate framework. It extends the bootstrap filter (BF) and related frameworks to optimal design by marginalizing utility measures over the yet unknown data values. PreDIA is a strictly formal information-processing scheme free of linearizations. It works with arbitrary simulation tools, provides full flexibility concerning measurement types (linear, nonlinear, direct, indirect), allows for any desired task-driven formulations, and can account for various sources of uncertainty (e.g., heterogeneity, geostatistical assumptions, boundary conditions, measurement values, model structure uncertainty, a large class of model errors) via Bayesian geostatistics and model averaging. Existing methods fail to simultaneously provide these crucial advantages, which our method buys at relatively higher-computational costs. We demonstrate the applicability and advantages of PreDIA over conventional linearized methods in a synthetic example of subsurface transport. In the example, we show that informative data is often invisible for linearized methods that confuse zero correlation with statistical independence. Hence, PreDIA will often lead to substantially better sampling designs. Finally, we extend our example to specifically highlight the consideration of conceptual model uncertainty.

49 CFR Appendix D to Part 222 - Determining Risk Levels

Code of Federal Regulations, 2011 CFR

2011-10-01

... prediction formulas can be used to derive the following for each crossing: 1. the predicted collisions (PC) 2... for errors such as data entry errors. The final output is the predicted number of collisions (PC). (e... collisions (PC). (f) For the prediction and severity index formulas, please see the following DOT...

Anticipatory synergy adjustments reflect individual performance of feedforward force control.

PubMed

Togo, Shunta; Imamizu, Hiroshi

2016-10-06

We grasp and dexterously manipulate an object through multi-digit synergy. In the framework of the uncontrolled manifold (UCM) hypothesis, multi-digit synergy is defined as the coordinated control mechanism of fingers to stabilize variable important for task success, e.g., total force. Previous studies reported anticipatory synergy adjustments (ASAs) that correspond to a drop of the synergy index before a quick change of the total force. The present study compared ASA's properties with individual performances of feedforward force control to investigate a relationship of those. Subjects performed a total finger force production task that consisted of a phase in which subjects tracked target line with visual information and a phase in which subjects produced total force pulse without visual information. We quantified their multi-digit synergy through UCM analysis and observed significant ASAs before producing total force pulse. The time of the ASA initiation and the magnitude of the drop of the synergy index were significantly correlated with the error of force pulse, but not with the tracking error. Almost all subjects showed a significant increase of the variance that affected the total force. Our study directly showed that ASA reflects the individual performance of feedforward force control independently of target-tracking performance and suggests that the multi-digit synergy was weakened to adjust the multi-digit movements based on a prediction error so as to reduce the future error. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Lock-in amplifier error prediction and correction in frequency sweep measurements.

PubMed

Sonnaillon, Maximiliano Osvaldo; Bonetto, Fabian Jose

2007-01-01

This article proposes an analytical algorithm for predicting errors in lock-in amplifiers (LIAs) working with time-varying reference frequency. Furthermore, a simple method for correcting such errors is presented. The reference frequency can be swept in order to measure the frequency response of a system within a given spectrum. The continuous variation of the reference frequency produces a measurement error that depends on three factors: the sweep speed, the LIA low-pass filters, and the frequency response of the measured system. The proposed error prediction algorithm is based on the final value theorem of the Laplace transform. The correction method uses a double-sweep measurement. A mathematical analysis is presented and validated with computational simulations and experimental measurements.

Predicted pH at the domestic and public supply drinking water depths, Central Valley, California

USGS Publications Warehouse

Rosecrans, Celia Z.; Nolan, Bernard T.; Gronberg, Jo Ann M.

2017-03-08

This scientific investigations map is a product of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project modeling and mapping team. The prediction grids depicted in this map are of continuous pH and are intended to provide an understanding of groundwater-quality conditions at the domestic and public supply drinking water zones in the groundwater of the Central Valley of California. The chemical quality of groundwater and the fate of many contaminants is often influenced by pH in all aquifers. These grids are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to pH. In this work, the median well depth categorized as domestic supply was 30 meters below land surface, and the median well depth categorized as public supply is 100 meters below land surface. Prediction grids were created using prediction modeling methods, specifically boosted regression trees (BRT) with a Gaussian error distribution within a statistical learning framework within the computing framework of R (http://www.r-project.org/). The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. The response variable was measured pH from 1,337 wells and was compiled from two sources: USGS National Water Information System (NWIS) database (all data are publicly available from the USGS: http://waterdata.usgs.gov/ca/nwis/nwis) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water quality data are publicly available from the SWRCB: http://www.waterboards.ca.gov/gama/geotracker_gama.shtml). Only wells with measured pH and well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993–2014 was used. A total of 1,003 wells (training dataset) were used to train the BRT model, and 334 wells (hold-out dataset) were used to validate the prediction model. The training r-squared was 0.70, and the root-mean-square error (RMSE) in standard pH units was 0.26. The hold-out r-squared was 0.43, and RMSE in standard pH units was 0.37. Predictor variables consisting of more than 60 variables from 7 sources were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt and others, 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. In this work, wells were attributed to predictor variable values in ArcGIS using a 500-meter buffer.Faunt, C.C., ed., 2009, Groundwater availability in the Central Valley aquifer, California: U.S. Geological Survey Professional Paper 1776, 225 p., accessed at https://pubs.usgs.gov/pp/1766/.Faunt, C.C., Belitz, K., and Hanson, R.T., 2010, Development of a three-dimensional model of sedimentary texture in valley-fill deposits of Central Valley, California, USA: Hydrogeology Journal, v. 18, no. 3, p. 625–649, https://doi.org/10.1007/s10040-009-0539-7.

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE PAGES

Buitrago, Jaime; Asfour, Shihab

2017-01-01

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

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

Buitrago, Jaime; Asfour, Shihab

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Investigating Experimental Effects within the Framework of Structural Equation Modeling: An Example with Effects on Both Error Scores and Reaction Times

ERIC Educational Resources Information Center

Schweizer, Karl

2008-01-01

Structural equation modeling provides the framework for investigating experimental effects on the basis of variances and covariances in repeated measurements. A special type of confirmatory factor analysis as part of this framework enables the appropriate representation of the experimental effect and the separation of experimental and…

75 FR 63721 - Fisheries of the Northeastern United States; Atlantic Sea Scallop Fishery; Framework Adjustment...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-18

... these errors. DATES: Effective October 18, 2010. FOR FURTHER INFORMATION CONTACT: Emily Bryant, Fishery... incurred prior to Framework 21's effectiveness would be applied to FY 2011 allocations. NMFS received one...) and final rules to implement Framework 21. Classification Pursuant to 5 U.S.C. 553(b)(B), the...

Sampling Errors of SSM/I and TRMM Rainfall Averages: Comparison with Error Estimates from Surface Data and a Sample Model

NASA Technical Reports Server (NTRS)

Bell, Thomas L.; Kundu, Prasun K.; Kummerow, Christian D.; Einaudi, Franco (Technical Monitor)

2000-01-01

Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote-sensing error and, in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that Root-mean-square (RMS) random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain-gauge and radar data. This relationship was examined using satellite SSM/I data obtained over the western equatorial Pacific during TOGA COARE. RMS error inferred directly from SSM/I rainfall estimates was found to be larger than predicted from surface data, and to depend less on local rain rate than was predicted. Preliminary examination of TRMM microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be directly computed from the satellite data.

Prediction of transmission distortion for wireless video communication: analysis.

PubMed

Chen, Zhifeng; Wu, Dapeng

2012-03-01

Transmitting video over wireless is a challenging problem since video may be seriously distorted due to packet errors caused by wireless channels. The capability of predicting transmission distortion (i.e., video distortion caused by packet errors) can assist in designing video encoding and transmission schemes that achieve maximum video quality or minimum end-to-end video distortion. This paper is aimed at deriving formulas for predicting transmission distortion. The contribution of this paper is twofold. First, we identify the governing law that describes how the transmission distortion process evolves over time and analytically derive the transmission distortion formula as a closed-form function of video frame statistics, channel error statistics, and system parameters. Second, we identify, for the first time, two important properties of transmission distortion. The first property is that the clipping noise, which is produced by nonlinear clipping, causes decay of propagated error. The second property is that the correlation between motion-vector concealment error and propagated error is negative and has dominant impact on transmission distortion, compared with other correlations. Due to these two properties and elegant error/distortion decomposition, our formula provides not only more accurate prediction but also lower complexity than the existing methods.

Pharmacogenetic excitation of dorsomedial prefrontal cortex restores fear prediction error.

PubMed

Yau, Joanna Oi-Yue; McNally, Gavan P

2015-01-07

Pavlovian conditioning involves encoding the predictive relationship between a conditioned stimulus (CS) and an unconditioned stimulus, so that synaptic plasticity and learning is instructed by prediction error. Here we used pharmacogenetic techniques to show a causal relation between activity of rat dorsomedial prefrontal cortex (dmPFC) neurons and fear prediction error. We expressed the excitatory hM3Dq designer receptor exclusively activated by a designer drug (DREADD) in dmPFC and isolated actions of prediction error by using an associative blocking design. Rats were trained to fear the visual CS (CSA) in stage I via pairings with footshock. Then in stage II, rats received compound presentations of visual CSA and auditory CS (CSB) with footshock. This prior fear conditioning of CSA reduced the prediction error during stage II to block fear learning to CSB. The group of rats that received AAV-hSYN-eYFP vector that was treated with clozapine-N-oxide (CNO; 3 mg/kg, i.p.) before stage II showed blocking when tested in the absence of CNO the next day. In contrast, the groups that received AAV-hSYN-hM3Dq and AAV-CaMKIIα-hM3Dq that were treated with CNO before stage II training did not show blocking; learning toward CSB was restored. This restoration of prediction error and fear learning was specific to the injection of CNO because groups that received AAV-hSYN-hM3Dq and AAV-CaMKIIα-hM3Dq that were injected with vehicle before stage II training did show blocking. These effects were not attributable to the DREADD manipulation enhancing learning or arousal, increasing fear memory strength or asymptotic levels of fear learning, or altering fear memory retrieval. Together, these results identify a causal role for dmPFC in a signature of adaptive behavior: using the past to predict future danger and learning from errors in these predictions. Copyright © 2015 the authors 0270-6474/15/350074-10$15.00/0.