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.

Errors Affect Hypothetical Intertemporal Food Choice in Women

PubMed Central

Sellitto, Manuela; di Pellegrino, Giuseppe

2014-01-01

Growing evidence suggests that the ability to control behavior is enhanced in contexts in which errors are more frequent. Here we investigated whether pairing desirable food with errors could decrease impulsive choice during hypothetical temporal decisions about food. To this end, healthy women performed a Stop-signal task in which one food cue predicted high-error rate, and another food cue predicted low-error rate. Afterwards, we measured participants’ intertemporal preferences during decisions between smaller-immediate and larger-delayed amounts of food. We expected reduced sensitivity to smaller-immediate amounts of food associated with high-error rate. Moreover, taking into account that deprivational states affect sensitivity for food, we controlled for participants’ hunger. Results showed that pairing food with high-error likelihood decreased temporal discounting. This effect was modulated by hunger, indicating that, the lower the hunger level, the more participants showed reduced impulsive preference for the food previously associated with a high number of errors as compared with the other food. These findings reveal that errors, which are motivationally salient events that recruit cognitive control and drive avoidance learning against error-prone behavior, are effective in reducing impulsive choice for edible outcomes. PMID:25244534

Parental Cognitive Errors Mediate Parental Psychopathology and Ratings of Child Inattention.

PubMed

Haack, Lauren M; Jiang, Yuan; Delucchi, Kevin; Kaiser, Nina; McBurnett, Keith; Hinshaw, Stephen; Pfiffner, Linda

2017-09-01

We investigate the Depression-Distortion Hypothesis in a sample of 199 school-aged children with ADHD-Predominantly Inattentive presentation (ADHD-I) by examining relations and cross-sectional mediational pathways between parental characteristics (i.e., levels of parental depressive and ADHD symptoms) and parental ratings of child problem behavior (inattention, sluggish cognitive tempo, and functional impairment) via parental cognitive errors. Results demonstrated a positive association between parental factors and parental ratings of inattention, as well as a mediational pathway between parental depressive and ADHD symptoms and parental ratings of inattention via parental cognitive errors. Specifically, higher levels of parental depressive and ADHD symptoms predicted higher levels of cognitive errors, which in turn predicted higher parental ratings of inattention. Findings provide evidence for core tenets of the Depression-Distortion Hypothesis, which state that parents with high rates of psychopathology hold negative schemas for their child's behavior and subsequently, report their child's behavior as more severe. © 2016 Family Process Institute.

Impact of an antiretroviral stewardship strategy on medication error rates.

PubMed

Shea, Katherine M; Hobbs, Athena Lv; Shumake, Jason D; Templet, Derek J; Padilla-Tolentino, Eimeira; Mondy, Kristin E

2018-05-02

The impact of an antiretroviral stewardship strategy on medication error rates was evaluated. This single-center, retrospective, comparative cohort study included patients at least 18 years of age infected with human immunodeficiency virus (HIV) who were receiving antiretrovirals and admitted to the hospital. A multicomponent approach was developed and implemented and included modifications to the order-entry and verification system, pharmacist education, and a pharmacist-led antiretroviral therapy checklist. Pharmacists performed prospective audits using the checklist at the time of order verification. To assess the impact of the intervention, a retrospective review was performed before and after implementation to assess antiretroviral errors. Totals of 208 and 24 errors were identified before and after the intervention, respectively, resulting in a significant reduction in the overall error rate ( p < 0.001). In the postintervention group, significantly lower medication error rates were found in both patient admissions containing at least 1 medication error ( p < 0.001) and those with 2 or more errors ( p < 0.001). Significant reductions were also identified in each error type, including incorrect/incomplete medication regimen, incorrect dosing regimen, incorrect renal dose adjustment, incorrect administration, and the presence of a major drug-drug interaction. A regression tree selected ritonavir as the only specific medication that best predicted more errors preintervention ( p < 0.001); however, no antiretrovirals reliably predicted errors postintervention. An antiretroviral stewardship strategy for hospitalized HIV patients including prospective audit by staff pharmacists through use of an antiretroviral medication therapy checklist at the time of order verification decreased error rates. Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

PubMed Central

Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen

2014-01-01

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829

Detecting Signatures of GRACE Sensor Errors in Range-Rate Residuals

NASA Astrophysics Data System (ADS)

Goswami, S.; Flury, J.

2016-12-01

In order to reach the accuracy of the GRACE baseline, predicted earlier from the design simulations, efforts are ongoing since a decade. GRACE error budget is highly dominated by noise from sensors, dealiasing models and modeling errors. GRACE range-rate residuals contain these errors. Thus, their analysis provides an insight to understand the individual contribution to the error budget. Hence, we analyze the range-rate residuals with focus on contribution of sensor errors due to mis-pointing and bad ranging performance in GRACE solutions. For the analysis of pointing errors, we consider two different reprocessed attitude datasets with differences in pointing performance. Then range-rate residuals are computed from these two datasetsrespectively and analysed. We further compare the system noise of four K-and Ka- band frequencies of the two spacecrafts, with range-rate residuals. Strong signatures of mis-pointing errors can be seen in the range-rate residuals. Also, correlation between range frequency noise and range-rate residuals are seen.

A fresh look at the predictors of naming accuracy and errors in Alzheimer's disease.

PubMed

Cuetos, Fernando; Rodríguez-Ferreiro, Javier; Sage, Karen; Ellis, Andrew W

2012-09-01

In recent years, a considerable number of studies have tried to establish which characteristics of objects and their names predict the responses of patients with Alzheimer's disease (AD) in the picture-naming task. The frequency of use of words and their age of acquisition (AoA) have been implicated as two of the most influential variables, with naming being best preserved for objects with high-frequency, early-acquired names. The present study takes a fresh look at the predictors of naming success in Spanish and English AD patients using a range of measures of word frequency and AoA along with visual complexity, imageability, and word length as predictors. Analyses using generalized linear mixed modelling found that naming accuracy was better predicted by AoA ratings taken from older adults than conventional ratings from young adults. Older frequency measures based on written language samples predicted accuracy better than more modern measures based on the frequencies of words in film subtitles. Replacing adult frequency with an estimate of cumulative (lifespan) frequency did not reduce the impact of AoA. Semantic error rates were predicted by both written word frequency and senior AoA while null response errors were only predicted by frequency. Visual complexity, imageability, and word length did not predict naming accuracy or errors. ©2012 The British Psychological Society.

Data Analysis & Statistical Methods for Command File Errors

NASA Technical Reports Server (NTRS)

Meshkat, Leila; Waggoner, Bruce; Bryant, Larry

2014-01-01

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications.

PubMed

Wu, Xiao-Lin; Xu, Jiaqi; Feng, Guofei; Wiggans, George R; Taylor, Jeremy F; He, Jun; Qian, Changsong; Qiu, Jiansheng; Simpson, Barry; Walker, Jeremy; Bauck, Stewart

2016-01-01

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with ≤1,000 SNPs, but required considerably more computing time. Nevertheless, the differences diminished when >5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with ≥3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal.

Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications

PubMed Central

Wu, Xiao-Lin; Xu, Jiaqi; Feng, Guofei; Wiggans, George R.; Taylor, Jeremy F.; He, Jun; Qian, Changsong; Qiu, Jiansheng; Simpson, Barry; Walker, Jeremy; Bauck, Stewart

2016-01-01

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with ≤1,000 SNPs, but required considerably more computing time. Nevertheless, the differences diminished when >5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with ≥3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal. PMID:27583971

The Pandolf equation under-predicts the metabolic rate of contemporary military load carriage.

PubMed

Drain, Jace R; Aisbett, Brad; Lewis, Michael; Billing, Daniel C

2017-11-01

This investigation assessed the accuracy of error of the Pandolf load carriage energy expenditure equation when simulating contemporary military conditions (load distribution, external load and walking speed). Within-participant design. Sixteen male participants completed 10 trials comprised of five walking speeds (2.5, 3.5, 4.5, 5.5 and 6.5km·h -1 ) and two external loads (22.7 and 38.4kg). The Pandolf equation demonstrated poor predictive precision, with a mean bias of 124.9W and -48.7 to 298.5W 95% limits of agreement. Furthermore, the Pandolf equation systematically under-predicted metabolic rate (p<0.05) across the 10 speed-load combinations. Predicted metabolic rate error ranged from 12-33% across all conditions with the 'moderate' walking speeds (i.e. 4.5-5.5km·h -1 ) yielding less prediction error (12-17%) when compared to the slower and faster walking speeds (21-33%). Factors such as mechanical efficiency and load distribution contribute to the impaired predictive accuracy. The authors suggest the Pandolf equation should be applied to military load carriage with caution. Copyright © 2017 Sports Medicine Australia. All rights reserved.

Videopanorama Frame Rate Requirements Derived from Visual Discrimination of Deceleration During Simulated Aircraft Landing

NASA Technical Reports Server (NTRS)

Furnstenau, Norbert; Ellis, Stephen R.

2015-01-01

In order to determine the required visual frame rate (FR) for minimizing prediction errors with out-the-window video displays at remote/virtual airport towers, thirteen active air traffic controllers viewed high dynamic fidelity simulations of landing aircraft and decided whether aircraft would stop as if to be able to make a turnoff or whether a runway excursion would be expected. The viewing conditions and simulation dynamics replicated visual rates and environments of transport aircraft landing at small commercial airports. The required frame rate was estimated using Bayes inference on prediction errors by linear FRextrapolation of event probabilities conditional on predictions (stop, no-stop). Furthermore estimates were obtained from exponential model fits to the parametric and non-parametric perceptual discriminabilities d' and A (average area under ROC-curves) as dependent on FR. Decision errors are biased towards preference of overshoot and appear due to illusionary increase in speed at low frames rates. Both Bayes and A - extrapolations yield a framerate requirement of 35 < FRmin < 40 Hz. When comparing with published results [12] on shooter game scores the model based d'(FR)-extrapolation exhibits the best agreement and indicates even higher FRmin > 40 Hz for minimizing decision errors. Definitive recommendations require further experiments with FR > 30 Hz.

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.

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

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.

Optimizing the learning rate for adaptive estimation of neural encoding models

PubMed Central

2018-01-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains. PMID:29813069

Optimizing the learning rate for adaptive estimation of neural encoding models.

PubMed

Hsieh, Han-Lin; Shanechi, Maryam M

2018-05-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.

Prediction of error rates in dose-imprinted memories on board CRRES by two different methods. [Combined Release and Radiation Effects Satellite

NASA Technical Reports Server (NTRS)

Brucker, G. J.; Stassinopoulos, E. G.

1991-01-01

An analysis of the expected space radiation effects on the single event upset (SEU) properties of CMOS/bulk memories onboard the Combined Release and Radiation Effects Satellite (CRRES) is presented. Dose-imprint data from ground test irradiations of identical devices are applied to the predictions of cosmic-ray-induced space upset rates in the memories onboard the spacecraft. The calculations take into account the effect of total dose on the SEU sensitivity of the devices as the dose accumulates in orbit. Estimates of error rates, which involved an arbitrary selection of a single pair of threshold linear energy transfer (LET) and asymptotic cross-section values, were compared to the results of an integration over the cross-section curves versus LET. The integration gave lower upset rates than the use of the selected values of the SEU parameters. Since the integration approach is more accurate and eliminates the need for an arbitrary definition of threshold LET and asymptotic cross section, it is recommended for all error rate predictions where experimental sigma-versus-LET curves are available.

Is ozone model bias driven by errors in cloud predictions? A quantitative assessment using satellite cloud retrievals in WRF-Chem

NASA Astrophysics Data System (ADS)

Ryu, Y. H.; Hodzic, A.; Barré, J.; Descombes, G.; Minnis, P.

2017-12-01

Clouds play a key role in radiation and hence O3 photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much of the bias in O3 predictions is caused by inaccurate cloud predictions. This study quantifies the errors in surface O3 predictions associated with clouds in summertime over CONUS using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Cloud fields used for photochemistry are corrected based on satellite cloud retrievals in sensitivity simulations. It is found that the WRF-Chem model is able to detect about 60% of clouds in the right locations and generally underpredicts cloud optical depths. The errors in hourly O3 due to the errors in cloud predictions can be up to 60 ppb. On average in summertime over CONUS, the errors in 8-h average O3 of 1-6 ppb are found to be attributable to those in cloud predictions under cloudy sky conditions. The contribution of changes in photolysis rates due to clouds is found to be larger ( 80 % on average) than that of light-dependent BVOC emissions. The effects of cloud corrections on O­3 are about 2 times larger in VOC-limited than NOx-limited regimes, suggesting that the benefits of accurate cloud predictions would be greater in VOC-limited than NOx-limited regimes.

Addressing Angular Single-Event Effects in the Estimation of On-Orbit Error Rates

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

Lee, David S.; Swift, Gary M.; Wirthlin, Michael J.

2015-12-01

Our study describes complications introduced by angular direct ionization events on space error rate predictions. In particular, prevalence of multiple-cell upsets and a breakdown in the application of effective linear energy transfer in modern-scale devices can skew error rates approximated from currently available estimation models. Moreover, this paper highlights the importance of angular testing and proposes a methodology to extend existing error estimation tools to properly consider angular strikes in modern-scale devices. Finally, these techniques are illustrated with test data provided from a modern 28 nm SRAM-based device.

ADEPT, a dynamic next generation sequencing data error-detection program with trimming

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

Feng, Shihai; Lo, Chien-Chi; Li, Po-E

Illumina is the most widely used next generation sequencing technology and produces millions of short reads that contain errors. These sequencing errors constitute a major problem in applications such as de novo genome assembly, metagenomics analysis and single nucleotide polymorphism discovery. In this study, we present ADEPT, a dynamic error detection method, based on the quality scores of each nucleotide and its neighboring nucleotides, together with their positions within the read and compares this to the position-specific quality score distribution of all bases within the sequencing run. This method greatly improves upon other available methods in terms of the truemore » positive rate of error discovery without affecting the false positive rate, particularly within the middle of reads. We conclude that ADEPT is the only tool to date that dynamically assesses errors within reads by comparing position-specific and neighboring base quality scores with the distribution of quality scores for the dataset being analyzed. The result is a method that is less prone to position-dependent under-prediction, which is one of the most prominent issues in error prediction. The outcome is that ADEPT improves upon prior efforts in identifying true errors, primarily within the middle of reads, while reducing the false positive rate.« less

ADEPT, a dynamic next generation sequencing data error-detection program with trimming

DOE PAGES

Feng, Shihai; Lo, Chien-Chi; Li, Po-E; ...

2016-02-29

Illumina is the most widely used next generation sequencing technology and produces millions of short reads that contain errors. These sequencing errors constitute a major problem in applications such as de novo genome assembly, metagenomics analysis and single nucleotide polymorphism discovery. In this study, we present ADEPT, a dynamic error detection method, based on the quality scores of each nucleotide and its neighboring nucleotides, together with their positions within the read and compares this to the position-specific quality score distribution of all bases within the sequencing run. This method greatly improves upon other available methods in terms of the truemore » positive rate of error discovery without affecting the false positive rate, particularly within the middle of reads. We conclude that ADEPT is the only tool to date that dynamically assesses errors within reads by comparing position-specific and neighboring base quality scores with the distribution of quality scores for the dataset being analyzed. The result is a method that is less prone to position-dependent under-prediction, which is one of the most prominent issues in error prediction. The outcome is that ADEPT improves upon prior efforts in identifying true errors, primarily within the middle of reads, while reducing the false positive rate.« less

Error rate information in attention allocation pilot models

NASA Technical Reports Server (NTRS)

Faulkner, W. H.; Onstott, E. D.

1977-01-01

The Northrop urgency decision pilot model was used in a command tracking task to compare the optimized performance of multiaxis attention allocation pilot models whose urgency functions were (1) based on tracking error alone, and (2) based on both tracking error and error rate. A matrix of system dynamics and command inputs was employed, to create both symmetric and asymmetric two axis compensatory tracking tasks. All tasks were single loop on each axis. Analysis showed that a model that allocates control attention through nonlinear urgency functions using only error information could not achieve performance of the full model whose attention shifting algorithm included both error and error rate terms. Subsequent to this analysis, tracking performance predictions for the full model were verified by piloted flight simulation. Complete model and simulation data are presented.

Rapid and accurate prediction of degradant formation rates in pharmaceutical formulations using high-performance liquid chromatography-mass spectrometry.

PubMed

Darrington, Richard T; Jiao, Jim

2004-04-01

Rapid and accurate stability prediction is essential to pharmaceutical formulation development. Commonly used stability prediction methods include monitoring parent drug loss at intended storage conditions or initial rate determination of degradants under accelerated conditions. Monitoring parent drug loss at the intended storage condition does not provide a rapid and accurate stability assessment because often <0.5% drug loss is all that can be observed in a realistic time frame, while the accelerated initial rate method in conjunction with extrapolation of rate constants using the Arrhenius or Eyring equations often introduces large errors in shelf-life prediction. In this study, the shelf life prediction of a model pharmaceutical preparation utilizing sensitive high-performance liquid chromatography-mass spectrometry (LC/MS) to directly quantitate degradant formation rates at the intended storage condition is proposed. This method was compared to traditional shelf life prediction approaches in terms of time required to predict shelf life and associated error in shelf life estimation. Results demonstrated that the proposed LC/MS method using initial rates analysis provided significantly improved confidence intervals for the predicted shelf life and required less overall time and effort to obtain the stability estimation compared to the other methods evaluated. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review.

PubMed

Frankenfield, David; Roth-Yousey, Lori; Compher, Charlene

2005-05-01

An assessment of energy needs is a necessary component in the development and evaluation of a nutrition care plan. The metabolic rate can be measured or estimated by equations, but estimation is by far the more common method. However, predictive equations might generate errors large enough to impact outcome. Therefore, a systematic review of the literature was undertaken to document the accuracy of predictive equations preliminary to deciding on the imperative to measure metabolic rate. As part of a larger project to determine the role of indirect calorimetry in clinical practice, an evidence team identified published articles that examined the validity of various predictive equations for resting metabolic rate (RMR) in nonobese and obese people and also in individuals of various ethnic and age groups. Articles were accepted based on defined criteria and abstracted using evidence analysis tools developed by the American Dietetic Association. Because these equations are applied by dietetics practitioners to individuals, a key inclusion criterion was research reports of individual data. The evidence was systematically evaluated, and a conclusion statement and grade were developed. Four prediction equations were identified as the most commonly used in clinical practice (Harris-Benedict, Mifflin-St Jeor, Owen, and World Health Organization/Food and Agriculture Organization/United Nations University [WHO/FAO/UNU]). Of these equations, the Mifflin-St Jeor equation was the most reliable, predicting RMR within 10% of measured in more nonobese and obese individuals than any other equation, and it also had the narrowest error range. No validation work concentrating on individual errors was found for the WHO/FAO/UNU equation. Older adults and US-residing ethnic minorities were underrepresented both in the development of predictive equations and in validation studies. The Mifflin-St Jeor equation is more likely than the other equations tested to estimate RMR to within 10% of that measured, but noteworthy errors and limitations exist when it is applied to individuals and possibly when it is generalized to certain age and ethnic groups. RMR estimation errors would be eliminated by valid measurement of RMR with indirect calorimetry, using an evidence-based protocol to minimize measurement error. The Expert Panel advises clinical judgment regarding when to accept estimated RMR using predictive equations in any given individual. Indirect calorimetry may be an important tool when, in the judgment of the clinician, the predictive methods fail an individual in a clinically relevant way. For members of groups that are greatly underrepresented by existing validation studies of predictive equations, a high level of suspicion regarding the accuracy of the equations is warranted.

A method of predicting flow rates required to achieve anti-icing performance with a porous leading edge ice protection system

NASA Technical Reports Server (NTRS)

Kohlman, D. L.; Albright, A. E.

1983-01-01

An analytical method was developed for predicting minimum flow rates required to provide anti-ice protection with a porous leading edge fluid ice protection system. The predicted flow rates compare with an average error of less than 10 percent to six experimentally determined flow rates from tests in the NASA Icing Research Tunnel on a general aviation wing section.

Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction

NASA Astrophysics Data System (ADS)

Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.

2012-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. DEP's "West of Hudson" reservoirs located in the Catskill Mountains are unfiltered per a renewable filtration avoidance determination granted by the EPA. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. A logical strategy for turbidity control is to temporarily remove the turbid reservoirs from service. While effective in limiting delivery of turbid water and reducing the need for in-reservoir alum flocculation, this strategy runs the risk of negatively impacting water supply reliability. Thus, it is advantageous for DEP to understand how long a particular turbidity event will affect their system. In order to understand the duration, intensity and total load of a turbidity event, predictions of future in-stream turbidity values are important. Traditionally, turbidity predictions have been carried out by applying streamflow observations/forecasts to a flow-turbidity rating curve. However, predictions from rating curves are often inaccurate due to inter- and intra-event variability in flow-turbidity relationships. Predictions can be improved by applying an autoregressive moving average (ARMA) time series model in combination with a traditional rating curve. Since 2003, DEP and the Upstate Freshwater Institute have compiled a relatively consistent set of 15-minute turbidity observations at various locations on Esopus Creek above Ashokan Reservoir. Using daily averages of this data and streamflow observations at nearby USGS gauges, flow-turbidity rating curves were developed via linear regression. Time series analysis revealed that the linear regression residuals may be represented using an ARMA(1,2) process. Based on this information, flow-turbidity regressions with ARMA(1,2) errors were fit to the observations. Preliminary model validation exercises at a 30-day forecast horizon show that the ARMA error models generally improve the predictive skill of the linear regression rating curves. Skill seems to vary based on the ambient hydrologic conditions at the onset of the forecast. For example, ARMA error model forecasts issued before a high flow/turbidity event do not show significant improvements over the rating curve approach. However, ARMA error model forecasts issued during the "falling limb" of the hydrograph are significantly more accurate than rating curves for both single day and accumulated event predictions. In order to assist in reservoir operations decisions associated with turbidity events and general water supply reliability, DEP has initiated design of an Operations Support Tool (OST). OST integrates a reservoir operations model with 2D hydrodynamic water quality models and a database compiling near-real-time data sources and hydrologic forecasts. Currently, OST uses conventional flow-turbidity rating curves and hydrologic forecasts for predictive turbidity inputs. Given the improvements in predictive skill over traditional rating curves, the ARMA error models are currently being evaluated as an addition to DEP's Operations Support Tool.

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

Tropical forecasting - Predictability perspective

NASA Technical Reports Server (NTRS)

Shukla, J.

1989-01-01

Results are presented of classical predictability studies and forecast experiments with observed initial conditions to show the nature of initial error growth and final error equilibration for the tropics and midlatitudes, separately. It is found that the theoretical upper limit of tropical circulation predictability is far less than for midlatitudes. The error growth for a complete general circulation model is compared to a dry version of the same model in which there is no prognostic equation for moisture, and diabatic heat sources are prescribed. It is found that the growth rate of synoptic-scale errors for the dry model is significantly smaller than for the moist model, suggesting that the interactions between dynamics and moist processes are among the important causes of atmospheric flow predictability degradation. Results are then presented of numerical experiments showing that correct specification of the slowly varying boundary condition of SST produces significant improvement in the prediction of time-averaged circulation and rainfall over the tropics.

Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems.

PubMed

Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

2013-01-01

An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. THE ANFIS AND ANN MODELS WERE COMPARED IN TERMS OF SIX STATISTICAL INDICES CALCULATED BY COMPARING THEIR PREDICTION RESULTS WITH ACTUAL DATA: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R (2)). Graphical plots were also used for model comparison. The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.

Leuconostoc Mesenteroides Growth in Food Products: Prediction and Sensitivity Analysis by Adaptive-Network-Based Fuzzy Inference Systems

PubMed Central

Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

2013-01-01

Background An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. Methods The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R 2). Graphical plots were also used for model comparison. Conclusions The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. PMID:23705023

Design and analysis of multihypothesis motion-compensated prediction (MHMCP) codec for error-resilient visual communications

NASA Astrophysics Data System (ADS)

Kung, Wei-Ying; Kim, Chang-Su; Kuo, C.-C. Jay

2004-10-01

A multi-hypothesis motion compensated prediction (MHMCP) scheme, which predicts a block from a weighted superposition of more than one reference blocks in the frame buffer, is proposed and analyzed for error resilient visual communication in this research. By combining these reference blocks effectively, MHMCP can enhance the error resilient capability of compressed video as well as achieve a coding gain. In particular, we investigate the error propagation effect in the MHMCP coder and analyze the rate-distortion performance in terms of the hypothesis number and hypothesis coefficients. It is shown that MHMCP suppresses the short-term effect of error propagation more effectively than the intra refreshing scheme. Simulation results are given to confirm the analysis. Finally, several design principles for the MHMCP coder are derived based on the analytical and experimental results.

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.

Arima model and exponential smoothing method: A comparison

NASA Astrophysics Data System (ADS)

Wan Ahmad, Wan Kamarul Ariffin; Ahmad, Sabri

2013-04-01

This study shows the comparison between Autoregressive Moving Average (ARIMA) model and Exponential Smoothing Method in making a prediction. The comparison is focused on the ability of both methods in making the forecasts with the different number of data sources and the different length of forecasting period. For this purpose, the data from The Price of Crude Palm Oil (RM/tonne), Exchange Rates of Ringgit Malaysia (RM) in comparison to Great Britain Pound (GBP) and also The Price of SMR 20 Rubber Type (cents/kg) with three different time series are used in the comparison process. Then, forecasting accuracy of each model is measured by examinethe prediction error that producedby using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute deviation (MAD). The study shows that the ARIMA model can produce a better prediction for the long-term forecasting with limited data sources, butcannot produce a better prediction for time series with a narrow range of one point to another as in the time series for Exchange Rates. On the contrary, Exponential Smoothing Method can produce a better forecasting for Exchange Rates that has a narrow range of one point to another for its time series, while itcannot produce a better prediction for a longer forecasting period.

Models for H₃ receptor antagonist activity of sulfonylurea derivatives.

PubMed

Khatri, Naveen; Madan, A K

2014-03-01

The histamine H₃ receptor has been perceived as an auspicious target for the treatment of various central and peripheral nervous system diseases. In present study, a wide variety of 60 2D and 3D molecular descriptors (MDs) were successfully utilized for the development of models for the prediction of antagonist activity of sulfonylurea derivatives for histamine H₃ receptors. Models were developed through decision tree (DT), random forest (RF) and moving average analysis (MAA). Dragon software version 6.0.28 was employed for calculation of values of diverse MDs of each analogue involved in the data set. The DT classified and correctly predicted the input data with an impressive non-error rate of 94% in the training set and 82.5% during cross validation. RF correctly classified the analogues into active and inactive with a non-error rate of 79.3%. The MAA based models predicted the antagonist histamine H₃ receptor activity with non-error rate up to 90%. Active ranges of the proposed MAA based models not only exhibited high potency but also showed improved safety as indicated by relatively high values of selectivity index. The statistical significance of the models was assessed through sensitivity, specificity, non-error rate, Matthew's correlation coefficient and intercorrelation analysis. Proposed models offer vast potential for providing lead structures for development of potent but safe H₃ receptor antagonist sulfonylurea derivatives. Copyright © 2013 Elsevier Inc. All rights reserved.

Modeling and predicting historical volatility in exchange rate markets

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2017-04-01

Volatility modeling and forecasting of currency exchange rate is an important task in several business risk management tasks; including treasury risk management, derivatives pricing, and portfolio risk evaluation. The purpose of this study is to present a simple and effective approach for predicting historical volatility of currency exchange rate. The approach is based on a limited set of technical indicators as inputs to the artificial neural networks (ANN). To show the effectiveness of the proposed approach, it was applied to forecast US/Canada and US/Euro exchange rates volatilities. The forecasting results show that our simple approach outperformed the conventional GARCH and EGARCH with different distribution assumptions, and also the hybrid GARCH and EGARCH with ANN in terms of mean absolute error, mean of squared errors, and Theil's inequality coefficient. Because of the simplicity and effectiveness of the approach, it is promising for US currency volatility prediction tasks.

Computational modeling of bedform evolution in rivers with implications for predictions of flood stage and bed evolution

USGS Publications Warehouse

Nelson, Jonathan M.; Shimizu, Yasuyuki; Giri, Sanjay; McDonald, Richard R.

2010-01-01

Uncertainties in flood stage prediction and bed evolution in rivers are frequently associated with the evolution of bedforms over a hydrograph. For the case of flood prediction, the evolution of the bedforms may alter the effective bed roughness, so predictions of stage and velocity based on assuming bedforms retain the same size and shape over a hydrograph will be incorrect. These same effects will produce errors in the prediction of the sediment transport and bed evolution, but in this latter case the errors are typically larger, as even small errors in the prediction of bedform form drag can make very large errors in predicting the rates of sediment motion and the associated erosion and deposition. In situations where flows change slowly, it may be possible to use empirical results that relate bedform morphology to roughness and effective form drag to avoid these errors; but in many cases where the bedforms evolve rapidly and are in disequilibrium with the instantaneous flow, these empirical methods cannot be accurately applied. Over the past few years, computational models for bedform development, migration, and adjustment to varying flows have been developed and tested with a variety of laboratory and field data. These models, which are based on detailed multidimensional flow modeling incorporating large eddy simulation, appear to be capable of predicting bedform dimensions during steady flows as well as their time dependence during discharge variations. In the work presented here, models of this type are used to investigate the impacts of bedform on stage and bed evolution in rivers during flood hydrographs. The method is shown to reproduce hysteresis in rating curves as well as other more subtle effects in the shape of flood waves. Techniques for combining the bedform evolution models with larger-scale models for river reach flow, sediment transport, and bed evolution are described and used to show the importance of including dynamic bedform effects in river modeling. For example calculations for a flood on the Kootenai River, errors of almost 1m in predicted stage and errors of about a factor of two in the predicted maximum depths of erosion can be attributed to bedform evolution. Thus, treating bedforms explicitly in flood and bed evolution models can decrease uncertainty and increase the accuracy of predictions.

Tax revenue and inflation rate predictions in Banda Aceh using Vector Error Correction Model (VECM)

NASA Astrophysics Data System (ADS)

Maulia, Eva; Miftahuddin; Sofyan, Hizir

2018-05-01

A country has some important parameters to achieve the welfare of the economy, such as tax revenues and inflation. One of the largest revenues of the state budget in Indonesia comes from the tax sector. Besides, the rate of inflation occurring in a country can be used as one measure, to measure economic problems that the country facing. Given the importance of tax revenue and inflation rate control in achieving economic prosperity, it is necessary to analyze the relationship and forecasting tax revenue and inflation rate. VECM (Vector Error Correction Model) was chosen as the method used in this research, because of the data used in the form of multivariate time series data. This study aims to produce a VECM model with optimal lag and to predict the tax revenue and inflation rate of the VECM model. The results show that the best model for data of tax revenue and the inflation rate in Banda Aceh City is VECM with 3rd optimal lag or VECM (3). Of the seven models formed, there is a significant model that is the acceptance model of income tax. The predicted results of tax revenue and the inflation rate in Kota Banda Aceh for the next 6, 12 and 24 periods (months) obtained using VECM (3) are considered valid, since they have a minimum error value compared to other models.

Methods to Improve the Maintenance of the Earth Catalog of Satellites During Severe Solar Storms

NASA Technical Reports Server (NTRS)

Wilkin, Paul G.; Tolson, Robert H.

1998-01-01

The objective of this thesis is to investigate methods to improve the ability to maintain the inventory of orbital elements of Earth satellites during periods of atmospheric disturbance brought on by severe solar activity. Existing techniques do not account for such atmospheric dynamics, resulting in tracking errors of several seconds in predicted crossing time. Two techniques are examined to reduce of these tracking errors. First, density predicted from various atmospheric models is fit to the orbital decay rate for a number of satellites. An orbital decay model is then developed that could be used to reduce tracking errors by accounting for atmospheric changes. The second approach utilizes a Kalman filter to estimate the orbital decay rate of a satellite after every observation. The new information is used to predict the next observation. Results from the first approach demonstrated the feasibility of building an orbital decay model based on predicted atmospheric density. Correlation of atmospheric density to orbital decay was as high as 0.88. However, it is clear that contemporary: atmospheric models need further improvement in modeling density perturbations polar region brought on by solar activity. The second approach resulted in a dramatic reduction in tracking errors for certain satellites during severe solar Storms. For example, in the limited cases studied, the reduction in tracking errors ranged from 79 to 25 percent.

Predicting online ratings based on the opinion spreading process

NASA Astrophysics Data System (ADS)

He, Xing-Sheng; Zhou, Ming-Yang; Zhuo, Zhao; Fu, Zhong-Qian; Liu, Jian-Guo

2015-10-01

Predicting users' online ratings is always a challenge issue and has drawn lots of attention. In this paper, we present a rating prediction method by combining the user opinion spreading process with the collaborative filtering algorithm, where user similarity is defined by measuring the amount of opinion a user transfers to another based on the primitive user-item rating matrix. The proposed method could produce a more precise rating prediction for each unrated user-item pair. In addition, we introduce a tunable parameter λ to regulate the preferential diffusion relevant to the degree of both opinion sender and receiver. The numerical results for Movielens and Netflix data sets show that this algorithm has a better accuracy than the standard user-based collaborative filtering algorithm using Cosine and Pearson correlation without increasing computational complexity. By tuning λ, our method could further boost the prediction accuracy when using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as measurements. In the optimal cases, on Movielens and Netflix data sets, the corresponding algorithmic accuracy (MAE and RMSE) are improved 11.26% and 8.84%, 13.49% and 10.52% compared to the item average method, respectively.

Mean Bias in Seasonal Forecast Model and ENSO Prediction Error.

PubMed

Kim, Seon Tae; Jeong, Hye-In; Jin, Fei-Fei

2017-07-20

This study uses retrospective forecasts made using an APEC Climate Center seasonal forecast model to investigate the cause of errors in predicting the amplitude of El Niño Southern Oscillation (ENSO)-driven sea surface temperature variability. When utilizing Bjerknes coupled stability (BJ) index analysis, enhanced errors in ENSO amplitude with forecast lead times are found to be well represented by those in the growth rate estimated by the BJ index. ENSO amplitude forecast errors are most strongly associated with the errors in both the thermocline slope response and surface wind response to forcing over the tropical Pacific, leading to errors in thermocline feedback. This study concludes that upper ocean temperature bias in the equatorial Pacific, which becomes more intense with increasing lead times, is a possible cause of forecast errors in the thermocline feedback and thus in ENSO amplitude.

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.

Error associated with model predictions of wildland fire rate of spread

Treesearch

Miguel G. Cruz; Martin E. Alexander

2015-01-01

How well can we expect to predict the spread rate of wildfires and prescribed fires? The degree of accuracy in model predictions of wildland fire behaviour characteristics are dependent on the model's applicability to a given situation, the validity of the model's relationships, and the reliability of the model input data (Alexander and Cruz 2013b#. We...

The contribution of low-energy protons to the total on-orbit SEU rate

DOE PAGES

Dodds, Nathaniel Anson; Martinez, Marino J.; Dodd, Paul E.; ...

2015-11-10

Low- and high-energy proton experimental data and error rate predictions are presented for many bulk Si and SOI circuits from the 20-90 nm technology nodes to quantify how much low-energy protons (LEPs) can contribute to the total on-orbit single-event upset (SEU) rate. Every effort was made to predict LEP error rates that are conservatively high; even secondary protons generated in the spacecraft shielding have been included in the analysis. Across all the environments and circuits investigated, and when operating within 10% of the nominal operating voltage, LEPs were found to increase the total SEU rate to up to 4.3 timesmore » as high as it would have been in the absence of LEPs. Therefore, the best approach to account for LEP effects may be to calculate the total error rate from high-energy protons and heavy ions, and then multiply it by a safety margin of 5. If that error rate can be tolerated then our findings suggest that it is justified to waive LEP tests in certain situations. Trends were observed in the LEP angular responses of the circuits tested. As a result, grazing angles were the worst case for the SOI circuits, whereas the worst-case angle was at or near normal incidence for the bulk circuits.« less

SEU induced errors observed in microprocessor systems

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

Asenek, V.; Underwood, C.; Oldfield, M.

In this paper, the authors present software tools for predicting the rate and nature of observable SEU induced errors in microprocessor systems. These tools are built around a commercial microprocessor simulator and are used to analyze real satellite application systems. Results obtained from simulating the nature of SEU induced errors are shown to correlate with ground-based radiation test data.

Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-02-01

Multiresolution analysis techniques including continuous wavelet transform, empirical mode decomposition, and variational mode decomposition are tested in the context of interest rate next-day variation prediction. In particular, multiresolution analysis techniques are used to decompose interest rate actual variation and feedforward neural network for training and prediction. Particle swarm optimization technique is adopted to optimize its initial weights. For comparison purpose, autoregressive moving average model, random walk process and the naive model are used as main reference models. In order to show the feasibility of the presented hybrid models that combine multiresolution analysis techniques and feedforward neural network optimized by particle swarm optimization, we used a set of six illustrative interest rates; including Moody's seasoned Aaa corporate bond yield, Moody's seasoned Baa corporate bond yield, 3-Month, 6-Month and 1-Year treasury bills, and effective federal fund rate. The forecasting results show that all multiresolution-based prediction systems outperform the conventional reference models on the criteria of mean absolute error, mean absolute deviation, and root mean-squared error. Therefore, it is advantageous to adopt hybrid multiresolution techniques and soft computing models to forecast interest rate daily variations as they provide good forecasting performance.

CREME96 and Related Error Rate Prediction Methods

NASA Technical Reports Server (NTRS)

Adams, James H., Jr.

2012-01-01

Predicting the rate of occurrence of single event effects (SEEs) in space requires knowledge of the radiation environment and the response of electronic devices to that environment. Several analytical models have been developed over the past 36 years to predict SEE rates. The first error rate calculations were performed by Binder, Smith and Holman. Bradford and Pickel and Blandford, in their CRIER (Cosmic-Ray-Induced-Error-Rate) analysis code introduced the basic Rectangular ParallelePiped (RPP) method for error rate calculations. For the radiation environment at the part, both made use of the Cosmic Ray LET (Linear Energy Transfer) spectra calculated by Heinrich for various absorber Depths. A more detailed model for the space radiation environment within spacecraft was developed by Adams and co-workers. This model, together with a reformulation of the RPP method published by Pickel and Blandford, was used to create the CR ME (Cosmic Ray Effects on Micro-Electronics) code. About the same time Shapiro wrote the CRUP (Cosmic Ray Upset Program) based on the RPP method published by Bradford. It was the first code to specifically take into account charge collection from outside the depletion region due to deformation of the electric field caused by the incident cosmic ray. Other early rate prediction methods and codes include the Single Event Figure of Merit, NOVICE, the Space Radiation code and the effective flux method of Binder which is the basis of the SEFA (Scott Effective Flux Approximation) model. By the early 1990s it was becoming clear that CREME and the other early models needed Revision. This revision, CREME96, was completed and released as a WWW-based tool, one of the first of its kind. The revisions in CREME96 included improved environmental models and improved models for calculating single event effects. The need for a revision of CREME also stimulated the development of the CHIME (CRRES/SPACERAD Heavy Ion Model of the Environment) and MACREE (Modeling and Analysis of Cosmic Ray Effects in Electronics). The Single Event Figure of Merit method was also revised to use the solar minimum galactic cosmic ray spectrum and extended to circular orbits down to 200 km at any inclination. More recently a series of commercial codes was developed by TRAD (Test & Radiations) which includes the OMERE code which calculates single event effects. There are other error rate prediction methods which use Monte Carlo techniques. In this chapter the analytic methods for estimating the environment within spacecraft will be discussed.

Explaining errors in children's questions.

PubMed

Rowland, Caroline F

2007-07-01

The ability to explain the occurrence of errors in children's speech is an essential component of successful theories of language acquisition. The present study tested some generativist and constructivist predictions about error on the questions produced by ten English-learning children between 2 and 5 years of age. The analyses demonstrated that, as predicted by some generativist theories [e.g. Santelmann, L., Berk, S., Austin, J., Somashekar, S. & Lust. B. (2002). Continuity and development in the acquisition of inversion in yes/no questions: dissociating movement and inflection, Journal of Child Language, 29, 813-842], questions with auxiliary DO attracted higher error rates than those with modal auxiliaries. However, in wh-questions, questions with modals and DO attracted equally high error rates, and these findings could not be explained in terms of problems forming questions with why or negated auxiliaries. It was concluded that the data might be better explained in terms of a constructivist account that suggests that entrenched item-based constructions may be protected from error in children's speech, and that errors occur when children resort to other operations to produce questions [e.g. Dabrowska, E. (2000). From formula to schema: the acquisition of English questions. Cognitive Liguistics, 11, 83-102; Rowland, C. F. & Pine, J. M. (2000). Subject-auxiliary inversion errors and wh-question acquisition: What children do know? Journal of Child Language, 27, 157-181; Tomasello, M. (2003). Constructing a language: A usage-based theory of language acquisition. Cambridge, MA: Harvard University Press]. However, further work on constructivist theory development is required to allow researchers to make predictions about the nature of these operations.

Implementation of bayesian model averaging on the weather data forecasting applications utilizing open weather map

NASA Astrophysics Data System (ADS)

Rahmat, R. F.; Nasution, F. R.; Seniman; Syahputra, M. F.; Sitompul, O. S.

2018-02-01

Weather is condition of air in a certain region at a relatively short period of time, measured with various parameters such as; temperature, air preasure, wind velocity, humidity and another phenomenons in the atmosphere. In fact, extreme weather due to global warming would lead to drought, flood, hurricane and other forms of weather occasion, which directly affects social andeconomic activities. Hence, a forecasting technique is to predict weather with distinctive output, particullary mapping process based on GIS with information about current weather status in certain cordinates of each region with capability to forecast for seven days afterward. Data used in this research are retrieved in real time from the server openweathermap and BMKG. In order to obtain a low error rate and high accuracy of forecasting, the authors use Bayesian Model Averaging (BMA) method. The result shows that the BMA method has good accuracy. Forecasting error value is calculated by mean square error shows (MSE). The error value emerges at minumum temperature rated at 0.28 and maximum temperature rated at 0.15. Meanwhile, the error value of minimum humidity rates at 0.38 and the error value of maximum humidity rates at 0.04. Afterall, the forecasting error rate of wind speed is at 0.076. The lower the forecasting error rate, the more optimized the accuracy is.

Visuo-vestibular interaction: predicting the position of a visual target during passive body rotation.

PubMed

Mackrous, I; Simoneau, M

2011-11-10

Following body rotation, optimal updating of the position of a memorized target is attained when retinal error is perceived and corrective saccade is performed. Thus, it appears that these processes may enable the calibration of the vestibular system by facilitating the sharing of information between both reference frames. Here, it is assessed whether having sensory information regarding body rotation in the target reference frame could enhance an individual's learning rate to predict the position of an earth-fixed target. During rotation, participants had to respond when they felt their body midline had crossed the position of the target and received knowledge of result. During practice blocks, for two groups, visual cues were displayed in the same reference frame of the target, whereas a third group relied on vestibular information (vestibular-only group) to predict the location of the target. Participants, unaware of the role of the visual cues (visual cues group), learned to predict the location of the target and spatial error decreased from 16.2 to 2.0°, reflecting a learning rate of 34.08 trials (determined from fitting a falling exponential model). In contrast, the group aware of the role of the visual cues (explicit visual cues group) showed a faster learning rate (i.e. 2.66 trials) but similar final spatial error 2.9°. For the vestibular-only group, similar accuracy was achieved (final spatial error of 2.3°), but their learning rate was much slower (i.e. 43.29 trials). Transferring to the Post-test (no visual cues and no knowledge of result) increased the spatial error of the explicit visual cues group (9.5°), but it did not change the performance of the vestibular group (1.2°). Overall, these results imply that cognition assists the brain in processing the sensory information within the target reference frame. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

Evaluating the Performance of a New Model for Predicting the Growth of Clostridium perfringens in Cooked, Uncured Meat and Poultry Products under Isothermal, Heating, and Dynamically Cooling Conditions.

PubMed

Huang, Lihan

2016-07-01

Clostridium perfringens type A is a significant public health threat and its spores may germinate, outgrow, and multiply during cooling of cooked meats. This study applies a new C. perfringens growth model in the USDA Integrated Pathogen Modeling Program-Dynamic Prediction (IPMP Dynamic Prediction) Dynamic Prediction to predict the growth from spores of C. perfringens in cooked uncured meat and poultry products using isothermal, dynamic heating, and cooling data reported in the literature. The residual errors of predictions (observation-prediction) are analyzed, and the root-mean-square error (RMSE) calculated. For isothermal and heating profiles, each data point in growth curves is compared. The mean residual errors (MRE) of predictions range from -0.40 to 0.02 Log colony forming units (CFU)/g, with a RMSE of approximately 0.6 Log CFU/g. For cooling, the end point predictions are conservative in nature, with an MRE of -1.16 Log CFU/g for single-rate cooling and -0.66 Log CFU/g for dual-rate cooling. The RMSE is between 0.6 and 0.7 Log CFU/g. Compared with other models reported in the literature, this model makes more accurate and fail-safe predictions. For cooling, the percentage for accurate and fail-safe predictions is between 97.6% and 100%. Under criterion 1, the percentage of accurate predictions is 47.5% for single-rate cooling and 66.7% for dual-rate cooling, while the fail-dangerous predictions are between 0% and 2.4%. This study demonstrates that IPMP Dynamic Prediction can be used by food processors and regulatory agencies as a tool to predict the growth of C. perfringens in uncured cooked meats and evaluate the safety of cooked or heat-treated uncured meat and poultry products exposed to cooling deviations or to develop customized cooling schedules. This study also demonstrates the need for more accurate data collection during cooling. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Sentinel node status prediction by four statistical models: results from a large bi-institutional series (n = 1132).

PubMed

Mocellin, Simone; Thompson, John F; Pasquali, Sandro; Montesco, Maria C; Pilati, Pierluigi; Nitti, Donato; Saw, Robyn P; Scolyer, Richard A; Stretch, Jonathan R; Rossi, Carlo R

2009-12-01

To improve selection for sentinel node (SN) biopsy (SNB) in patients with cutaneous melanoma using statistical models predicting SN status. About 80% of patients currently undergoing SNB are node negative. In the absence of conclusive evidence of a SNBassociated survival benefit, these patients may be over-treated. Here, we tested the efficiency of 4 different models in predicting SN status. The clinicopathologic data (age, gender, tumor thickness, Clark level, regression, ulceration, histologic subtype, and mitotic index) of 1132 melanoma patients who had undergone SNB at institutions in Italy and Australia were analyzed. Logistic regression, classification tree, random forest, and support vector machine models were fitted to the data. The predictive models were built with the aim of maximizing the negative predictive value (NPV) and reducing the rate of SNB procedures though minimizing the error rate. After cross-validation logistic regression, classification tree, random forest, and support vector machine predictive models obtained clinically relevant NPV (93.6%, 94.0%, 97.1%, and 93.0%, respectively), SNB reduction (27.5%, 29.8%, 18.2%, and 30.1%, respectively), and error rates (1.8%, 1.8%, 0.5%, and 2.1%, respectively). Using commonly available clinicopathologic variables, predictive models can preoperatively identify a proportion of patients ( approximately 25%) who might be spared SNB, with an acceptable (1%-2%) error. If validated in large prospective series, these models might be implemented in the clinical setting for improved patient selection, which ultimately would lead to better quality of life for patients and optimization of resource allocation for the health care system.

Pilots Rate Augmented Generalized Predictive Control for Reconfiguration

NASA Technical Reports Server (NTRS)

Soloway, Don; Haley, Pam

2004-01-01

The objective of this paper is to report the results from the research being conducted in reconfigurable fight controls at NASA Ames. A study was conducted with three NASA Dryden test pilots to evaluate two approaches of reconfiguring an aircraft's control system when failures occur in the control surfaces and engine. NASA Ames is investigating both a Neural Generalized Predictive Control scheme and a Neural Network based Dynamic Inverse controller. This paper highlights the Predictive Control scheme where a simple augmentation to reduce zero steady-state error led to the neural network predictor model becoming redundant for the task. Instead of using a neural network predictor model, a nominal single point linear model was used and then augmented with an error corrector. This paper shows that the Generalized Predictive Controller and the Dynamic Inverse Neural Network controller perform equally well at reconfiguration, but with less rate requirements from the actuators. Also presented are the pilot ratings for each controller for various failure scenarios and two samples of the required control actuation during reconfiguration. Finally, the paper concludes by stepping through the Generalized Predictive Control's reconfiguration process for an elevator failure.

Modeling coherent errors in quantum error correction

NASA Astrophysics Data System (ADS)

Greenbaum, Daniel; Dutton, Zachary

2018-01-01

Analysis of quantum error correcting codes is typically done using a stochastic, Pauli channel error model for describing the noise on physical qubits. However, it was recently found that coherent errors (systematic rotations) on physical data qubits result in both physical and logical error rates that differ significantly from those predicted by a Pauli model. Here we examine the accuracy of the Pauli approximation for noise containing coherent errors (characterized by a rotation angle ɛ) under the repetition code. We derive an analytic expression for the logical error channel as a function of arbitrary code distance d and concatenation level n, in the small error limit. We find that coherent physical errors result in logical errors that are partially coherent and therefore non-Pauli. However, the coherent part of the logical error is negligible at fewer than {ε }-({dn-1)} error correction cycles when the decoder is optimized for independent Pauli errors, thus providing a regime of validity for the Pauli approximation. Above this number of correction cycles, the persistent coherent logical error will cause logical failure more quickly than the Pauli model would predict, and this may need to be combated with coherent suppression methods at the physical level or larger codes.

Classification based upon gene expression data: bias and precision of error rates.

PubMed

Wood, Ian A; Visscher, Peter M; Mengersen, Kerrie L

2007-06-01

Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp

Design of a digital voice data compression technique for orbiter voice channels

NASA Technical Reports Server (NTRS)

1975-01-01

Candidate techniques were investigated for digital voice compression to a transmission rate of 8 kbps. Good voice quality, speaker recognition, and robustness in the presence of error bursts were considered. The technique of delayed-decision adaptive predictive coding is described and compared with conventional adaptive predictive coding. Results include a set of experimental simulations recorded on analog tape. The two FM broadcast segments produced show the delayed-decision technique to be virtually undegraded or minimally degraded at .001 and .01 Viterbi decoder bit error rates. Preliminary estimates of the hardware complexity of this technique indicate potential for implementation in space shuttle orbiters.

Combining inferences from models of capture efficiency, detectability, and suitable habitat to classify landscapes for conservation of threatened bull trout

USGS Publications Warehouse

Peterson, J.; Dunham, J.B.

2003-01-01

Effective conservation efforts for at-risk species require knowledge of the locations of existing populations. Species presence can be estimated directly by conducting field-sampling surveys or alternatively by developing predictive models. Direct surveys can be expensive and inefficient, particularly for rare and difficult-to-sample species, and models of species presence may produce biased predictions. We present a Bayesian approach that combines sampling and model-based inferences for estimating species presence. The accuracy and cost-effectiveness of this approach were compared to those of sampling surveys and predictive models for estimating the presence of the threatened bull trout ( Salvelinus confluentus ) via simulation with existing models and empirical sampling data. Simulations indicated that a sampling-only approach would be the most effective and would result in the lowest presence and absence misclassification error rates for three thresholds of detection probability. When sampling effort was considered, however, the combined approach resulted in the lowest error rates per unit of sampling effort. Hence, lower probability-of-detection thresholds can be specified with the combined approach, resulting in lower misclassification error rates and improved cost-effectiveness.

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.

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.

Reward prediction error signal enhanced by striatum-amygdala interaction explains the acceleration of probabilistic reward learning by emotion.

PubMed

Watanabe, Noriya; Sakagami, Masamichi; Haruno, Masahiko

2013-03-06

Learning does not only depend on rationality, because real-life learning cannot be isolated from emotion or social factors. Therefore, it is intriguing to determine how emotion changes learning, and to identify which neural substrates underlie this interaction. Here, we show that the task-independent presentation of an emotional face before a reward-predicting cue increases the speed of cue-reward association learning in human subjects compared with trials in which a neutral face is presented. This phenomenon was attributable to an increase in the learning rate, which regulates reward prediction errors. Parallel to these behavioral findings, functional magnetic resonance imaging demonstrated that presentation of an emotional face enhanced reward prediction error (RPE) signal in the ventral striatum. In addition, we also found a functional link between this enhanced RPE signal and increased activity in the amygdala following presentation of an emotional face. Thus, this study revealed an acceleration of cue-reward association learning by emotion, and underscored a role of striatum-amygdala interactions in the modulation of the reward prediction errors by emotion.

Data driven CAN node reliability assessment for manufacturing system

NASA Astrophysics Data System (ADS)

Zhang, Leiming; Yuan, Yong; Lei, Yong

2017-01-01

The reliability of the Controller Area Network(CAN) is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off(MTTB) of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process(GZIP) model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.

Iterative random vs. Kennard-Stone sampling for IR spectrum-based classification task using PLS2-DA

NASA Astrophysics Data System (ADS)

Lee, Loong Chuen; Liong, Choong-Yeun; Jemain, Abdul Aziz

2018-04-01

External testing (ET) is preferred over auto-prediction (AP) or k-fold-cross-validation in estimating more realistic predictive ability of a statistical model. With IR spectra, Kennard-stone (KS) sampling algorithm is often used to split the data into training and test sets, i.e. respectively for model construction and for model testing. On the other hand, iterative random sampling (IRS) has not been the favored choice though it is theoretically more likely to produce reliable estimation. The aim of this preliminary work is to compare performances of KS and IRS in sampling a representative training set from an attenuated total reflectance - Fourier transform infrared spectral dataset (of four varieties of blue gel pen inks) for PLS2-DA modeling. The `best' performance achievable from the dataset is estimated with AP on the full dataset (APF, error). Both IRS (n = 200) and KS were used to split the dataset in the ratio of 7:3. The classic decision rule (i.e. maximum value-based) is employed for new sample prediction via partial least squares - discriminant analysis (PLS2-DA). Error rate of each model was estimated repeatedly via: (a) AP on full data (APF, error); (b) AP on training set (APS, error); and (c) ET on the respective test set (ETS, error). A good PLS2-DA model is expected to produce APS, error and EVS, error that is similar to the APF, error. Bearing that in mind, the similarities between (a) APS, error vs. APF, error; (b) ETS, error vs. APF, error and; (c) APS, error vs. ETS, error were evaluated using correlation tests (i.e. Pearson and Spearman's rank test), using series of PLS2-DA models computed from KS-set and IRS-set, respectively. Overall, models constructed from IRS-set exhibits more similarities between the internal and external error rates than the respective KS-set, i.e. less risk of overfitting. In conclusion, IRS is more reliable than KS in sampling representative training set.

Modulation of the error-related negativity by response conflict.

PubMed

Danielmeier, Claudia; Wessel, Jan R; Steinhauser, Marco; Ullsperger, Markus

2009-11-01

An arrow version of the Eriksen flanker task was employed to investigate the influence of conflict on the error-related negativity (ERN). The degree of conflict was modulated by varying the distance between flankers and the target arrow (CLOSE and FAR conditions). Error rates and reaction time data from a behavioral experiment were used to adapt a connectionist model of this task. This model was based on the conflict monitoring theory and simulated behavioral and event-related potential data. The computational model predicted an increased ERN amplitude in FAR incompatible (the low-conflict condition) compared to CLOSE incompatible errors (the high-conflict condition). A subsequent ERP experiment confirmed the model predictions. The computational model explains this finding with larger post-response conflict in far trials. In addition, data and model predictions of the N2 and the LRP support the conflict interpretation of the ERN.

Quantifying errors in surface ozone predictions associated with clouds over the CONUS: a WRF-Chem modeling study using satellite cloud retrievals

NASA Astrophysics Data System (ADS)

Ryu, Young-Hee; Hodzic, Alma; Barre, Jerome; Descombes, Gael; Minnis, Patrick

2018-05-01

Clouds play a key role in radiation and hence O3 photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much error in O3 predictions can be directly attributed to error in cloud predictions. This study applies the Weather Research and Forecasting with Chemistry (WRF-Chem) model at 12 km horizontal resolution with the Morrison microphysics and Grell 3-D cumulus parameterization to quantify uncertainties in summertime surface O3 predictions associated with cloudiness over the contiguous United States (CONUS). All model simulations are driven by reanalysis of atmospheric data and reinitialized every 2 days. In sensitivity simulations, cloud fields used for photochemistry are corrected based on satellite cloud retrievals. The results show that WRF-Chem predicts about 55 % of clouds in the right locations and generally underpredicts cloud optical depths. These errors in cloud predictions can lead to up to 60 ppb of overestimation in hourly surface O3 concentrations on some days. The average difference in summertime surface O3 concentrations derived from the modeled clouds and satellite clouds ranges from 1 to 5 ppb for maximum daily 8 h average O3 (MDA8 O3) over the CONUS. This represents up to ˜ 40 % of the total MDA8 O3 bias under cloudy conditions in the tested model version. Surface O3 concentrations are sensitive to cloud errors mainly through the calculation of photolysis rates (for ˜ 80 %), and to a lesser extent to light-dependent BVOC emissions. The sensitivity of surface O3 concentrations to satellite-based cloud corrections is about 2 times larger in VOC-limited than NOx-limited regimes. Our results suggest that the benefits of accurate predictions of cloudiness would be significant in VOC-limited regions, which are typical of urban areas.

Prediction of pilot reserve attention capacity during air-to-air target tracking

NASA Technical Reports Server (NTRS)

Onstott, E. D.; Faulkner, W. H.

1977-01-01

Reserve attention capacity of a pilot was calculated using a pilot model that allocates exclusive model attention according to the ranking of task urgency functions whose variables are tracking error and error rate. The modeled task consisted of tracking a maneuvering target aircraft both vertically and horizontally, and when possible, performing a diverting side task which was simulated by the precise positioning of an electrical stylus and modeled as a task of constant urgency in the attention allocation algorithm. The urgency of the single loop vertical task is simply the magnitude of the vertical tracking error, while the multiloop horizontal task requires a nonlinear urgency measure of error and error rate terms. Comparison of model results with flight simulation data verified the computed model statistics of tracking error of both axes, lateral and longitudinal stick amplitude and rate, and side task episodes. Full data for the simulation tracking statistics as well as the explicit equations and structure of the urgency function multiaxis pilot model are presented.

A study of the 200-metre fast walk test as a possible new assessment tool to predict maximal heart rate and define target heart rate for exercise training of coronary heart disease patients.

PubMed

Casillas, Jean-Marie; Joussain, Charles; Gremeaux, Vincent; Hannequin, Armelle; Rapin, Amandine; Laurent, Yves; Benaïm, Charles

2015-02-01

To develop a new predictive model of maximal heart rate based on two walking tests at different speeds (comfortable and brisk walking) as an alternative to a cardiopulmonary exercise test during cardiac rehabilitation. Evaluation of a clinical assessment tool. A Cardiac Rehabilitation Department in France. A total of 148 patients (133 men), mean age of 59 ±9 years, at the end of an outpatient cardiac rehabilitation programme. Patients successively performed a 6-minute walk test, a 200 m fast-walk test (200mFWT), and a cardiopulmonary exercise test, with measure of heart rate at the end of each test. An all-possible regression procedure was used to determine the best predictive regression models of maximal heart rate. The best model was compared with the Fox equation in term of predictive error of maximal heart rate using the paired t-test. Results of the two walking tests correlated significantly with maximal heart rate determined during the cardiopulmonary exercise test, whereas anthropometric parameters and resting heart rate did not. The simplified predictive model with the most acceptable mean error was: maximal heart rate = 130 - 0.6 × age + 0.3 × HR200mFWT (R(2) = 0.24). This model was superior to the Fox formula (R(2) = 0.138). The relationship between training target heart rate calculated from measured reserve heart rate and that established using this predictive model was statistically significant (r = 0.528, p < 10(-6)). A formula combining heart rate measured during a safe simple fast walk test and age is more efficient than an equation only including age to predict maximal heart rate and training target heart rate. © The Author(s) 2014.

Draft versus finished sequence data for DNA and protein diagnostic signature development

PubMed Central

Gardner, Shea N.; Lam, Marisa W.; Smith, Jason R.; Torres, Clinton L.; Slezak, Tom R.

2005-01-01

Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10−3–10−5 (∼8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ∼1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures. PMID:16243783

PREDICTION OF SOLAR FLARE SIZE AND TIME-TO-FLARE USING SUPPORT VECTOR MACHINE REGRESSION

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

Boucheron, Laura E.; Al-Ghraibah, Amani; McAteer, R. T. James

We study the prediction of solar flare size and time-to-flare using 38 features describing magnetic complexity of the photospheric magnetic field. This work uses support vector regression to formulate a mapping from the 38-dimensional feature space to a continuous-valued label vector representing flare size or time-to-flare. When we consider flaring regions only, we find an average error in estimating flare size of approximately half a geostationary operational environmental satellite (GOES) class. When we additionally consider non-flaring regions, we find an increased average error of approximately three-fourths a GOES class. We also consider thresholding the regressed flare size for the experimentmore » containing both flaring and non-flaring regions and find a true positive rate of 0.69 and a true negative rate of 0.86 for flare prediction. The results for both of these size regression experiments are consistent across a wide range of predictive time windows, indicating that the magnetic complexity features may be persistent in appearance long before flare activity. This is supported by our larger error rates of some 40 hr in the time-to-flare regression problem. The 38 magnetic complexity features considered here appear to have discriminative potential for flare size, but their persistence in time makes them less discriminative for the time-to-flare problem.« less

Horizon sensor errors calculated by computer models compared with errors measured in orbit

NASA Technical Reports Server (NTRS)

Ward, K. A.; Hogan, R.; Andary, J.

1982-01-01

Using a computer program to model the earth's horizon and to duplicate the signal processing procedure employed by the ESA (Earth Sensor Assembly), errors due to radiance variation have been computed for a particular time of the year. Errors actually occurring in flight at the same time of year are inferred from integrated rate gyro data for a satellite of the TIROS series of NASA weather satellites (NOAA-A). The predicted performance is compared with actual flight history.

Evaluation of a Mysis bioenergetics model

USGS Publications Warehouse

Chipps, S.R.; Bennett, D.H.

2002-01-01

Direct approaches for estimating the feeding rate of the opossum shrimp Mysis relicta can be hampered by variable gut residence time (evacuation rate models) and non-linear functional responses (clearance rate models). Bioenergetics modeling provides an alternative method, but the reliability of this approach needs to be evaluated using independent measures of growth and food consumption. In this study, we measured growth and food consumption for M. relicta and compared experimental results with those predicted from a Mysis bioenergetics model. For Mysis reared at 10??C, model predictions were not significantly different from observed values. Moreover, decomposition of mean square error indicated that 70% of the variation between model predictions and observed values was attributable to random error. On average, model predictions were within 12% of observed values. A sensitivity analysis revealed that Mysis respiration and prey energy density were the most sensitive parameters affecting model output. By accounting for uncertainty (95% CLs) in Mysis respiration, we observed a significant improvement in the accuracy of model output (within 5% of observed values), illustrating the importance of sensitive input parameters for model performance. These findings help corroborate the Mysis bioenergetics model and demonstrate the usefulness of this approach for estimating Mysis feeding rate.

[Establishing and applying of autoregressive integrated moving average model to predict the incidence rate of dysentery in Shanghai].

PubMed

Li, Jian; Wu, Huan-Yu; Li, Yan-Ting; Jin, Hui-Ming; Gu, Bao-Ke; Yuan, Zheng-An

2010-01-01

To explore the feasibility of establishing and applying of autoregressive integrated moving average (ARIMA) model to predict the incidence rate of dysentery in Shanghai, so as to provide the theoretical basis for prevention and control of dysentery. ARIMA model was established based on the monthly incidence rate of dysentery of Shanghai from 1990 to 2007. The parameters of model were estimated through unconditional least squares method, the structure was determined according to criteria of residual un-correlation and conclusion, and the model goodness-of-fit was determined through Akaike information criterion (AIC) and Schwarz Bayesian criterion (SBC). The constructed optimal model was applied to predict the incidence rate of dysentery of Shanghai in 2008 and evaluate the validity of model through comparing the difference of predicted incidence rate and actual one. The incidence rate of dysentery in 2010 was predicted by ARIMA model based on the incidence rate from January 1990 to June 2009. The model ARIMA (1, 1, 1) (0, 1, 2)(12) had a good fitness to the incidence rate with both autoregressive coefficient (AR1 = 0.443) during the past time series, moving average coefficient (MA1 = 0.806) and seasonal moving average coefficient (SMA1 = 0.543, SMA2 = 0.321) being statistically significant (P < 0.01). AIC and SBC were 2.878 and 16.131 respectively and predicting error was white noise. The mathematic function was (1-0.443B) (1-B) (1-B(12))Z(t) = (1-0.806B) (1-0.543B(12)) (1-0.321B(2) x 12) micro(t). The predicted incidence rate in 2008 was consistent with the actual one, with the relative error of 6.78%. The predicted incidence rate of dysentery in 2010 based on the incidence rate from January 1990 to June 2009 would be 9.390 per 100 thousand. ARIMA model can be used to fit the changes of incidence rate of dysentery and to forecast the future incidence rate in Shanghai. It is a predicted model of high precision for short-time forecast.

Human Factors Risk Analyses of a Doffing Protocol for Ebola-Level Personal Protective Equipment: Mapping Errors to Contamination.

PubMed

Mumma, Joel M; Durso, Francis T; Ferguson, Ashley N; Gipson, Christina L; Casanova, Lisa; Erukunuakpor, Kimberly; Kraft, Colleen S; Walsh, Victoria L; Zimring, Craig; DuBose, Jennifer; Jacob, Jesse T

2018-03-05

Doffing protocols for personal protective equipment (PPE) are critical for keeping healthcare workers (HCWs) safe during care of patients with Ebola virus disease. We assessed the relationship between errors and self-contamination during doffing. Eleven HCWs experienced with doffing Ebola-level PPE participated in simulations in which HCWs donned PPE marked with surrogate viruses (ɸ6 and MS2), completed a clinical task, and were assessed for contamination after doffing. Simulations were video recorded, and a failure modes and effects analysis and fault tree analyses were performed to identify errors during doffing, quantify their risk (risk index), and predict contamination data. Fifty-one types of errors were identified, many having the potential to spread contamination. Hand hygiene and removing the powered air purifying respirator (PAPR) hood had the highest total risk indexes (111 and 70, respectively) and number of types of errors (9 and 13, respectively). ɸ6 was detected on 10% of scrubs and the fault tree predicted a 10.4% contamination rate, likely occurring when the PAPR hood inadvertently contacted scrubs during removal. MS2 was detected on 10% of hands, 20% of scrubs, and 70% of inner gloves and the predicted rates were 7.3%, 19.4%, 73.4%, respectively. Fault trees for MS2 and ɸ6 contamination suggested similar pathways. Ebola-level PPE can both protect and put HCWs at risk for self-contamination throughout the doffing process, even among experienced HCWs doffing with a trained observer. Human factors methodologies can identify error-prone steps, delineate the relationship between errors and self-contamination, and suggest remediation strategies.

A Bayesian approach to model structural error and input variability in groundwater modeling

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Lin, Y. F. F.; Liang, F.

2015-12-01

Effective water resource management typically relies on numerical models to analyze groundwater flow and solute transport processes. Model structural error (due to simplification and/or misrepresentation of the "true" environmental system) and input forcing variability (which commonly arises since some inputs are uncontrolled or estimated with high uncertainty) are ubiquitous in groundwater models. Calibration that overlooks errors in model structure and input data can lead to biased parameter estimates and compromised predictions. We present a fully Bayesian approach for a complete assessment of uncertainty for spatially distributed groundwater models. The approach explicitly recognizes stochastic input and uses data-driven error models based on nonparametric kernel methods to account for model structural error. We employ exploratory data analysis to assist in specifying informative prior for error models to improve identifiability. The inference is facilitated by an efficient sampling algorithm based on DREAM-ZS and a parameter subspace multiple-try strategy to reduce the required number of forward simulations of the groundwater model. We demonstrate the Bayesian approach through a synthetic case study of surface-ground water interaction under changing pumping conditions. It is found that explicit treatment of errors in model structure and input data (groundwater pumping rate) has substantial impact on the posterior distribution of groundwater model parameters. Using error models reduces predictive bias caused by parameter compensation. In addition, input variability increases parametric and predictive uncertainty. The Bayesian approach allows for a comparison among the contributions from various error sources, which could inform future model improvement and data collection efforts on how to best direct resources towards reducing predictive uncertainty.

Verification of real-time WSA-ENLIL+Cone simulations of CME arrival-time at the CCMC/SWRC from 2010-2016

NASA Astrophysics Data System (ADS)

Wold, A. M.; Mays, M. L.; Taktakishvili, A.; Odstrcil, D.; MacNeice, P. J.; Jian, L. K.

2017-12-01

The Wang-Sheeley-Arge (WSA)-ENLIL+Cone model is used extensively in space weather operations world-wide to model CME propagation. As such, it is important to assess its performance. We present validation results of the WSA-ENLIL+Cone model installed at the Community Coordinated Modeling Center (CCMC) and executed in real-time by the CCMC/Space Weather Research Center (SWRC). CCMC/SWRC uses the WSA-ENLIL+Cone model to predict CME arrivals at NASA missions throughout the inner heliosphere. In this work we compare model predicted CME arrival-times to in-situ ICME leading edge measurements near Earth, STEREO-A and STEREO-B for simulations completed between March 2010-December 2016 (over 1,800 CMEs). We report hit, miss, false alarm, and correct rejection statistics for all three spacecraft. For all predicted CME arrivals, the hit rate is 0.5, and the false alarm rate is 0.1. For the 273 events where the CME was predicted to arrive at Earth, STEREO-A, or STEREO-B and we observed an arrival (hit), the mean absolute arrival-time prediction error was 10.4 ± 0.9 hours, with a tendency to early prediction error of -4.0 hours. We show the dependence of the arrival-time error on CME input parameters. We also explore the impact of the multi-spacecraft observations used to initialize the model CME inputs by comparing model verification results before and after the STEREO-B communication loss (since September 2014) and STEREO-A side-lobe operations (August 2014-December 2015). There is an increase of 1.7 hours in the CME arrival time error during single, or limited two-viewpoint periods, compared to the three-spacecraft viewpoint period. This trend would apply to a future space weather mission at L5 or L4 as another coronagraph viewpoint to reduce CME arrival time errors compared to a single L1 viewpoint.

Sensitivity of an Elekta iView GT a-Si EPID model to delivery errors for pre-treatment verification of IMRT fields.

PubMed

Herwiningsih, Sri; Hanlon, Peta; Fielding, Andrew

2014-12-01

A Monte Carlo model of an Elekta iViewGT amorphous silicon electronic portal imaging device (a-Si EPID) has been validated for pre-treatment verification of clinical IMRT treatment plans. The simulations involved the use of the BEAMnrc and DOSXYZnrc Monte Carlo codes to predict the response of the iViewGT a-Si EPID model. The predicted EPID images were compared to the measured images obtained from the experiment. The measured EPID images were obtained by delivering a photon beam from an Elekta Synergy linac to the Elekta iViewGT a-Si EPID. The a-Si EPID was used with no additional build-up material. Frame averaged EPID images were acquired and processed using in-house software. The agreement between the predicted and measured images was analyzed using the gamma analysis technique with acceptance criteria of 3 %/3 mm. The results show that the predicted EPID images for four clinical IMRT treatment plans have a good agreement with the measured EPID signal. Three prostate IMRT plans were found to have an average gamma pass rate of more than 95.0 % and a spinal IMRT plan has the average gamma pass rate of 94.3 %. During the period of performing this work a routine MLC calibration was performed and one of the IMRT treatments re-measured with the EPID. A change in the gamma pass rate for one field was observed. This was the motivation for a series of experiments to investigate the sensitivity of the method by introducing delivery errors, MLC position and dosimetric overshoot, into the simulated EPID images. The method was found to be sensitive to 1 mm leaf position errors and 10 % overshoot errors.

Maximum likelihood convolutional decoding (MCD) performance due to system losses

NASA Technical Reports Server (NTRS)

Webster, L.

1976-01-01

A model for predicting the computational performance of a maximum likelihood convolutional decoder (MCD) operating in a noisy carrier reference environment is described. This model is used to develop a subroutine that will be utilized by the Telemetry Analysis Program to compute the MCD bit error rate. When this computational model is averaged over noisy reference phase errors using a high-rate interpolation scheme, the results are found to agree quite favorably with experimental measurements.

Depicting mass flow rate of R134a /LPG refrigerant through straight and helical coiled adiabatic capillary tubes of vapor compression refrigeration system using artificial neural network approach

NASA Astrophysics Data System (ADS)

Gill, Jatinder; Singh, Jagdev

2018-07-01

In this work, an experimental investigation is carried out with R134a and LPG refrigerant mixture for depicting mass flow rate through straight and helical coil adiabatic capillary tubes in a vapor compression refrigeration system. Various experiments were conducted under steady-state conditions, by changing capillary tube length, inner diameter, coil diameter and degree of subcooling. The results showed that mass flow rate through helical coil capillary tube was found lower than straight capillary tube by about 5-16%. Dimensionless correlation and Artificial Neural Network (ANN) models were developed to predict mass flow rate. It was found that dimensionless correlation and ANN model predictions agreed well with experimental results and brought out an absolute fraction of variance of 0.961 and 0.988, root mean square error of 0.489 and 0.275 and mean absolute percentage error of 4.75% and 2.31% respectively. The results suggested that ANN model shows better statistical prediction than dimensionless correlation model.

Determination of reaeration-rate coefficients of the Wabash River, Indiana, by the modified tracer technique

USGS Publications Warehouse

Crawford, Charles G.

1985-01-01

The modified tracer technique was used to determine reaeration-rate coefficients in the Wabash River in reaches near Lafayette and Terre Haute, Indiana, at streamflows ranging from 2,310 to 7,400 cu ft/sec. Chemically pure (CP grade) ethylene was used as the tracer gas, and rhodamine-WT dye was used as the dispersion-dilution tracer. Reaeration coefficients determined for a 13.5-mi reach near Terre Haute, Indiana, at streamflows of 3,360 and 7,400 cu ft/sec (71% and 43% flow duration) were 1.4/day and 1.1/day at 20 C, respectively. Reaeration-rate coefficients determined for a 18.4-mile reach near Lafayette, Indiana, at streamflows of 2,310 and 3,420 cu ft/sec (70% and 53 % flow duration), were 1.2/day and 0.8/day at 20 C, respectively. None of the commonly used equations found in the literature predicted reaeration-rate coefficients similar to those measured for reaches of the Wabash River near Lafayette and Terre Haute. The average absolute prediction error for 10 commonly used reaeration equations ranged from 22% to 154%. Prediction error was much smaller in the reach near Terre Haute than in the reach near Lafayette. The overall average of the absolute prediction error for all 10 equations was 22% for the reach near Terre Haute and 128% for the reach near Lafayette. Confidence limits of results obtained from the modified tracer technique were smaller than those obtained from the equations in the literature. 

An experiment in software reliability: Additional analyses using data from automated replications

NASA Technical Reports Server (NTRS)

Dunham, Janet R.; Lauterbach, Linda A.

1988-01-01

A study undertaken to collect software error data of laboratory quality for use in the development of credible methods for predicting the reliability of software used in life-critical applications is summarized. The software error data reported were acquired through automated repetitive run testing of three independent implementations of a launch interceptor condition module of a radar tracking problem. The results are based on 100 test applications to accumulate a sufficient sample size for error rate estimation. The data collected is used to confirm the results of two Boeing studies reported in NASA-CR-165836 Software Reliability: Repetitive Run Experimentation and Modeling, and NASA-CR-172378 Software Reliability: Additional Investigations into Modeling With Replicated Experiments, respectively. That is, the results confirm the log-linear pattern of software error rates and reject the hypothesis of equal error rates per individual fault. This rejection casts doubt on the assumption that the program's failure rate is a constant multiple of the number of residual bugs; an assumption which underlies some of the current models of software reliability. data raises new questions concerning the phenomenon of interacting faults.

Time Course of Visual Extrapolation Accuracy

DTIC Science & Technology

1995-09-01

The pond and duckweed problem: Three experiments on the misperception of exponential growth . Acta Psychologica 43, 239-251. Wiener, E.L., 1962...random variation in tracker velocity. Both models predicted changes in hit and false alarm rates well, except in a condition where response asymmetries...systematic velocity error in tracking, only random variation in tracker velocity. Both models predicted changes in hit and false alarm rates well

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

Short-term prediction of rain attenuation level and volatility in Earth-to-Satellite links at EHF band

NASA Astrophysics Data System (ADS)

de Montera, L.; Mallet, C.; Barthès, L.; Golé, P.

2008-08-01

This paper shows how nonlinear models originally developed in the finance field can be used to predict rain attenuation level and volatility in Earth-to-Satellite links operating at the Extremely High Frequencies band (EHF, 20 50 GHz). A common approach to solving this problem is to consider that the prediction error corresponds only to scintillations, whose variance is assumed to be constant. Nevertheless, this assumption does not seem to be realistic because of the heteroscedasticity of error time series: the variance of the prediction error is found to be time-varying and has to be modeled. Since rain attenuation time series behave similarly to certain stocks or foreign exchange rates, a switching ARIMA/GARCH model was implemented. The originality of this model is that not only the attenuation level, but also the error conditional distribution are predicted. It allows an accurate upper-bound of the future attenuation to be estimated in real time that minimizes the cost of Fade Mitigation Techniques (FMT) and therefore enables the communication system to reach a high percentage of availability. The performance of the switching ARIMA/GARCH model was estimated using a measurement database of the Olympus satellite 20/30 GHz beacons and this model is shown to outperform significantly other existing models. The model also includes frequency scaling from the downlink frequency to the uplink frequency. The attenuation effects (gases, clouds and rain) are first separated with a neural network and then scaled using specific scaling factors. As to the resulting uplink prediction error, the error contribution of the frequency scaling step is shown to be larger than that of the downlink prediction, indicating that further study should focus on improving the accuracy of the scaling factor.

Adaptive intercolor error prediction coder for lossless color (rgb) picutre compression

NASA Astrophysics Data System (ADS)

Mann, Y.; Peretz, Y.; Mitchell, Harvey B.

2001-09-01

Most of the current lossless compression algorithms, including the new international baseline JPEG-LS algorithm, do not exploit the interspectral correlations that exist between the color planes in an input color picture. To improve the compression performance (i.e., lower the bit rate) it is necessary to exploit these correlations. A major concern is to find efficient methods for exploiting the correlations that, at the same time, are compatible with and can be incorporated into the JPEG-LS algorithm. One such algorithm is the method of intercolor error prediction (IEP), which when used with the JPEG-LS algorithm, results on average in a reduction of 8% in the overall bit rate. We show how the IEP algorithm can be simply modified and that it nearly doubles the size of the reduction in bit rate to 15%.

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.

Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

NASA Astrophysics Data System (ADS)

Das, Debashish; Safa Sadiq, Ali; Mirjalili, Seyedali; Noraziah, A.

2017-09-01

Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

Experimental investigation of false positive errors in auditory species occurrence surveys

USGS Publications Warehouse

Miller, David A.W.; Weir, Linda A.; McClintock, Brett T.; Grant, Evan H. Campbell; Bailey, Larissa L.; Simons, Theodore R.

2012-01-01

False positive errors are a significant component of many ecological data sets, which in combination with false negative errors, can lead to severe biases in conclusions about ecological systems. We present results of a field experiment where observers recorded observations for known combinations of electronically broadcast calling anurans under conditions mimicking field surveys to determine species occurrence. Our objectives were to characterize false positive error probabilities for auditory methods based on a large number of observers, to determine if targeted instruction could be used to reduce false positive error rates, and to establish useful predictors of among-observer and among-species differences in error rates. We recruited 31 observers, ranging in abilities from novice to expert, that recorded detections for 12 species during 180 calling trials (66,960 total observations). All observers made multiple false positive errors and on average 8.1% of recorded detections in the experiment were false positive errors. Additional instruction had only minor effects on error rates. After instruction, false positive error probabilities decreased by 16% for treatment individuals compared to controls with broad confidence interval overlap of 0 (95% CI: -46 to 30%). This coincided with an increase in false negative errors due to the treatment (26%; -3 to 61%). Differences among observers in false positive and in false negative error rates were best predicted by scores from an online test and a self-assessment of observer ability completed prior to the field experiment. In contrast, years of experience conducting call surveys was a weak predictor of error rates. False positive errors were also more common for species that were played more frequently, but were not related to the dominant spectral frequency of the call. Our results corroborate other work that demonstrates false positives are a significant component of species occurrence data collected by auditory methods. Instructing observers to only report detections they are completely certain are correct is not sufficient to eliminate errors. As a result, analytical methods that account for false positive errors will be needed, and independent testing of observer ability is a useful predictor for among-observer variation in observation error rates.

Notes on Accuracy of Finite-Volume Discretization Schemes on Irregular Grids

NASA Technical Reports Server (NTRS)

Diskin, Boris; Thomas, James L.

2011-01-01

Truncation-error analysis is a reliable tool in predicting convergence rates of discretization errors on regular smooth grids. However, it is often misleading in application to finite-volume discretization schemes on irregular (e.g., unstructured) grids. Convergence of truncation errors severely degrades on general irregular grids; a design-order convergence can be achieved only on grids with a certain degree of geometric regularity. Such degradation of truncation-error convergence does not necessarily imply a lower-order convergence of discretization errors. In these notes, irregular-grid computations demonstrate that the design-order discretization-error convergence can be achieved even when truncation errors exhibit a lower-order convergence or, in some cases, do not converge at all.

The influence of orbit selection on the accuracy of the Stanford Relativity gyroscope experiment

NASA Technical Reports Server (NTRS)

Vassar, R.; Everitt, C. W. F.; Vanpatten, R. A.; Breakwell, J. V.

1980-01-01

This paper discusses an error analysis for the Stanford Relativity experiment, designed to measure the precession of a gyroscope's spin-axis predicted by general relativity. Measurements will be made of the spin-axis orientations of 4 superconducting spherical gyroscopes carried by an earth-satellite. Two relativistic precessions are predicted: a 'geodetic' precession associated with the satellite's orbital motion and a 'motional' precession due to the earth's rotation. Using a Kalman filter covariance analysis with a realistic error model we have computed the error in determining the relativistic precession rates. Studies show that a slightly off-polar orbit is better than a polar orbit for determining the 'motional' drift.

Advanced error-prediction LDPC with temperature compensation for highly reliable SSDs

NASA Astrophysics Data System (ADS)

Tokutomi, Tsukasa; Tanakamaru, Shuhei; Iwasaki, Tomoko Ogura; Takeuchi, Ken

2015-09-01

To improve the reliability of NAND Flash memory based solid-state drives (SSDs), error-prediction LDPC (EP-LDPC) has been proposed for multi-level-cell (MLC) NAND Flash memory (Tanakamaru et al., 2012, 2013), which is effective for long retention times. However, EP-LDPC is not as effective for triple-level cell (TLC) NAND Flash memory, because TLC NAND Flash has higher error rates and is more sensitive to program-disturb error. Therefore, advanced error-prediction LDPC (AEP-LDPC) has been proposed for TLC NAND Flash memory (Tokutomi et al., 2014). AEP-LDPC can correct errors more accurately by precisely describing the error phenomena. In this paper, the effects of AEP-LDPC are investigated in a 2×nm TLC NAND Flash memory with temperature characterization. Compared with LDPC-with-BER-only, the SSD's data-retention time is increased by 3.4× and 9.5× at room-temperature (RT) and 85 °C, respectively. Similarly, the acceptable BER is increased by 1.8× and 2.3×, respectively. Moreover, AEP-LDPC can correct errors with pre-determined tables made at higher temperatures to shorten the measurement time before shipping. Furthermore, it is found that one table can cover behavior over a range of temperatures in AEP-LDPC. As a result, the total table size can be reduced to 777 kBytes, which makes this approach more practical.

Computational substrates of norms and their violations during social exchange.

PubMed

Xiang, Ting; Lohrenz, Terry; Montague, P Read

2013-01-16

Social norms in humans constrain individual behaviors to establish shared expectations within a social group. Previous work has probed social norm violations and the feelings that such violations engender; however, a computational rendering of the underlying neural and emotional responses has been lacking. We probed norm violations using a two-party, repeated fairness game (ultimatum game) where proposers offer a split of a monetary resource to a responder who either accepts or rejects the offer. Using a norm-training paradigm where subject groups are preadapted to either high or low offers, we demonstrate that unpredictable shifts in expected offers creates a difference in rejection rates exhibited by the two responder groups for otherwise identical offers. We constructed an ideal observer model that identified neural correlates of norm prediction errors in the ventral striatum and anterior insula, regions that also showed strong responses to variance-prediction errors generated by the same model. Subjective feelings about offers correlated with these norm prediction errors, and the two signals displayed overlapping, but not identical, neural correlates in striatum, insula, and medial orbitofrontal cortex. These results provide evidence for the hypothesis that responses in anterior insula can encode information about social norm violations that correlate with changes in overt behavior (changes in rejection rates). Together, these results demonstrate that the brain regions involved in reward prediction and risk prediction are also recruited in signaling social norm violations.

Computational Substrates of Norms and Their Violations during Social Exchange

PubMed Central

Xiang, Ting; Lohrenz, Terry; Montague, P. Read

2013-01-01

Social norms in humans constrain individual behaviors to establish shared expectations within a social group. Previous work has probed social norm violations and the feelings that such violations engender; however, a computational rendering of the underlying neural and emotional responses has been lacking. We probed norm violations using a two-party, repeated fairness game (ultimatum game) where proposers offer a split of a monetary resource to a responder who either accepts or rejects the offer. Using a norm-training paradigm where subject groups are preadapted to either high or low offers, we demonstrate that unpredictable shifts in expected offers creates a difference in rejection rates exhibited by the two responder groups for otherwise identical offers. We constructed an ideal observer model that identified neural correlates of norm prediction errors in the ventral striatum and anterior insula, regions that also showed strong responses to variance-prediction errors generated by the same model. Subjective feelings about offers correlated with these norm prediction errors, and the two signals displayed overlapping, but not identical, neural correlates in striatum, insula, and medial orbitofrontal cortex. These results provide evidence for the hypothesis that responses in anterior insula can encode information about social norm violations that correlate with changes in overt behavior (changes in rejection rates). Together, these results demonstrate that the brain regions involved in reward prediction and risk prediction are also recruited in signaling social norm violations. PMID:23325247

Effects of Stopping Ions and LET Fluctuations on Soft Error Rate Prediction.

DOE PAGES

Weeden-Wright, S. L.; King, Michael Patrick; Hooten, N. C.; ...

2015-02-01

Variability in energy deposition from stopping ions and LET fluctuations is quantified for specific radiation environments. When compared to predictions using average LET via CREME96, LET fluctuations lead to an order-of-magnitude difference in effective flux and a nearly 4x decrease in predicted soft error rate (SER) in an example calculation performed on a commercial 65 nm SRAM. The large LET fluctuations reported here will be even greater for the smaller sensitive volumes that are characteristic of highly scaled technologies. End-of-range effects of stopping ions do not lead to significant inaccuracies in radiation environments with low solar activity unless the sensitivevolumemore » thickness is 100 μm or greater. In contrast, end-of-range effects for stopping ions lead to significant inaccuracies for sensitive- volume thicknesses less than 10 μm in radiation environments with high solar activity.« less

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate.

PubMed

Puntel, Laila A; Sawyer, John E; Barker, Daniel W; Thorburn, Peter J; Castellano, Michael J; Moore, Kenneth J; VanLoocke, Andrew; Heaton, Emily A; Archontoulis, Sotirios V

2018-01-01

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time ( R 2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity ( R 2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined ( n = 31) with an average error range of ±38 kg N ha -1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

PubMed Central

Puntel, Laila A.; Sawyer, John E.; Barker, Daniel W.; Thorburn, Peter J.; Castellano, Michael J.; Moore, Kenneth J.; VanLoocke, Andrew; Heaton, Emily A.; Archontoulis, Sotirios V.

2018-01-01

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha−1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost. PMID:29706974

Using Word Prediction Software to Increase Typing Fluency with Students with Physical Disabilities

ERIC Educational Resources Information Center

Tumlin, Jennifer; Heller, Kathryn Wolff

2004-01-01

The purpose of this study was to examine the use of word prediction software to increase typing speed and decrease spelling errors for students who have physical disabilities that affect hand use. Student perceptions regarding the effectiveness of word prediction was examined as well as their typing rates and spelling accuracy. Four students with…

Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory

PubMed Central

Tao, Qing

2017-01-01

Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM. PMID:29391864

Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory.

PubMed

Yang, Haimin; Pan, Zhisong; Tao, Qing

2017-01-01

Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.

Study of Uncertainties of Predicting Space Shuttle Thermal Environment. [impact of heating rate prediction errors on weight of thermal protection system

NASA Technical Reports Server (NTRS)

Fehrman, A. L.; Masek, R. V.

1972-01-01

Quantitative estimates of the uncertainty in predicting aerodynamic heating rates for a fully reusable space shuttle system are developed and the impact of these uncertainties on Thermal Protection System (TPS) weight are discussed. The study approach consisted of statistical evaluations of the scatter of heating data on shuttle configurations about state-of-the-art heating prediction methods to define the uncertainty in these heating predictions. The uncertainties were then applied as heating rate increments to the nominal predicted heating rate to define the uncertainty in TPS weight. Separate evaluations were made for the booster and orbiter, for trajectories which included boost through reentry and touchdown. For purposes of analysis, the vehicle configuration is divided into areas in which a given prediction method is expected to apply, and separate uncertainty factors and corresponding uncertainty in TPS weight derived for each area.

Metacognition and proofreading: the roles of aging, motivation, and interest.

PubMed

Hargis, Mary B; Yue, Carole L; Kerr, Tyson; Ikeda, Kenji; Murayama, Kou; Castel, Alan D

2017-03-01

The current study examined younger and older adults' error detection accuracy, prediction calibration, and postdiction calibration on a proofreading task, to determine if age-related differences would be present in this type of common error detection task. Participants were given text passages, and were first asked to predict the percentage of errors they would detect in the passage. They then read the passage and circled errors (which varied in complexity and locality), and made postdictions regarding their performance, before repeating this with another passage and answering a comprehension test of both passages. There were no age-related differences in error detection accuracy, text comprehension, or metacognitive calibration, though participants in both age groups were overconfident overall in their metacognitive judgments. Both groups gave similar ratings of motivation to complete the task. The older adults rated the passages as more interesting than younger adults did, although this level of interest did not appear to influence error-detection performance. The age equivalence in both proofreading ability and calibration suggests that the ability to proofread text passages and the associated metacognitive monitoring used in judging one's own performance are maintained in aging. These age-related similarities persisted when younger adults completed the proofreading tasks on a computer screen, rather than with paper and pencil. The findings provide novel insights regarding the influence that cognitive aging may have on metacognitive accuracy and text processing in an everyday task.

Comparisons of single event vulnerability of GaAs SRAMS

NASA Astrophysics Data System (ADS)

Weatherford, T. R.; Hauser, J. R.; Diehl, S. E.

1986-12-01

A GaAs MESFET/JFET model incorporated into SPICE has been used to accurately describe C-EJFET, E/D MESFET and D MESFET/resistor GaAs memory technologies. These cells have been evaluated for critical charges due to gate-to-drain and drain-to-source charge collection. Low gate-to-drain critical charges limit conventional GaAs SRAM soft error rates to approximately 1E-6 errors/bit-day. SEU hardening approaches including decoupling resistors, diodes, and FETs have been investigated. Results predict GaAs RAM cell critical charges can be increased to over 0.1 pC. Soft error rates in such hardened memories may approach 1E-7 errors/bit-day without significantly reducing memory speed. Tradeoffs between hardening level, performance and fabrication complexity are discussed.

Sleep quality, but not quantity, is associated with self-perceived minor error rates among emergency department nurses.

PubMed

Weaver, Amy L; Stutzman, Sonja E; Supnet, Charlene; Olson, DaiWai M

2016-03-01

The emergency department (ED) is demanding and high risk. The impact of sleep quantity has been hypothesized to impact patient care. This study investigated the hypothesis that fatigue and impaired mentation, due to sleep disturbance and shortened overall sleeping hours, would lead to increased nursing errors. This is a prospective observational study of 30 ED nurses using self-administered survey and sleep architecture measured by wrist actigraphy as predictors of self-reported error rates. An actigraphy device was worn prior to working a 12-hour shift and nurses completed the Pittsburgh Sleep Quality Index (PSQI). Error rates were reported on a visual analog scale at the end of a 12-hour shift. The PSQI responses indicated that 73.3% of subjects had poor sleep quality. Lower sleep quality measured by actigraphy (hours asleep/hours in bed) was associated with higher self-perceived minor errors. Sleep quantity (total hours slept) was not associated with minor, moderate, nor severe errors. Our study found that ED nurses' sleep quality, immediately prior to a working 12-hour shift, is more predictive of error than sleep quantity. These results present evidence that a "good night's sleep" prior to working a nursing shift in the ED is beneficial for reducing minor errors. Copyright © 2016 Elsevier Ltd. All rights reserved.

Parallel photonic information processing at gigabyte per second data rates using transient states

NASA Astrophysics Data System (ADS)

Brunner, Daniel; Soriano, Miguel C.; Mirasso, Claudio R.; Fischer, Ingo

2013-01-01

The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

Generalized additive models and Lucilia sericata growth: assessing confidence intervals and error rates in forensic entomology.

PubMed

Tarone, Aaron M; Foran, David R

2008-07-01

Forensic entomologists use blow fly development to estimate a postmortem interval. Although accurate, fly age estimates can be imprecise for older developmental stages and no standard means of assigning confidence intervals exists. Presented here is a method for modeling growth of the forensically important blow fly Lucilia sericata, using generalized additive models (GAMs). Eighteen GAMs were created to predict the extent of juvenile fly development, encompassing developmental stage, length, weight, strain, and temperature data, collected from 2559 individuals. All measures were informative, explaining up to 92.6% of the deviance in the data, though strain and temperature exerted negligible influences. Predictions made with an independent data set allowed for a subsequent examination of error. Estimates using length and developmental stage were within 5% of true development percent during the feeding portion of the larval life cycle, while predictions for postfeeding third instars were less precise, but within expected error.

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

NASA Astrophysics Data System (ADS)

Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

2013-09-01

This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

Development of Predictive Models for the Growth Kinetics of Listeria monocytogenes on Fresh Pork under Different Storage Temperatures.

PubMed

Luo, Ke; Hong, Sung-Sam; Wang, Jun; Chung, Mi-Ja; Deog-Hwan, Oh

2015-05-01

This study was conducted to develop a predictive model to estimate the growth of Listeria monocytogenes on fresh pork during storage at constant temperatures (5, 10, 15, 20, 25, 30, and 35°C). The Baranyi model was fitted to growth data (log CFU per gram) to calculate the specific growth rate (SGR) and lag time (LT) with a high coefficient of determination (R(2) > 0.98). As expected, SGR increased with a decline in LT with rising temperatures in all samples. Secondary models were then developed to describe the variation of SGR and LT as a function of temperature. Subsequently, the developed models were validated with additional independent growth data collected at 7, 17, 27, and 37°C and from published reports using proportion of relative errors and proportion of standard error of prediction. The proportion of relative errors of the SGR and LT models developed herein were 0.79 and 0.18, respectively. In addition, the standard error of prediction values of the SGR and LT of L. monocytogenes ranged from 25.7 to 33.1% and from 44.92 to 58.44%, respectively. These results suggest that the model developed in this study was capable of predicting the growth of L. monocytogenes under various isothermal conditions.

Digital Troposcatter Performance Model. Users Manual.

DTIC Science & Technology

1983-11-01

and Information Systems - .,- - - UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE (When Data Entered) S REPORT DOCUIAENTATION PAGE READ...Diffraction Multipath Prediction MD-918 Modem Error Rate Prediction AN/TRC-170 Link Analysis 20. ABSTRACT (Continue en reverse esie If neceseay end...configurations used in the Defense Communications System (DCS), and prediction of the performance of both the MD-918 and AN/TRC-170 digital troposcatter modems

Space-Time Earthquake Prediction: The Error Diagrams

NASA Astrophysics Data System (ADS)

Molchan, G.

2010-08-01

The quality of earthquake prediction is usually characterized by a two-dimensional diagram n versus τ, where n is the rate of failures-to-predict and τ is a characteristic of space-time alarm. Unlike the time prediction case, the quantity τ is not defined uniquely. We start from the case in which τ is a vector with components related to the local alarm times and find a simple structure of the space-time diagram in terms of local time diagrams. This key result is used to analyze the usual 2-d error sets { n, τ w } in which τ w is a weighted mean of the τ components and w is the weight vector. We suggest a simple algorithm to find the ( n, τ w ) representation of all random guess strategies, the set D, and prove that there exists the unique case of w when D degenerates to the diagonal n + τ w = 1. We find also a confidence zone of D on the ( n, τ w ) plane when the local target rates are known roughly. These facts are important for correct interpretation of ( n, τ w ) diagrams when we discuss the prediction capability of the data or prediction methods.

Feedforward compensation for novel dynamics depends on force field orientation but is similar for the left and right arms.

PubMed

Reuter, Eva-Maria; Cunnington, Ross; Mattingley, Jason B; Riek, Stephan; Carroll, Timothy J

2016-11-01

There are well-documented differences in the way that people typically perform identical motor tasks with their dominant and the nondominant arms. According to Yadav and Sainburg's (Neuroscience 196: 153-167, 2011) hybrid-control model, this is because the two arms rely to different degrees on impedance control versus predictive control processes. Here, we assessed whether differences in limb control mechanisms influence the rate of feedforward compensation to a novel dynamic environment. Seventy-five healthy, right-handed participants, divided into four subsamples depending on the arm (left, right) and direction of the force field (ipsilateral, contralateral), reached to central targets in velocity-dependent curl force fields. We assessed the rate at which participants developed predictive compensation for the force field using intermittent error-clamp trials and assessed both kinematic errors and initial aiming angles in the field trials. Participants who were exposed to fields that pushed the limb toward ipsilateral space reduced kinematic errors more slowly, built up less predictive field compensation, and relied more on strategic reaiming than those exposed to contralateral fields. However, there were no significant differences in predictive field compensation or kinematic errors between limbs, suggesting that participants using either the left or the right arm could adapt equally well to novel dynamics. It therefore appears that the distinct preferences in control mechanisms typically observed for the dominant and nondominant arms reflect a default mode that is based on habitual functional requirements rather than an absolute limit in capacity to access the controller specialized for the opposite limb. Copyright © 2016 the American Physiological Society.

Feedforward compensation for novel dynamics depends on force field orientation but is similar for the left and right arms

PubMed Central

Cunnington, Ross; Mattingley, Jason B.; Riek, Stephan; Carroll, Timothy J.

2016-01-01

There are well-documented differences in the way that people typically perform identical motor tasks with their dominant and the nondominant arms. According to Yadav and Sainburg's (Neuroscience 196: 153–167, 2011) hybrid-control model, this is because the two arms rely to different degrees on impedance control versus predictive control processes. Here, we assessed whether differences in limb control mechanisms influence the rate of feedforward compensation to a novel dynamic environment. Seventy-five healthy, right-handed participants, divided into four subsamples depending on the arm (left, right) and direction of the force field (ipsilateral, contralateral), reached to central targets in velocity-dependent curl force fields. We assessed the rate at which participants developed predictive compensation for the force field using intermittent error-clamp trials and assessed both kinematic errors and initial aiming angles in the field trials. Participants who were exposed to fields that pushed the limb toward ipsilateral space reduced kinematic errors more slowly, built up less predictive field compensation, and relied more on strategic reaiming than those exposed to contralateral fields. However, there were no significant differences in predictive field compensation or kinematic errors between limbs, suggesting that participants using either the left or the right arm could adapt equally well to novel dynamics. It therefore appears that the distinct preferences in control mechanisms typically observed for the dominant and nondominant arms reflect a default mode that is based on habitual functional requirements rather than an absolute limit in capacity to access the controller specialized for the opposite limb. PMID:27582293

Mitigating errors caused by interruptions during medication verification and administration: interventions in a simulated ambulatory chemotherapy setting.

PubMed

Prakash, Varuna; Koczmara, Christine; Savage, Pamela; Trip, Katherine; Stewart, Janice; McCurdie, Tara; Cafazzo, Joseph A; Trbovich, Patricia

2014-11-01

Nurses are frequently interrupted during medication verification and administration; however, few interventions exist to mitigate resulting errors, and the impact of these interventions on medication safety is poorly understood. The study objectives were to (A) assess the effects of interruptions on medication verification and administration errors, and (B) design and test the effectiveness of targeted interventions at reducing these errors. The study focused on medication verification and administration in an ambulatory chemotherapy setting. A simulation laboratory experiment was conducted to determine interruption-related error rates during specific medication verification and administration tasks. Interventions to reduce these errors were developed through a participatory design process, and their error reduction effectiveness was assessed through a postintervention experiment. Significantly more nurses committed medication errors when interrupted than when uninterrupted. With use of interventions when interrupted, significantly fewer nurses made errors in verifying medication volumes contained in syringes (16/18; 89% preintervention error rate vs 11/19; 58% postintervention error rate; p=0.038; Fisher's exact test) and programmed in ambulatory pumps (17/18; 94% preintervention vs 11/19; 58% postintervention; p=0.012). The rate of error commission significantly decreased with use of interventions when interrupted during intravenous push (16/18; 89% preintervention vs 6/19; 32% postintervention; p=0.017) and pump programming (7/18; 39% preintervention vs 1/19; 5% postintervention; p=0.017). No statistically significant differences were observed for other medication verification tasks. Interruptions can lead to medication verification and administration errors. Interventions were highly effective at reducing unanticipated errors of commission in medication administration tasks, but showed mixed effectiveness at reducing predictable errors of detection in medication verification tasks. These findings can be generalised and adapted to mitigate interruption-related errors in other settings where medication verification and administration are required. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Mitigating errors caused by interruptions during medication verification and administration: interventions in a simulated ambulatory chemotherapy setting

PubMed Central

Prakash, Varuna; Koczmara, Christine; Savage, Pamela; Trip, Katherine; Stewart, Janice; McCurdie, Tara; Cafazzo, Joseph A; Trbovich, Patricia

2014-01-01

Background Nurses are frequently interrupted during medication verification and administration; however, few interventions exist to mitigate resulting errors, and the impact of these interventions on medication safety is poorly understood. Objective The study objectives were to (A) assess the effects of interruptions on medication verification and administration errors, and (B) design and test the effectiveness of targeted interventions at reducing these errors. Methods The study focused on medication verification and administration in an ambulatory chemotherapy setting. A simulation laboratory experiment was conducted to determine interruption-related error rates during specific medication verification and administration tasks. Interventions to reduce these errors were developed through a participatory design process, and their error reduction effectiveness was assessed through a postintervention experiment. Results Significantly more nurses committed medication errors when interrupted than when uninterrupted. With use of interventions when interrupted, significantly fewer nurses made errors in verifying medication volumes contained in syringes (16/18; 89% preintervention error rate vs 11/19; 58% postintervention error rate; p=0.038; Fisher's exact test) and programmed in ambulatory pumps (17/18; 94% preintervention vs 11/19; 58% postintervention; p=0.012). The rate of error commission significantly decreased with use of interventions when interrupted during intravenous push (16/18; 89% preintervention vs 6/19; 32% postintervention; p=0.017) and pump programming (7/18; 39% preintervention vs 1/19; 5% postintervention; p=0.017). No statistically significant differences were observed for other medication verification tasks. Conclusions Interruptions can lead to medication verification and administration errors. Interventions were highly effective at reducing unanticipated errors of commission in medication administration tasks, but showed mixed effectiveness at reducing predictable errors of detection in medication verification tasks. These findings can be generalised and adapted to mitigate interruption-related errors in other settings where medication verification and administration are required. PMID:24906806

The calculation of average error probability in a digital fibre optical communication system

NASA Astrophysics Data System (ADS)

Rugemalira, R. A. M.

1980-03-01

This paper deals with the problem of determining the average error probability in a digital fibre optical communication system, in the presence of message dependent inhomogeneous non-stationary shot noise, additive Gaussian noise and intersymbol interference. A zero-forcing equalization receiver filter is considered. Three techniques for error rate evaluation are compared. The Chernoff bound and the Gram-Charlier series expansion methods are compared to the characteristic function technique. The latter predicts a higher receiver sensitivity

Multiple diagnosis based on photoplethysmography: hematocrit, SpO2, pulse, and respiration

NASA Astrophysics Data System (ADS)

Yoon, Gilwon; Lee, Jong Y.; Jeon, Kye Jin; Park, Kun-Kook; Yeo, Hyung S.; Hwang, Hyun T.; Kim, Hong S.; Hwang, In-Duk

2002-09-01

Photo-plethysmography measures pulsatile blood flow in real-time and non-invasively. One of widely known applications of PPG is the measurement of saturated oxygen in arterial blood(SpO2). In our work, using several wavelengths more than those used in a pulse oximeter, an algorithm and instrument have been developed to measure hematocrit, saturated oxygen, pulse and respiratory rates simultaneously. To predict hematocrit, a dedicated algorithm is developed based on scattering of RBC and a protocol for detecting outlier signals is used to increase accuracy and reliability. Digital filtering techniques are used to extract respiratory rate signals. Utilization of wavelengths under 1000nm and a multi-wavelength LED array chip and digital-oriented electronics enable us to make a compact device. Our preliminary clinical trials show that the achieved percent errors are +/-8.2% for hematocrit when tested with 594 persons, R2 for SpO2 fitting is 0.99985 when tested with a Bi-Tek pulse oximeter simulator and the SpO2 error for in vivo test is +/-2.5% over the range of 75~100%. The error of pulse rates is less than +/-5%. We obtained a positive predictive value of 96% for respiratory rates in qualitative analysis.

MO-FG-202-09: Virtual IMRT QA Using Machine Learning: A Multi-Institutional Validation

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

Valdes, G; Scheuermann, R; Solberg, T

Purpose: To validate a machine learning approach to Virtual IMRT QA for accurately predicting gamma passing rates using different QA devices at different institutions. Methods: A Virtual IMRT QA was constructed using a machine learning algorithm based on 416 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3mm with 10% threshold. An independent set of 139 IMRT measurements from a different institution, with QA data based on portal dosimetry using the same gamma index and 10% threshold, was used to further test the algorithm. Plans were characterized by 90 different complexity metrics. A weighted poisonmore » regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input. Results: In addition to predicting passing rates with 3% accuracy for all composite plans using diode-array detectors, passing rates for portal dosimetry on per-beam basis were predicted with an error <3.5% for 120 IMRT measurements. The remaining measurements (19) had large areas of low CU, where portal dosimetry has larger disagreement with the calculated dose and, as such, large errors were expected. These beams need to be further modeled to correct the under-response in low dose regions. Important features selected by Lasso to predict gamma passing rates were: complete irradiated area outline (CIAO) area, jaw position, fraction of MLC leafs with gaps smaller than 20 mm or 5mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted Average Irregularity Factor, duty cycle among others. Conclusion: We have demonstrated that the Virtual IMRT QA can predict passing rates using different QA devices and across multiple institutions. Prediction of QA passing rates could have profound implications on the current IMRT process.« less

Transcriptional fidelities of human mitochondrial POLRMT, yeast mitochondrial Rpo41, and phage T7 single-subunit RNA polymerases.

PubMed

Sultana, Shemaila; Solotchi, Mihai; Ramachandran, Aparna; Patel, Smita S

2017-11-03

Single-subunit RNA polymerases (RNAPs) are present in phage T7 and in mitochondria of all eukaryotes. This RNAP class plays important roles in biotechnology and cellular energy production, but we know little about its fidelity and error rates. Herein, we report the error rates of three single-subunit RNAPs measured from the catalytic efficiencies of correct and all possible incorrect nucleotides. The average error rates of T7 RNAP (2 × 10 -6 ), yeast mitochondrial Rpo41 (6 × 10 -6 ), and human mitochondrial POLRMT (RNA polymerase mitochondrial) (2 × 10 -5 ) indicate high accuracy/fidelity of RNA synthesis resembling those of replicative DNA polymerases. All three RNAPs exhibit a distinctly high propensity for GTP misincorporation opposite dT, predicting frequent A→G errors in RNA with rates of ∼10 -4 The A→C, G→A, A→U, C→U, G→U, U→C, and U→G errors mostly due to pyrimidine-purine mismatches were relatively frequent (10 -5 -10 -6 ), whereas C→G, U→A, G→C, and C→A errors from purine-purine and pyrimidine-pyrimidine mismatches were rare (10 -7 -10 -10 ). POLRMT also shows a high C→A error rate on 8-oxo-dG templates (∼10 -4 ). Strikingly, POLRMT shows a high mutagenic bypass rate, which is exacerbated by TEFM (transcription elongation factor mitochondrial). The lifetime of POLRMT on terminally mismatched elongation substrate is increased in the presence of TEFM, which allows POLRMT to efficiently bypass the error and continue with transcription. This investigation of nucleotide selectivity on normal and oxidatively damaged DNA by three single-subunit RNAPs provides the basic information to understand the error rates in mitochondria and, in the case of T7 RNAP, to assess the quality of in vitro transcribed RNAs. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Two-Step Fair Scheduling of Continuous Media Streams over Error-Prone Wireless Channels

NASA Astrophysics Data System (ADS)

Oh, Soohyun; Lee, Jin Wook; Park, Taejoon; Jo, Tae-Chang

In wireless cellular networks, streaming of continuous media (with strict QoS requirements) over wireless links is challenging due to their inherent unreliability characterized by location-dependent, bursty errors. To address this challenge, we present a two-step scheduling algorithm for a base station to provide streaming of continuous media to wireless clients over the error-prone wireless links. The proposed algorithm is capable of minimizing the packet loss rate of individual clients in the presence of error bursts, by transmitting packets in the round-robin manner and also adopting a mechanism for channel prediction and swapping.

Adaptive control of theophylline therapy: importance of blood sampling times.

PubMed

D'Argenio, D Z; Khakmahd, K

1983-10-01

A two-observation protocol for estimating theophylline clearance during a constant-rate intravenous infusion is used to examine the importance of blood sampling schedules with regard to the information content of resulting concentration data. Guided by a theory for calculating maximally informative sample times, population simulations are used to assess the effect of specific sampling times on the precision of resulting clearance estimates and subsequent predictions of theophylline plasma concentrations. The simulations incorporated noise terms for intersubject variability, dosing errors, sample collection errors, and assay error. Clearance was estimated using Chiou's method, least squares, and a Bayesian estimation procedure. The results of these simulations suggest that clinically significant estimation and prediction errors may result when using the above two-point protocol for estimating theophylline clearance if the time separating the two blood samples is less than one population mean elimination half-life.

Verification of real-time WSA-ENLIL+Cone simulations of CME arrival-time at the CCMC from 2010 to 2016

NASA Astrophysics Data System (ADS)

Wold, Alexandra M.; Mays, M. Leila; Taktakishvili, Aleksandre; Jian, Lan K.; Odstrcil, Dusan; MacNeice, Peter

2018-03-01

The Wang-Sheeley-Arge (WSA)-ENLIL+Cone model is used extensively in space weather operations world-wide to model coronal mass ejection (CME) propagation. As such, it is important to assess its performance. We present validation results of the WSA-ENLIL+Cone model installed at the Community Coordinated Modeling Center (CCMC) and executed in real-time by the CCMC space weather team. CCMC uses the WSA-ENLIL+Cone model to predict CME arrivals at NASA missions throughout the inner heliosphere. In this work we compare model predicted CME arrival-times to in situ interplanetary coronal mass ejection leading edge measurements at Solar TErrestrial RElations Observatory-Ahead (STEREO-A), Solar TErrestrial RElations Observatory-Behind (STEREO-B), and Earth (Wind and ACE) for simulations completed between March 2010 and December 2016 (over 1,800 CMEs). We report hit, miss, false alarm, and correct rejection statistics for all three locations. For all predicted CME arrivals, the hit rate is 0.5, and the false alarm rate is 0.1. For the 273 events where the CME was predicted to arrive at Earth, STEREO-A, or STEREO-B, and was actually observed (hit event), the mean absolute arrival-time prediction error was 10.4 ± 0.9 h, with a tendency to early prediction error of -4.0 h. We show the dependence of the arrival-time error on CME input parameters. We also explore the impact of the multi-spacecraft observations used to initialize the model CME inputs by comparing model verification results before and after the STEREO-B communication loss (since September 2014) and STEREO-A sidelobe operations (August 2014-December 2015). There is an increase of 1.7 h in the CME arrival time error during single, or limited two-viewpoint periods, compared to the three-spacecraft viewpoint period. This trend would apply to a future space weather mission at L5 or L4 as another coronagraph viewpoint to reduce CME arrival time errors compared to a single L1 viewpoint.

Validity of pre and post-term birth rates based on the date of last menstrual period compared to early obstetric ultrasonography.

PubMed

Medeiros, Maria Nilza Lima; Cavalcante, Nádia Carenina Nunes; Mesquita, Fabrício José Alencar; Batista, Rosângela Lucena Fernandes; Simões, Vanda Maria Ferreira; Cavalli, Ricardo de Carvalho; Cardoso, Viviane Cunha; Bettiol, Heloisa; Barbieri, Marco Antonio; Silva, Antônio Augusto Moura da

2015-04-01

The aim of this study was to assess the validity of the last menstrual period (LMP) estimate in determining pre and post-term birth rates, in a prenatal cohort from two Brazilian cities, São Luís and Ribeirão Preto. Pregnant women with a single fetus and less than 20 weeks' gestation by obstetric ultrasonography who received prenatal care in 2010 and 2011 were included. The LMP was obtained on two occasions (at 22-25 weeks gestation and after birth). The sensitivity of LMP obtained prenatally to estimate the preterm birth rate was 65.6% in São Luís and 78.7% in Ribeirão Preto and the positive predictive value was 57.3% in São Luís and 73.3% in Ribeirão Preto. LMP errors in identifying preterm birth were lower in the more developed city, Ribeirão Preto. The sensitivity and positive predictive value of LMP for the estimate of the post-term birth rate was very low and tended to overestimate it. LMP can be used with some errors to identify the preterm birth rate when obstetric ultrasonography is not available, but is not suitable for predicting post-term birth.

Population Pharmacokinetics of Intravenous Paracetamol (Acetaminophen) in Preterm and Term Neonates: Model Development and External Evaluation.

PubMed

Cook, Sarah F; Roberts, Jessica K; Samiee-Zafarghandy, Samira; Stockmann, Chris; King, Amber D; Deutsch, Nina; Williams, Elaine F; Allegaert, Karel; Wilkins, Diana G; Sherwin, Catherine M T; van den Anker, John N

2016-01-01

The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Nonlinear mixed-effects models were constructed from paracetamol concentration-time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1-14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1-28.1). Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations.

Population Pharmacokinetics of Intravenous Paracetamol (Acetaminophen) in Preterm and Term Neonates: Model Development and External Evaluation

PubMed Central

Cook, Sarah F.; Roberts, Jessica K.; Samiee-Zafarghandy, Samira; Stockmann, Chris; King, Amber D.; Deutsch, Nina; Williams, Elaine F.; Allegaert, Karel; Sherwin, Catherine M. T.; van den Anker, John N.

2017-01-01

Objectives The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Methods Nonlinear mixed-effects models were constructed from paracetamol concentration–time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. Results The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1–14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1–28.1). Conclusions Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations. PMID:26201306

Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

PubMed Central

Doubková, Marcela; Van Dijk, Albert I.J.M.; Sabel, Daniel; Wagner, Wolfgang; Blöschl, Günter

2012-01-01

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling. PMID:23483015

Quantifying predictability variations in a low-order ocean-atmosphere model - A dynamical systems approach

NASA Technical Reports Server (NTRS)

Nese, Jon M.; Dutton, John A.

1993-01-01

The predictability of the weather and climatic states of a low-order moist general circulation model is quantified using a dynamic systems approach, and the effect of incorporating a simple oceanic circulation on predictability is evaluated. The predictability and the structure of the model attractors are compared using Liapunov exponents, local divergence rates, and the correlation and Liapunov dimensions. It was found that the activation of oceanic circulation increases the average error doubling time of the atmosphere and the coupled ocean-atmosphere system by 10 percent and decreases the variance of the largest local divergence rate by 20 percent. When an oceanic circulation develops, the average predictability of annually averaged states is improved by 25 percent and the variance of the largest local divergence rate decreases by 25 percent.

Error-Rate Bounds for Coded PPM on a Poisson Channel

NASA Technical Reports Server (NTRS)

Moision, Bruce; Hamkins, Jon

2009-01-01

Equations for computing tight bounds on error rates for coded pulse-position modulation (PPM) on a Poisson channel at high signal-to-noise ratio have been derived. These equations and elements of the underlying theory are expected to be especially useful in designing codes for PPM optical communication systems. The equations and the underlying theory apply, more specifically, to a case in which a) At the transmitter, a linear outer code is concatenated with an inner code that includes an accumulator and a bit-to-PPM-symbol mapping (see figure) [this concatenation is known in the art as "accumulate-PPM" (abbreviated "APPM")]; b) The transmitted signal propagates on a memoryless binary-input Poisson channel; and c) At the receiver, near-maximum-likelihood (ML) decoding is effected through an iterative process. Such a coding/modulation/decoding scheme is a variation on the concept of turbo codes, which have complex structures, such that an exact analytical expression for the performance of a particular code is intractable. However, techniques for accurately estimating the performances of turbo codes have been developed. The performance of a typical turbo code includes (1) a "waterfall" region consisting of a steep decrease of error rate with increasing signal-to-noise ratio (SNR) at low to moderate SNR, and (2) an "error floor" region with a less steep decrease of error rate with increasing SNR at moderate to high SNR. The techniques used heretofore for estimating performance in the waterfall region have differed from those used for estimating performance in the error-floor region. For coded PPM, prior to the present derivations, equations for accurate prediction of the performance of coded PPM at high SNR did not exist, so that it was necessary to resort to time-consuming simulations in order to make such predictions. The present derivation makes it unnecessary to perform such time-consuming simulations.

Unacceptably High Error Rates in Vitek 2 Testing of Cefepime Susceptibility in Extended-Spectrum-β-Lactamase-Producing Escherichia coli

PubMed Central

Rhodes, Nathaniel J.; Richardson, Chad L.; Heraty, Ryan; Liu, Jiajun; Malczynski, Michael; Qi, Chao

2014-01-01

While a lack of concordance is known between gold standard MIC determinations and Vitek 2, the magnitude of the discrepancy and its impact on treatment decisions for extended-spectrum-β-lactamase (ESBL)-producing Escherichia coli are not. Clinical isolates of ESBL-producing E. coli were collected from blood, tissue, and body fluid samples from January 2003 to July 2009. Resistance genotypes were identified by PCR. Primary analyses evaluated the discordance between Vitek 2 and gold standard methods using cefepime susceptibility breakpoint cutoff values of 8, 4, and 2 μg/ml. The discrepancies in MICs between the methods were classified per convention as very major, major, and minor errors. Sensitivity, specificity, and positive and negative predictive values for susceptibility classifications were calculated. A total of 304 isolates were identified; 59% (179) of the isolates carried blaCTX-M, 47% (143) carried blaTEM, and 4% (12) carried blaSHV. At a breakpoint MIC of 8 μg/ml, Vitek 2 produced a categorical agreement of 66.8% and exhibited very major, major, and minor error rates of 23% (20/87 isolates), 5.1% (8/157 isolates), and 24% (73/304), respectively. The sensitivity, specificity, and positive and negative predictive values for a susceptibility breakpoint of 8 μg/ml were 94.9%, 61.2%, 72.3%, and 91.8%, respectively. The sensitivity, specificity, and positive and negative predictive values for a susceptibility breakpoint of 2 μg/ml were 83.8%, 65.3%, 41%, and 93.3%, respectively. Vitek 2 results in unacceptably high error rates for cefepime compared to those of agar dilution for ESBL-producing E. coli. Clinicians should be wary of making treatment decisions on the basis of Vitek 2 susceptibility results for ESBL-producing E. coli. PMID:24752253

Prediction of body lipid change in pregnancy and lactation.

PubMed

Friggens, N C; Ingvartsen, K L; Emmans, G C

2004-04-01

A simple method to predict the genetically driven pattern of body lipid change through pregnancy and lactation in dairy cattle is proposed. The rationale and evidence for genetically driven body lipid change have their basis in evolutionary considerations and in the homeorhetic changes in lipid metabolism through the reproductive cycle. The inputs required to predict body lipid change are body lipid mass at calving (kg) and the date of conception (days in milk). Body lipid mass can be derived from body condition score and live weight. A key assumption is that there is a linear rate of change of the rate of body lipid change (dL/dt) between calving and a genetically determined time in lactation (T') at which a particular level of body lipid (L') is sought. A second assumption is that there is a linear rate of change of the rate of body lipid change (dL/dt) between T' and the next calving. The resulting model was evaluated using 2 sets of data. The first was from Holstein cows with 3 different levels of body fatness at calving. The second was from Jersey cows in first, second, and third parity. The model was found to reproduce the observed patterns of change in body lipid reserves through lactation in both data sets. The average error of prediction was low, less than the variation normally associated with the recording of condition score, and was similar for the 2 data sets. When the model was applied using the initially suggested parameter values derived from the literature the average error of prediction was 0.185 units of condition score (+/- 0.086 SD). After minor adjustments to the parameter values, the average error of prediction was 0.118 units of condition score (+/- 0.070 SD). The assumptions on which the model is based were sufficient to predict the changes in body lipid of both Holstein and Jersey cows under different nutritional conditions and parities. Thus, the model presented here shows that it is possible to predict genetically driven curves of body lipid change through lactation in a simple way that requires few parameters and inputs that can be derived in practice. It is expected that prediction of the cow's energy requirements can be substantially improved, particularly in early lactation, by incorporating a genetically driven body energy mobilization.

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.

Adaptive Trajectory Prediction Algorithm for Climbing Flights

NASA Technical Reports Server (NTRS)

Schultz, Charles Alexander; Thipphavong, David P.; Erzberger, Heinz

2012-01-01

Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced features for NextGen. The algorithm described in this paper improves climb trajectory prediction accuracy by adjusting trajectory predictions based on observed track data. It utilizes rate-of-climb and airspeed measurements derived from position data to dynamically adjust the aircraft weight modeled for trajectory predictions. In simulations with weight uncertainty, the algorithm is able to adapt to within 3 percent of the actual gross weight within two minutes of the initial adaptation. The root-mean-square of altitude errors for five-minute predictions was reduced by 73 percent. Conflict detection performance also improved, with a 15 percent reduction in missed alerts and a 10 percent reduction in false alerts. In a simulation with climb speed capture intent and weight uncertainty, the algorithm improved climb trajectory prediction accuracy by up to 30 percent and conflict detection performance, reducing missed and false alerts by up to 10 percent.

The Effects of Reflective Activities on Skill Adaptation in a Work-Related Instrumental Learning Setting

ERIC Educational Resources Information Center

Roessger, Kevin M.

2014-01-01

In work-related instrumental learning contexts, the role of reflective activities is unclear. Kolb's experiential learning theory and Mezirow's transformative learning theory predict skill adaptation as an outcome. This prediction was tested by manipulating reflective activities and assessing participants' response and error rates during novel…

A Physics-Based Engineering Methodology for Calculating Soft Error Rates of Bulk CMOS and SiGe Heterojunction Bipolar Transistor Integrated Circuits

NASA Astrophysics Data System (ADS)

Fulkerson, David E.

2010-02-01

This paper describes a new methodology for characterizing the electrical behavior and soft error rate (SER) of CMOS and SiGe HBT integrated circuits that are struck by ions. A typical engineering design problem is to calculate the SER of a critical path that commonly includes several circuits such as an input buffer, several logic gates, logic storage, clock tree circuitry, and an output buffer. Using multiple 3D TCAD simulations to solve this problem is too costly and time-consuming for general engineering use. The new and simple methodology handles the problem with ease by simple SPICE simulations. The methodology accurately predicts the measured threshold linear energy transfer (LET) of a bulk CMOS SRAM. It solves for circuit currents and voltage spikes that are close to those predicted by expensive 3D TCAD simulations. It accurately predicts the measured event cross-section vs. LET curve of an experimental SiGe HBT flip-flop. The experimental cross section vs. frequency behavior and other subtle effects are also accurately predicted.

English speech sound development in preschool-aged children from bilingual English-Spanish environments.

PubMed

Gildersleeve-Neumann, Christina E; Kester, Ellen S; Davis, Barbara L; Peña, Elizabeth D

2008-07-01

English speech acquisition by typically developing 3- to 4-year-old children with monolingual English was compared to English speech acquisition by typically developing 3- to 4-year-old children with bilingual English-Spanish backgrounds. We predicted that exposure to Spanish would not affect the English phonetic inventory but would increase error frequency and type in bilingual children. Single-word speech samples were collected from 33 children. Phonetically transcribed samples for the 3 groups (monolingual English children, English-Spanish bilingual children who were predominantly exposed to English, and English-Spanish bilingual children with relatively equal exposure to English and Spanish) were compared at 2 time points and for change over time for phonetic inventory, phoneme accuracy, and error pattern frequencies. Children demonstrated similar phonetic inventories. Some bilingual children produced Spanish phonemes in their English and produced few consonant cluster sequences. Bilingual children with relatively equal exposure to English and Spanish averaged more errors than did bilingual children who were predominantly exposed to English. Both bilingual groups showed higher error rates than English-only children overall, particularly for syllable-level error patterns. All language groups decreased in some error patterns, although the ones that decreased were not always the same across language groups. Some group differences of error patterns and accuracy were significant. Vowel error rates did not differ by language group. Exposure to English and Spanish may result in a higher English error rate in typically developing bilinguals, including the application of Spanish phonological properties to English. Slightly higher error rates are likely typical for bilingual preschool-aged children. Change over time at these time points for all 3 groups was similar, suggesting that all will reach an adult-like system in English with exposure and practice.

Linear free energy relationships between aqueous phase hydroxyl radical reaction rate constants and free energy of activation.

PubMed

Minakata, Daisuke; Crittenden, John

2011-04-15

The hydroxyl radical (HO(•)) is a strong oxidant that reacts with electron-rich sites on organic compounds and initiates complex radical chain reactions in aqueous phase advanced oxidation processes (AOPs). Computer based kinetic modeling requires a reaction pathway generator and predictions of associated reaction rate constants. Previously, we reported a reaction pathway generator that can enumerate the most important elementary reactions for aliphatic compounds. For the reaction rate constant predictor, we develop linear free energy relationships (LFERs) between aqueous phase literature-reported HO(•) reaction rate constants and theoretically calculated free energies of activation for H-atom abstraction from a C-H bond and HO(•) addition to alkenes. The theoretical method uses ab initio quantum mechanical calculations, Gaussian 1-3, for gas phase reactions and a solvation method, COSMO-RS theory, to estimate the impact of water. Theoretically calculated free energies of activation are found to be within approximately ±3 kcal/mol of experimental values. Considering errors that arise from quantum mechanical calculations and experiments, this should be within the acceptable errors. The established LFERs are used to predict the HO(•) reaction rate constants within a factor of 5 from the experimental values. This approach may be applied to other reaction mechanisms to establish a library of rate constant predictions for kinetic modeling of AOPs.

Estimation of Power Consumption in the Circular Sawing of Stone Based on Tangential Force Distribution

NASA Astrophysics Data System (ADS)

Huang, Guoqin; Zhang, Meiqin; Huang, Hui; Guo, Hua; Xu, Xipeng

2018-04-01

Circular sawing is an important method for the processing of natural stone. The ability to predict sawing power is important in the optimisation, monitoring and control of the sawing process. In this paper, a predictive model (PFD) of sawing power, which is based on the tangential force distribution at the sawing contact zone, was proposed, experimentally validated and modified. With regard to the influence of sawing speed on tangential force distribution, the modified PFD (MPFD) performed with high predictive accuracy across a wide range of sawing parameters, including sawing speed. The mean maximum absolute error rate was within 6.78%, and the maximum absolute error rate was within 11.7%. The practicability of predicting sawing power by the MPFD with few initial experimental samples was proved in case studies. On the premise of high sample measurement accuracy, only two samples are required for a fixed sawing speed. The feasibility of applying the MPFD to optimise sawing parameters while lowering the energy consumption of the sawing system was validated. The case study shows that energy use was reduced 28% by optimising the sawing parameters. The MPFD model can be used to predict sawing power, optimise sawing parameters and control energy.

A Modified Double Multiple Nonlinear Regression Constitutive Equation for Modeling and Prediction of High Temperature Flow Behavior of BFe10-1-2 Alloy

NASA Astrophysics Data System (ADS)

Cai, Jun; Wang, Kuaishe; Shi, Jiamin; Wang, Wen; Liu, Yingying

2018-01-01

Constitutive analysis for hot working of BFe10-1-2 alloy was carried out by using experimental stress-strain data from isothermal hot compression tests, in a wide range of temperature of 1,023 1,273 K, and strain rate range of 0.001 10 s-1. A constitutive equation based on modified double multiple nonlinear regression was proposed considering the independent effects of strain, strain rate, temperature and their interrelation. The predicted flow stress data calculated from the developed equation was compared with the experimental data. Correlation coefficient (R), average absolute relative error (AARE) and relative errors were introduced to verify the validity of the developed constitutive equation. Subsequently, a comparative study was made on the capability of strain-compensated Arrhenius-type constitutive model. The results showed that the developed constitutive equation based on modified double multiple nonlinear regression could predict flow stress of BFe10-1-2 alloy with good correlation and generalization.

Predicting Microbial Fuel Cell Biofilm Communities and Bioreactor Performance using Artificial Neural Networks.

PubMed

Lesnik, Keaton Larson; Liu, Hong

2017-09-19

The complex interactions that occur in mixed-species bioelectrochemical reactors, like microbial fuel cells (MFCs), make accurate predictions of performance outcomes under untested conditions difficult. While direct correlations between any individual waste stream characteristic or microbial community structure and reactor performance have not been able to be directly established, the increase in sequencing data and readily available computational power enables the development of alternate approaches. In the current study, 33 MFCs were evaluated under a range of conditions including eight separate substrates and three different wastewaters. Artificial Neural Networks (ANNs) were used to establish mathematical relationships between wastewater/solution characteristics, biofilm communities, and reactor performance. ANN models that incorporated biotic interactions predicted reactor performance outcomes more accurately than those that did not. The average percent error of power density predictions was 16.01 ± 4.35%, while the average percent error of Coulombic efficiency and COD removal rate predictions were 1.77 ± 0.57% and 4.07 ± 1.06%, respectively. Predictions of power density improved to within 5.76 ± 3.16% percent error through classifying taxonomic data at the family versus class level. Results suggest that the microbial communities and performance of bioelectrochemical systems can be accurately predicted using data-mining, machine-learning techniques.

pKa prediction of monoprotic small molecules the SMARTS way.

PubMed

Lee, Adam C; Yu, Jing-Yu; Crippen, Gordon M

2008-10-01

Realizing favorable absorption, distribution, metabolism, elimination, and toxicity profiles is a necessity due to the high attrition rate of lead compounds in drug development today. The ability to accurately predict bioavailability can help save time and money during the screening and optimization processes. As several robust programs already exist for predicting logP, we have turned our attention to the fast and robust prediction of pK(a) for small molecules. Using curated data from the Beilstein Database and Lange's Handbook of Chemistry, we have created a decision tree based on a novel set of SMARTS strings that can accurately predict the pK(a) for monoprotic compounds with R(2) of 0.94 and root mean squared error of 0.68. Leave-some-out (10%) cross-validation achieved Q(2) of 0.91 and root mean squared error of 0.80.

Evaluating the predictive performance of empirical estimators of natural mortality rate using information on over 200 fish species

USGS Publications Warehouse

Then, Amy Y.; Hoenig, John M; Hall, Norman G.; Hewitt, David A.

2015-01-01

Many methods have been developed in the last 70 years to predict the natural mortality rate, M, of a stock based on empirical evidence from comparative life history studies. These indirect or empirical methods are used in most stock assessments to (i) obtain estimates of M in the absence of direct information, (ii) check on the reasonableness of a direct estimate of M, (iii) examine the range of plausible M estimates for the stock under consideration, and (iv) define prior distributions for Bayesian analyses. The two most cited empirical methods have appeared in the literature over 2500 times to date. Despite the importance of these methods, there is no consensus in the literature on how well these methods work in terms of prediction error or how their performance may be ranked. We evaluate estimators based on various combinations of maximum age (tmax), growth parameters, and water temperature by seeing how well they reproduce >200 independent, direct estimates of M. We use tenfold cross-validation to estimate the prediction error of the estimators and to rank their performance. With updated and carefully reviewed data, we conclude that a tmax-based estimator performs the best among all estimators evaluated. The tmax-based estimators in turn perform better than the Alverson–Carney method based on tmax and the von Bertalanffy K coefficient, Pauly’s method based on growth parameters and water temperature and methods based just on K. It is possible to combine two independent methods by computing a weighted mean but the improvement over the tmax-based methods is slight. Based on cross-validation prediction error, model residual patterns, model parsimony, and biological considerations, we recommend the use of a tmax-based estimator (M=4.899tmax−0.916">M=4.899t−0.916maxM=4.899tmax−0.916, prediction error = 0.32) when possible and a growth-based method (M=4.118K0.73L∞−0.33">M=4.118K0.73L−0.33∞M=4.118K0.73L∞−0.33 , prediction error = 0.6, length in cm) otherwise.

Study on elevated-temperature flow behavior of Ni-Cr-Mo-B ultra-heavy-plate steel via experiment and modelling

NASA Astrophysics Data System (ADS)

Gao, Zhi-yu; Kang, Yu; Li, Yan-shuai; Meng, Chao; Pan, Tao

2018-04-01

Elevated-temperature flow behavior of a novel Ni-Cr-Mo-B ultra-heavy-plate steel was investigated by conducting hot compressive deformation tests on a Gleeble-3800 thermo-mechanical simulator at a temperature range of 1123 K–1423 K with a strain rate range from 0.01 s‑1 to10 s‑1 and a height reduction of 70%. Based on the experimental results, classic strain-compensated Arrhenius-type, a new revised strain-compensated Arrhenius-type and classic modified Johnson-Cook constitutive models were developed for predicting the high-temperature deformation behavior of the steel. The predictability of these models were comparatively evaluated in terms of statistical parameters including correlation coefficient (R), average absolute relative error (AARE), average root mean square error (RMSE), normalized mean bias error (NMBE) and relative error. The statistical results indicate that the new revised strain-compensated Arrhenius-type model could give prediction of elevated-temperature flow stress for the steel accurately under the entire process conditions. However, the predicted values by the classic modified Johnson-Cook model could not agree well with the experimental values, and the classic strain-compensated Arrhenius-type model could track the deformation behavior more accurately compared with the modified Johnson-Cook model, but less accurately with the new revised strain-compensated Arrhenius-type model. In addition, reasons of differences in predictability of these models were discussed in detail.

Single point estimation of phenytoin dosing: a reappraisal.

PubMed

Koup, J R; Gibaldi, M; Godolphin, W

1981-11-01

A previously proposed method for estimation of phenytoin dosing requirement using a single serum sample obtained 24 hours after intravenous loading dose (18 mg/Kg) has been re-evaluated. Using more realistic values for the volume of distribution of phenytoin (0.4 to 1.2 L/Kg), simulations indicate that the proposed method will fail to consistently predict dosage requirements. Additional simulations indicate that two samples obtained during the 24 hour interval following the iv loading dose could be used to more reliably predict phenytoin dose requirement. Because of the nonlinear relationship which exists between phenytoin dose administration rate (RO) and the mean steady state serum concentration (CSS), small errors in prediction of the required RO result in much larger errors in CSS.

Stage-Discharge Relations for the Colorado River in Glen, Marble, and Grand Canyons, Arizona, 1990-2005

USGS Publications Warehouse

Hazel, Joseph E.; Kaplinski, Matt; Parnell, Rod; Kohl, Keith; Topping, David J.

2007-01-01

This report presents stage-discharge relations for 47 discrete locations along the Colorado River, downstream from Glen Canyon Dam. Predicting the river stage that results from changes in flow regime is important for many studies investigating the effects of dam operations on resources in and along the Colorado River. The empirically based stage-discharge relations were developed from water-surface elevation data surveyed at known discharges at all 47 locations. The rating curves accurately predict stage at each location for discharges between 141 cubic meters per second and 1,274 cubic meters per second. The coefficient of determination (R2) of the fit to the data ranged from 0.993 to 1.00. Given the various contributing errors to the method, a conservative error estimate of ?0.05 m was assigned to the rating curves.

Accurate prediction of cardiorespiratory fitness using cycle ergometry in minimally disabled persons with relapsing-remitting multiple sclerosis.

PubMed

Motl, Robert W; Fernhall, Bo

2012-03-01

To examine the accuracy of predicting peak oxygen consumption (VO(2peak)) primarily from peak work rate (WR(peak)) recorded during a maximal, incremental exercise test on a cycle ergometer among persons with relapsing-remitting multiple sclerosis (RRMS) who had minimal disability. Cross-sectional study. Clinical research laboratory. Women with RRMS (n=32) and sex-, age-, height-, and weight-matched healthy controls (n=16) completed an incremental exercise test on a cycle ergometer to volitional termination. Not applicable. Measured and predicted VO(2peak) and WR(peak). There were strong, statistically significant associations between measured and predicted VO(2peak) in the overall sample (R(2)=.89, standard error of the estimate=127.4 mL/min) and subsamples with (R(2)=.89, standard error of the estimate=131.3 mL/min) and without (R(2)=.85, standard error of the estimate=126.8 mL/min) multiple sclerosis (MS) based on the linear regression analyses. Based on the 95% confidence limits for worst-case errors, the equation predicted VO(2peak) within 10% of its true value in 95 of every 100 subjects with MS. Peak VO(2) can be accurately predicted in persons with RRMS who have minimal disability as it is in controls by using established equations and WR(peak) recorded from a maximal, incremental exercise test on a cycle ergometer. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Feasibility of predicting tumor motion using online data acquired during treatment and a generalized neural network optimized with offline patient tumor trajectories.

PubMed

Teo, Troy P; Ahmed, Syed Bilal; Kawalec, Philip; Alayoubi, Nadia; Bruce, Neil; Lyn, Ethan; Pistorius, Stephen

2018-02-01

The accurate prediction of intrafraction lung tumor motion is required to compensate for system latency in image-guided adaptive radiotherapy systems. The goal of this study was to identify an optimal prediction model that has a short learning period so that prediction and adaptation can commence soon after treatment begins, and requires minimal reoptimization for individual patients. Specifically, the feasibility of predicting tumor position using a combination of a generalized (i.e., averaged) neural network, optimized using historical patient data (i.e., tumor trajectories) obtained offline, coupled with the use of real-time online tumor positions (obtained during treatment delivery) was examined. A 3-layer perceptron neural network was implemented to predict tumor motion for a prediction horizon of 650 ms. A backpropagation algorithm and batch gradient descent approach were used to train the model. Twenty-seven 1-min lung tumor motion samples (selected from a CyberKnife patient dataset) were sampled at a rate of 7.5 Hz (0.133 s) to emulate the frame rate of an electronic portal imaging device (EPID). A sliding temporal window was used to sample the data for learning. The sliding window length was set to be equivalent to the first breathing cycle detected from each trajectory. Performing a parametric sweep, an averaged error surface of mean square errors (MSE) was obtained from the prediction responses of seven trajectories used for the training of the model (Group 1). An optimal input data size and number of hidden neurons were selected to represent the generalized model. To evaluate the prediction performance of the generalized model on unseen data, twenty tumor traces (Group 2) that were not involved in the training of the model were used for the leave-one-out cross-validation purposes. An input data size of 35 samples (4.6 s) and 20 hidden neurons were selected for the generalized neural network. An average sliding window length of 28 data samples was used. The average initial learning period prior to the availability of the first predicted tumor position was 8.53 ± 1.03 s. Average mean absolute error (MAE) of 0.59 ± 0.13 mm and 0.56 ± 0.18 mm were obtained from Groups 1 and 2, respectively, giving an overall MAE of 0.57 ± 0.17 mm. Average root-mean-square-error (RMSE) of 0.67 ± 0.36 for all the traces (0.76 ± 0.34 mm, Group 1 and 0.63 ± 0.36 mm, Group 2), is comparable to previously published results. Prediction errors are mainly due to the irregular periodicities between cycles. Since the errors from Groups 1 and 2 are within the same range, it demonstrates that this model can generalize and predict on unseen data. This is a first attempt to use an averaged MSE error surface (obtained from the prediction of different patients' tumor trajectories) to determine the parameters of a generalized neural network. This network could be deployed as a plug-and-play predictor for tumor trajectory during treatment delivery, eliminating the need for optimizing individual networks with pretreatment patient data. © 2017 American Association of Physicists in Medicine.

Perceptually tuned low-bit-rate video codec for ATM networks

NASA Astrophysics Data System (ADS)

Chou, Chun-Hsien

1996-02-01

In order to maintain high visual quality in transmitting low bit-rate video signals over asynchronous transfer mode (ATM) networks, a layered coding scheme that incorporates the human visual system (HVS), motion compensation (MC), and conditional replenishment (CR) is presented in this paper. An empirical perceptual model is proposed to estimate the spatio- temporal just-noticeable distortion (STJND) profile for each frame, by which perceptually important (PI) prediction-error signals can be located. Because of the limited channel capacity of the base layer, only coded data of motion vectors, the PI signals within a small strip of the prediction-error image and, if there are remaining bits, the PI signals outside the strip are transmitted by the cells of the base-layer channel. The rest of the coded data are transmitted by the second-layer cells which may be lost due to channel error or network congestion. Simulation results show that visual quality of the reconstructed CIF sequence is acceptable when the capacity of the base-layer channel is allocated with 2 multiplied by 64 kbps and the cells of the second layer are all lost.

Consistency of gene starts among Burkholderia genomes

PubMed Central

2011-01-01

Background Evolutionary divergence in the position of the translational start site among orthologous genes can have significant functional impacts. Divergence can alter the translation rate, degradation rate, subcellular location, and function of the encoded proteins. Results Existing Genbank gene maps for Burkholderia genomes suggest that extensive divergence has occurred--53% of ortholog sets based on Genbank gene maps had inconsistent gene start sites. However, most of these inconsistencies appear to be gene-calling errors. Evolutionary divergence was the most plausible explanation for only 17% of the ortholog sets. Correcting probable errors in the Genbank gene maps decreased the percentage of ortholog sets with inconsistent starts by 68%, increased the percentage of ortholog sets with extractable upstream intergenic regions by 32%, increased the sequence similarity of intergenic regions and predicted proteins, and increased the number of proteins with identifiable signal peptides. Conclusions Our findings highlight an emerging problem in comparative genomics: single-digit percent errors in gene predictions can lead to double-digit percentages of inconsistent ortholog sets. The work demonstrates a simple approach to evaluate and improve the quality of gene maps. PMID:21342528

Long-term cliff retreat and erosion hotspots along the central shores of the Monterey Bay National Marine Sanctuary

USGS Publications Warehouse

Moore, Laura J.; Griggs, Gary B.

2002-01-01

Quantification of cliff retreat rates for the southern half of Santa Cruz County, CA, USA, located within the Monterey Bay National Marine Sanctuary, using the softcopy/geographic information system (GIS) methodology results in average cliff retreat rates of 7–15 cm/yr between 1953 and 1994. The coastal dunes at the southern end of Santa Cruz County migrate seaward and landward through time and display net accretion between 1953 and 1994, which is partially due to development. In addition, three critically eroding segments of coastline with high average erosion rates ranging from 20 to 63 cm/yr are identified as erosion ‘hotspots’. These locations include: Opal Cliffs, Depot Hill and Manresa. Although cliff retreat is episodic, spatially variable at the scale of meters, and the factors affecting cliff retreat vary along the Santa Cruz County coastline, there is a compensation between factors affecting retreat such that over the long-term the coastline maintains a relatively smooth configuration. The softcopy/GIS methodology significantly reduces errors inherent in the calculation of retreat rates in high-relief areas (e.g. erosion rates generated in this study are generally correct to within 10 cm) by removing errors due to relief displacement. Although the resulting root mean squared error for erosion rates is relatively small, simple projections of past erosion rates are inadequate to provide predictions of future cliff position. Improved predictions can be made for individual coastal segments by using a mean erosion rate and the standard deviation as guides to future cliff behavior in combination with an understanding of processes acting along the coastal segments in question. This methodology can be applied on any high-relief coast where retreat rates can be measured.

Relations between Response Trajectories on the Continuous Performance Test and Teacher-Rated Problem Behaviors in Preschoolers

PubMed Central

Allan, Darcey M.; Lonigan, Christopher J.

2014-01-01

Although both the Continuous Performance Test (CPT) and behavior rating scales are used in both practice and research to assess inattentive and hyperactive/impulsive behaviors, the correlations between performance on the CPT and teachers' ratings are typically only small-to-moderate. This study examined trajectories of performance on a low target-frequency visual CPT in a sample of preschool children and how these trajectories were associated with teacher-ratings of problem behaviors (i.e., inattention, hyperactivity/impulsivity [H/I], and oppositional/defiant behavior). Participants included 399 preschool children (Mean age = 56 months; 49.4% female; 73.7% White/Caucasian). An ADHD-rating scale was completed by teachers, and the CPT was completed by the preschoolers. Results showed that children's performance across four temporal blocks on the CPT was not stable across the duration of the task, with error rates generally increasing from initial to later blocks. The predictive relations of teacher-rated problem behaviors to performance trajectories on the CPT were examined using growth curve models. Higher rates of teacher-reported inattention and H/I were uniquely associated with higher rates of initial omission errors and initial commission errors, respectively. Higher rates of teacher-reported overall problem behaviors were associated with increasing rates of omission but not commission errors during the CPT; however, the relation was not specific to one type of problem behavior. The results of this study indicate that the pattern of errors on the CPT in preschool samples is complex and may be determined by multiple behavioral factors. These findings have implications for the interpretation of CPT performance in young children. PMID:25419645

Relations between response trajectories on the continuous performance test and teacher-rated problem behaviors in preschoolers.

PubMed

Allan, Darcey M; Lonigan, Christopher J

2015-06-01

Although both the continuous performance test (CPT) and behavior rating scales are used in both practice and research to assess inattentive and hyperactive/impulsive behaviors, the correlations between performance on the CPT and teachers' ratings are typically only small-to-moderate. This study examined trajectories of performance on a low target-frequency visual CPT in a sample of preschool children and how these trajectories were associated with teacher-ratings of problem behaviors (i.e., inattention, hyperactivity/impulsivity [H/I], and oppositional/defiant behavior). Participants included 399 preschool children (mean age = 56 months; 49.4% female; 73.7% White/Caucasian). An attention deficit/hyperactivity disorder (ADHD) rating scale was completed by teachers, and the CPT was completed by the preschoolers. Results showed that children's performance across 4 temporal blocks on the CPT was not stable across the duration of the task, with error rates generally increasing from initial to later blocks. The predictive relations of teacher-rated problem behaviors to performance trajectories on the CPT were examined using growth curve models. Higher rates of teacher-reported inattention and H/I were uniquely associated with higher rates of initial omission errors and initial commission errors, respectively. Higher rates of teacher-reported overall problem behaviors were associated with increasing rates of omission but not commission errors during the CPT; however, the relation was not specific to 1 type of problem behavior. The results of this study indicate that the pattern of errors on the CPT in preschool samples is complex and may be determined by multiple behavioral factors. These findings have implications for the interpretation of CPT performance in young children. (c) 2015 APA, all rights reserved).

Testing the Agreement/Tense Omission Model: Why the Data on Children's Use of Non-Nominative 3psg Subjects Count against the ATOM

ERIC Educational Resources Information Center

Pine, Julian M.; Rowland, Caroline F.; Lieven, Elena V. M.; Theakston, Anna L.

2005-01-01

One of the most influential recent accounts of pronoun case-marking errors in young children's speech is Schutze & Wexler's (1996) Agreement/Tense Omission Model (ATOM). The ATOM predicts that the rate of agreeing verbs with non-nominative subjects will be so low that such errors can be reasonably disregarded as noise in the data. The present…

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

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.

Role-modeling and medical error disclosure: a national survey of trainees.

PubMed

Martinez, William; Hickson, Gerald B; Miller, Bonnie M; Doukas, David J; Buckley, John D; Song, John; Sehgal, Niraj L; Deitz, Jennifer; Braddock, Clarence H; Lehmann, Lisa Soleymani

2014-03-01

To measure trainees' exposure to negative and positive role-modeling for responding to medical errors and to examine the association between that exposure and trainees' attitudes and behaviors regarding error disclosure. Between May 2011 and June 2012, 435 residents at two large academic medical centers and 1,187 medical students from seven U.S. medical schools received anonymous, electronic questionnaires. The questionnaire asked respondents about (1) experiences with errors, (2) training for responding to errors, (3) behaviors related to error disclosure, (4) exposure to role-modeling for responding to errors, and (5) attitudes regarding disclosure. Using multivariate regression, the authors analyzed whether frequency of exposure to negative and positive role-modeling independently predicted two primary outcomes: (1) attitudes regarding disclosure and (2) nontransparent behavior in response to a harmful error. The response rate was 55% (884/1,622). Training on how to respond to errors had the largest independent, positive effect on attitudes (standardized effect estimate, 0.32, P < .001); negative role-modeling had the largest independent, negative effect (standardized effect estimate, -0.26, P < .001). Positive role-modeling had a positive effect on attitudes (standardized effect estimate, 0.26, P < .001). Exposure to negative role-modeling was independently associated with an increased likelihood of trainees' nontransparent behavior in response to an error (OR 1.37, 95% CI 1.15-1.64; P < .001). Exposure to role-modeling predicts trainees' attitudes and behavior regarding the disclosure of harmful errors. Negative role models may be a significant impediment to disclosure among trainees.

Development of a wideband pulse quaternary modulation system. [for an operational 400 Mbps baseband laser communication system

NASA Technical Reports Server (NTRS)

Federhofer, J. A.

1974-01-01

Laboratory data verifying the pulse quaternary modulation (PQM) theoretical predictions is presented. The first laboratory PQM laser communication system was successfully fabricated, integrated, tested and demonstrated. System bit error rate tests were performed and, in general, indicated approximately a 2 db degradation from the theoretically predicted results. These tests indicated that no gross errors were made in the initial theoretical analysis of PQM. The relative ease with which the entire PQM laboratory system was integrated and tested indicates that PQM is a viable candidate modulation scheme for an operational 400 Mbps baseband laser communication system.

Robust vector quantization for noisy channels

NASA Technical Reports Server (NTRS)

Demarca, J. R. B.; Farvardin, N.; Jayant, N. S.; Shoham, Y.

1988-01-01

The paper briefly discusses techniques for making vector quantizers more tolerant to tranmsission errors. Two algorithms are presented for obtaining an efficient binary word assignment to the vector quantizer codewords without increasing the transmission rate. It is shown that about 4.5 dB gain over random assignment can be achieved with these algorithms. It is also proposed to reduce the effects of error propagation in vector-predictive quantizers by appropriately constraining the response of the predictive loop. The constrained system is shown to have about 4 dB of SNR gain over an unconstrained system in a noisy channel, with a small loss of clean-channel performance.

Novel Radiobiological Gamma Index for Evaluation of 3-Dimensional Predicted Dose Distribution

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

Sumida, Iori, E-mail: sumida@radonc.med.osaka-u.ac.jp; Yamaguchi, Hajime; Kizaki, Hisao

2015-07-15

Purpose: To propose a gamma index-based dose evaluation index that integrates the radiobiological parameters of tumor control (TCP) and normal tissue complication probabilities (NTCP). Methods and Materials: Fifteen prostate and head and neck (H&N) cancer patients received intensity modulated radiation therapy. Before treatment, patient-specific quality assurance was conducted via beam-by-beam analysis, and beam-specific dose error distributions were generated. The predicted 3-dimensional (3D) dose distribution was calculated by back-projection of relative dose error distribution per beam. A 3D gamma analysis of different organs (prostate: clinical [CTV] and planned target volumes [PTV], rectum, bladder, femoral heads; H&N: gross tumor volume [GTV], CTV,more » spinal cord, brain stem, both parotids) was performed using predicted and planned dose distributions under 2%/2 mm tolerance and physical gamma passing rate was calculated. TCP and NTCP values were calculated for voxels with physical gamma indices (PGI) >1. We propose a new radiobiological gamma index (RGI) to quantify the radiobiological effects of TCP and NTCP and calculate radiobiological gamma passing rates. Results: The mean RGI gamma passing rates for prostate cases were significantly different compared with those of PGI (P<.03–.001). The mean RGI gamma passing rates for H&N cases (except for GTV) were significantly different compared with those of PGI (P<.001). Differences in gamma passing rates between PGI and RGI were due to dose differences between the planned and predicted dose distributions. Radiobiological gamma distribution was visualized to identify areas where the dose was radiobiologically important. Conclusions: RGI was proposed to integrate radiobiological effects into PGI. This index would assist physicians and medical physicists not only in physical evaluations of treatment delivery accuracy, but also in clinical evaluations of predicted dose distribution.« less

Error and Error Mitigation in Low-Coverage Genome Assemblies

PubMed Central

Hubisz, Melissa J.; Lin, Michael F.; Kellis, Manolis; Siepel, Adam

2011-01-01

The recent release of twenty-two new genome sequences has dramatically increased the data available for mammalian comparative genomics, but twenty of these new sequences are currently limited to ∼2× coverage. Here we examine the extent of sequencing error in these 2× assemblies, and its potential impact in downstream analyses. By comparing 2× assemblies with high-quality sequences from the ENCODE regions, we estimate the rate of sequencing error to be 1–4 errors per kilobase. While this error rate is fairly modest, sequencing error can still have surprising effects. For example, an apparent lineage-specific insertion in a coding region is more likely to reflect sequencing error than a true biological event, and the length distribution of coding indels is strongly distorted by error. We find that most errors are contributed by a small fraction of bases with low quality scores, in particular, by the ends of reads in regions of single-read coverage in the assembly. We explore several approaches for automatic sequencing error mitigation (SEM), making use of the localized nature of sequencing error, the fact that it is well predicted by quality scores, and information about errors that comes from comparisons across species. Our automatic methods for error mitigation cannot replace the need for additional sequencing, but they do allow substantial fractions of errors to be masked or eliminated at the cost of modest amounts of over-correction, and they can reduce the impact of error in downstream phylogenomic analyses. Our error-mitigated alignments are available for download. PMID:21340033

Prediction of change in protein unfolding rates upon point mutations in two state proteins.

PubMed

Chaudhary, Priyashree; Naganathan, Athi N; Gromiha, M Michael

2016-09-01

Studies on protein unfolding rates are limited and challenging due to the complexity of unfolding mechanism and the larger dynamic range of the experimental data. Though attempts have been made to predict unfolding rates using protein sequence-structure information there is no available method for predicting the unfolding rates of proteins upon specific point mutations. In this work, we have systematically analyzed a set of 790 single mutants and developed a robust method for predicting protein unfolding rates upon mutations (Δlnku) in two-state proteins by combining amino acid properties and knowledge-based classification of mutants with multiple linear regression technique. We obtain a mean absolute error (MAE) of 0.79/s and a Pearson correlation coefficient (PCC) of 0.71 between predicted unfolding rates and experimental observations using jack-knife test. We have developed a web server for predicting protein unfolding rates upon mutation and it is freely available at https://www.iitm.ac.in/bioinfo/proteinunfolding/unfoldingrace.html. Prominent features that determine unfolding kinetics as well as plausible reasons for the observed outliers are also discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

Stereotype threat can reduce older adults' memory errors.

PubMed

Barber, Sarah J; Mather, Mara

2013-01-01

Stereotype threat often incurs the cost of reducing the amount of information that older adults accurately recall. In the current research, we tested whether stereotype threat can also benefit memory. According to the regulatory focus account of stereotype threat, threat induces a prevention focus in which people become concerned with avoiding errors of commission and are sensitive to the presence or absence of losses within their environment. Because of this, we predicted that stereotype threat might reduce older adults' memory errors. Results were consistent with this prediction. Older adults under stereotype threat had lower intrusion rates during free-recall tests (Experiments 1 and 2). They also reduced their false alarms and adopted more conservative response criteria during a recognition test (Experiment 2). Thus, stereotype threat can decrease older adults' false memories, albeit at the cost of fewer veridical memories, as well.

Cerebral metabolic dysfunction and impaired vigilance in recently abstinent methamphetamine abusers.

PubMed

London, Edythe D; Berman, Steven M; Voytek, Bradley; Simon, Sara L; Mandelkern, Mark A; Monterosso, John; Thompson, Paul M; Brody, Arthur L; Geaga, Jennifer A; Hong, Michael S; Hayashi, Kiralee M; Rawson, Richard A; Ling, Walter

2005-11-15

Methamphetamine (MA) abusers have cognitive deficits, abnormal metabolic activity and structural deficits in limbic and paralimbic cortices, and reduced hippocampal volume. The links between cognitive impairment and these cerebral abnormalities are not established. We assessed cerebral glucose metabolism with [F-18]fluorodeoxyglucose positron emission tomography in 17 abstinent (4 to 7 days) methamphetamine users and 16 control subjects performing an auditory vigilance task and obtained structural magnetic resonance brain scans. Regional brain radioactivity served as a marker for relative glucose metabolism. Error rates on the task were related to regional radioactivity and hippocampal morphology. Methamphetamine users had higher error rates than control subjects on the vigilance task. The groups showed different relationships between error rates and relative activity in the anterior and middle cingulate gyrus and the insula. Whereas the MA user group showed negative correlations involving these regions, the control group showed positive correlations involving the cingulate cortex. Across groups, hippocampal metabolic and structural measures were negatively correlated with error rates. Dysfunction in the cingulate and insular cortices of recently abstinent MA abusers contribute to impaired vigilance and other cognitive functions requiring sustained attention. Hippocampal integrity predicts task performance in methamphetamine users as well as control subjects.

Earth's dynamo limit of predictability controlled by magnetic dissipation

NASA Astrophysics Data System (ADS)

Lhuillier, Florian; Aubert, Julien; Hulot, Gauthier

2011-08-01

To constrain the forecast horizon of geomagnetic data assimilation, it is of interest to quantify the range of predictability of the geodynamo. Following earlier work in the field of dynamic meteorology, we investigate the sensitivity of numerical dynamos to various perturbations applied to the magnetic, velocity and temperature fields. These perturbations result in some errors, which affect all fields in the same relative way, and grow at the same exponential rate λ=τ-1e, independent of the type and the amplitude of perturbation. Errors produced by the limited resolution of numerical dynamos are also shown to produce a similar amplification, with the same exponential rate. Exploring various possible scaling laws, we demonstrate that the growth rate is mainly proportional to an advection timescale. To better understand the mechanism responsible for the error amplification, we next compare these growth rates with two other dynamo outputs which display a similar dependence on advection: the inverse τ-1SV of the secular-variation timescale, characterizing the secular variation of the observable field produced by these dynamos; and the inverse (τmagdiss)-1 of the magnetic dissipation time, characterizing the rate at which magnetic energy is produced to compensate for Ohmic dissipation in these dynamos. The possible role of viscous dissipation is also discussed via the inverse (τkindiss)-1 of the analogous viscous dissipation time, characterizing the rate at which kinetic energy is produced to compensate for viscous dissipation. We conclude that τe tends to equate τmagdiss for dynamos operating in a turbulent regime with low enough Ekman number, and such that τmagdiss < τkindiss. As these conditions are met in the Earth's outer core, we suggest that τe is controlled by magnetic dissipation, leading to a value τe=τmagdiss≈ 30 yr. We finally discuss the consequences of our results for the practical limit of predictability of the geodynamo.

Application of a Hybrid Model for Predicting the Incidence of Tuberculosis in Hubei, China

PubMed Central

Zhang, Guoliang; Huang, Shuqiong; Duan, Qionghong; Shu, Wen; Hou, Yongchun; Zhu, Shiyu; Miao, Xiaoping; Nie, Shaofa; Wei, Sheng; Guo, Nan; Shan, Hua; Xu, Yihua

2013-01-01

Background A prediction model for tuberculosis incidence is needed in China which may be used as a decision-supportive tool for planning health interventions and allocating health resources. Methods The autoregressive integrated moving average (ARIMA) model was first constructed with the data of tuberculosis report rate in Hubei Province from Jan 2004 to Dec 2011.The data from Jan 2012 to Jun 2012 were used to validate the model. Then the generalized regression neural network (GRNN)-ARIMA combination model was established based on the constructed ARIMA model. Finally, the fitting and prediction accuracy of the two models was evaluated. Results A total of 465,960 cases were reported between Jan 2004 and Dec 2011 in Hubei Province. The report rate of tuberculosis was highest in 2005 (119.932 per 100,000 population) and lowest in 2010 (84.724 per 100,000 population). The time series of tuberculosis report rate show a gradual secular decline and a striking seasonal variation. The ARIMA (2, 1, 0) × (0, 1, 1)12 model was selected from several plausible ARIMA models. The residual mean square error of the GRNN-ARIMA model and ARIMA model were 0.4467 and 0.6521 in training part, and 0.0958 and 0.1133 in validation part, respectively. The mean absolute error and mean absolute percentage error of the hybrid model were also less than the ARIMA model. Discussion and Conclusions The gradual decline in tuberculosis report rate may be attributed to the effect of intensive measures on tuberculosis. The striking seasonal variation may have resulted from several factors. We suppose that a delay in the surveillance system may also have contributed to the variation. According to the fitting and prediction accuracy, the hybrid model outperforms the traditional ARIMA model, which may facilitate the allocation of health resources in China. PMID:24223232

Consequences of land-cover misclassification in models of impervious surface

USGS Publications Warehouse

McMahon, G.

2007-01-01

Model estimates of impervious area as a function of landcover area may be biased and imprecise because of errors in the land-cover classification. This investigation of the effects of land-cover misclassification on impervious surface models that use National Land Cover Data (NLCD) evaluates the consequences of adjusting land-cover within a watershed to reflect uncertainty assessment information. Model validation results indicate that using error-matrix information to adjust land-cover values used in impervious surface models does not substantially improve impervious surface predictions. Validation results indicate that the resolution of the landcover data (Level I and Level II) is more important in predicting impervious surface accurately than whether the land-cover data have been adjusted using information in the error matrix. Level I NLCD, adjusted for land-cover misclassification, is preferable to the other land-cover options for use in models of impervious surface. This result is tied to the lower classification error rates for the Level I NLCD. ?? 2007 American Society for Photogrammetry and Remote Sensing.

Preemption versus Entrenchment: Towards a Construction-General Solution to the Problem of the Retreat from Verb Argument Structure Overgeneralization

PubMed Central

Ambridge, Ben; Bidgood, Amy; Twomey, Katherine E.; Pine, Julian M.; Rowland, Caroline F.; Freudenthal, Daniel

2015-01-01

Participants aged 5;2-6;8, 9;2-10;6 and 18;1-22;2 (72 at each age) rated verb argument structure overgeneralization errors (e.g., *Daddy giggled the baby) using a five-point scale. The study was designed to investigate the feasibility of two proposed construction-general solutions to the question of how children retreat from, or avoid, such errors. No support was found for the prediction of the preemption hypothesis that the greater the frequency of the verb in the single most nearly synonymous construction (for this example, the periphrastic causative; e.g., Daddy made the baby giggle), the lower the acceptability of the error. Support was found, however, for the prediction of the entrenchment hypothesis that the greater the overall frequency of the verb, regardless of construction, the lower the acceptability of the error, at least for the two older groups. Thus while entrenchment appears to be a robust solution to the problem of the retreat from error, and one that generalizes across different error types, we did not find evidence that this is the case for preemption. The implication is that the solution to the retreat from error lies not with specialized mechanisms, but rather in a probabilistic process of construction competition. PMID:25919003

Preemption versus Entrenchment: Towards a Construction-General Solution to the Problem of the Retreat from Verb Argument Structure Overgeneralization.

PubMed

Ambridge, Ben; Bidgood, Amy; Twomey, Katherine E; Pine, Julian M; Rowland, Caroline F; Freudenthal, Daniel

2014-01-01

Participants aged 5;2-6;8, 9;2-10;6 and 18;1-22;2 (72 at each age) rated verb argument structure overgeneralization errors (e.g., *Daddy giggled the baby) using a five-point scale. The study was designed to investigate the feasibility of two proposed construction-general solutions to the question of how children retreat from, or avoid, such errors. No support was found for the prediction of the preemption hypothesis that the greater the frequency of the verb in the single most nearly synonymous construction (for this example, the periphrastic causative; e.g., Daddy made the baby giggle), the lower the acceptability of the error. Support was found, however, for the prediction of the entrenchment hypothesis that the greater the overall frequency of the verb, regardless of construction, the lower the acceptability of the error, at least for the two older groups. Thus while entrenchment appears to be a robust solution to the problem of the retreat from error, and one that generalizes across different error types, we did not find evidence that this is the case for preemption. The implication is that the solution to the retreat from error lies not with specialized mechanisms, but rather in a probabilistic process of construction competition.

Influence of Subjectivity in Geological Mapping on the Net Penetration Rate Prediction for a Hard Rock TBM

NASA Astrophysics Data System (ADS)

Seo, Yongbeom; Macias, Francisco Javier; Jakobsen, Pål Drevland; Bruland, Amund

2018-05-01

The net penetration rate of hard rock tunnel boring machines (TBM) is influenced by rock mass degree of fracturing. This influence is taken into account in the NTNU prediction model by the rock mass fracturing factor ( k s). k s is evaluated by geological mapping, the measurement of the orientation of fractures and the spacing of fractures and fracture type. Geological mapping is a subjective procedure. Mapping results can therefore contain considerable uncertainty. The mapping data of a tunnel mapped by three researchers were compared, and the influence of the variation in geological mapping was estimated to assess the influence of subjectivity in geological mapping. This study compares predicted net penetration rates and actual net penetration rates for TBM tunneling (from field data) and suggests mapping methods that can reduce the error related to subjectivity. The main findings of this paper are as follows: (1) variation of mapping data between individuals; (2) effect of observed variation on uncertainty in predicted net penetration rates; (3) influence of mapping methods on the difference between predicted and actual net penetration rate.

Coherent errors in quantum error correction

NASA Astrophysics Data System (ADS)

Greenbaum, Daniel; Dutton, Zachary

Analysis of quantum error correcting (QEC) codes is typically done using a stochastic, Pauli channel error model for describing the noise on physical qubits. However, it was recently found that coherent errors (systematic rotations) on physical data qubits result in both physical and logical error rates that differ significantly from those predicted by a Pauli model. We present analytic results for the logical error as a function of concatenation level and code distance for coherent errors under the repetition code. For data-only coherent errors, we find that the logical error is partially coherent and therefore non-Pauli. However, the coherent part of the error is negligible after two or more concatenation levels or at fewer than ɛ - (d - 1) error correction cycles. Here ɛ << 1 is the rotation angle error per cycle for a single physical qubit and d is the code distance. These results support the validity of modeling coherent errors using a Pauli channel under some minimum requirements for code distance and/or concatenation. We discuss extensions to imperfect syndrome extraction and implications for general QEC.

Application of a bioenergetics model for hatchery production: Largemouth bass fed commercial diets

USGS Publications Warehouse

Csargo, Isak J.; Michael L. Brown,; Chipps, Steven R.

2012-01-01

Fish bioenergetics models based on natural prey items have been widely used to address research and management questions. However, few attempts have been made to evaluate and apply bioenergetics models to hatchery-reared fish receiving commercial feeds that contain substantially higher energy densities than natural prey. In this study, we evaluated a bioenergetics model for age-0 largemouth bass Micropterus salmoidesreared on four commercial feeds. Largemouth bass (n ≈ 3,504) were reared for 70 d at 25°C in sixteen 833-L circular tanks connected in parallel to a recirculation system. Model performance was evaluated using error components (mean, slope, and random) derived from decomposition of the mean square error obtained from regression of observed on predicted values. Mean predicted consumption was only 8.9% lower than mean observed consumption and was similar to error rates observed for largemouth bass consuming natural prey. Model evaluation showed that the 97.5% joint confidence region included the intercept of 0 (−0.43 ± 3.65) and slope of 1 (1.08 ± 0.20), which indicates the model accurately predicted consumption. Moreover model error was similar among feeds (P = 0.98), and most error was probably attributable to sampling error (unconsumed feed), underestimated predator energy densities, or consumption-dependent error, which is common in bioenergetics models. This bioenergetics model could provide a valuable tool in hatchery production of largemouth bass. Furthermore, we believe that bioenergetics modeling could be useful in aquaculture production, particularly for species lacking historical hatchery constants or conventional growth models.

A computational substrate for incentive salience.

PubMed

McClure, Samuel M; Daw, Nathaniel D; Montague, P Read

2003-08-01

Theories of dopamine function are at a crossroads. Computational models derived from single-unit recordings capture changes in dopaminergic neuron firing rate as a prediction error signal. These models employ the prediction error signal in two roles: learning to predict future rewarding events and biasing action choice. Conversely, pharmacological inhibition or lesion of dopaminergic neuron function diminishes the ability of an animal to motivate behaviors directed at acquiring rewards. These lesion experiments have raised the possibility that dopamine release encodes a measure of the incentive value of a contemplated behavioral act. The most complete psychological idea that captures this notion frames the dopamine signal as carrying 'incentive salience'. On the surface, these two competing accounts of dopamine function seem incommensurate. To the contrary, we demonstrate that both of these functions can be captured in a single computational model of the involvement of dopamine in reward prediction for the purpose of reward seeking.

Driver's mental workload prediction model based on physiological indices.

PubMed

Yan, Shengyuan; Tran, Cong Chi; Wei, Yingying; Habiyaremye, Jean Luc

2017-09-15

Developing an early warning model to predict the driver's mental workload (MWL) is critical and helpful, especially for new or less experienced drivers. The present study aims to investigate the correlation between new drivers' MWL and their work performance, regarding the number of errors. Additionally, the group method of data handling is used to establish the driver's MWL predictive model based on subjective rating (NASA task load index [NASA-TLX]) and six physiological indices. The results indicate that the NASA-TLX and the number of errors are positively correlated, and the predictive model shows the validity of the proposed model with an R 2 value of 0.745. The proposed model is expected to provide a reference value for the new drivers of their MWL by providing the physiological indices, and the driving lesson plans can be proposed to sustain an appropriate MWL as well as improve the driver's work performance.

Ecological impacts and management strategies for western larch in the face of climate-change

Treesearch

Gerald E. Rehfeldt; Barry C. Jaquish

2010-01-01

Approximately 185,000 forest inventory and ecological plots from both USA and Canada were used to predict the contemporary distribution of western larch (Larix occidentalis Nutt.) from climate variables. The random forests algorithm, using an 8-variable model, produced an overall error rate of about 2.9 %, nearly all of which consisted of predicting presence at...

Errors in Multi-Digit Arithmetic and Behavioral Inattention in Children With Math Difficulties

PubMed Central

Raghubar, Kimberly; Cirino, Paul; Barnes, Marcia; Ewing-Cobbs, Linda; Fletcher, Jack; Fuchs, Lynn

2009-01-01

Errors in written multi-digit computation were investigated in children with math difficulties. Third-and fourth-grade children (n = 291) with coexisting math and reading difficulties, math difficulties, reading difficulties, or no learning difficulties were compared. A second analysis compared those with severe math learning difficulties, low average achievement in math, and no learning difficulties. Math fact errors were related to the severity of the math difficulties, not to reading status. Contrary to predictions, children with poorer reading, regardless of math achievement, committed more visually based errors. Operation switch errors were not systematically related to group membership. Teacher ratings of behavioral inattention were related to accuracy, math fact errors, and procedural bugs. The findings are discussed with respect to hypotheses about the cognitive origins of arithmetic errors and in relation to current discussions about how to conceptualize math disabilities. PMID:19380494

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.

The balanced mind: the variability of task-unrelated thoughts predicts error monitoring

PubMed Central

Allen, Micah; Smallwood, Jonathan; Christensen, Joanna; Gramm, Daniel; Rasmussen, Beinta; Jensen, Christian Gaden; Roepstorff, Andreas; Lutz, Antoine

2013-01-01

Self-generated thoughts unrelated to ongoing activities, also known as “mind-wandering,” make up a substantial portion of our daily lives. Reports of such task-unrelated thoughts (TUTs) predict both poor performance on demanding cognitive tasks and blood-oxygen-level-dependent (BOLD) activity in the default mode network (DMN). However, recent findings suggest that TUTs and the DMN can also facilitate metacognitive abilities and related behaviors. To further understand these relationships, we examined the influence of subjective intensity, ruminative quality, and variability of mind-wandering on response inhibition and monitoring, using the Error Awareness Task (EAT). We expected to replicate links between TUT and reduced inhibition, and explored whether variance in TUT would predict improved error monitoring, reflecting a capacity to balance between internal and external cognition. By analyzing BOLD responses to subjective probes and the EAT, we dissociated contributions of the DMN, executive, and salience networks to task performance. While both response inhibition and online TUT ratings modulated BOLD activity in the medial prefrontal cortex (mPFC) of the DMN, the former recruited a more dorsal area implying functional segregation. We further found that individual differences in mean TUTs strongly predicted EAT stop accuracy, while TUT variability specifically predicted levels of error awareness. Interestingly, we also observed co-activation of salience and default mode regions during error awareness, supporting a link between monitoring and TUTs. Altogether our results suggest that although TUT is detrimental to task performance, fluctuations in attention between self-generated and external task-related thought is a characteristic of individuals with greater metacognitive monitoring capacity. Achieving a balance between internally and externally oriented thought may thus aid individuals in optimizing their task performance. PMID:24223545

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.

The study of PDF turbulence models in combustion

NASA Technical Reports Server (NTRS)

Hsu, Andrew T.

1991-01-01

The accurate prediction of turbulent combustion is still beyond reach for today's computation techniques. It is the consensus of the combustion profession that the predictions of chemically reacting flow were poor if conventional turbulence models were used. The main difficulty lies in the fact that the reaction rate is highly nonlinear, and the use of averaged temperature, pressure, and density produces excessively large errors. The probability density function (PDF) method is the only alternative at the present time that uses local instant values of the temperature, density, etc. in predicting chemical reaction rate, and thus it is the only viable approach for turbulent combustion calculations.

Effects of Target Fragmentation on Evaluation of LET Spectra From Space Radiation in Low-Earth Orbit (LEO) Environment: Impact on SEU Predictions

NASA Technical Reports Server (NTRS)

Shinn, J. L.; Cucinotta, F. A.; Badhwar, G. D.; ONeill, P. M.; Badavi, F. F.

1995-01-01

Recent improvements in the radiation transport code HZETRN/BRYNTRN and galactic cosmic ray environmental model have provided an opportunity to investigate the effects of target fragmentation on estimates of single event upset (SEU) rates for spacecraft memory devices. Since target fragments are mostly of very low energy, an SEU prediction model has been derived in terms of particle energy rather than linear energy transfer (LET) to account for nonlinear relationship between range and energy. Predictions are made for SEU rates observed on two Shuttle flights, each at low and high inclination orbit. Corrections due to track structure effects are made for both high energy ions with track structure larger than device sensitive volume and for low energy ions with dense track where charge recombination is important. Results indicate contributions from target fragments are relatively important at large shield depths (or any thick structure material) and at low inclination orbit. Consequently, a more consistent set of predictions for upset rates observed in these two flights is reached when compared to an earlier analysis with CREME model. It is also observed that the errors produced by assuming linear relationship in range and energy in the earlier analysis have fortuitously canceled out the errors for not considering target fragmentation and track structure effects.

SU-G-BRB-03: Assessing the Sensitivity and False Positive Rate of the Integrated Quality Monitor (IQM) Large Area Ion Chamber to MLC Positioning Errors

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

Boehnke, E McKenzie; DeMarco, J; Steers, J

2016-06-15

Purpose: To examine both the IQM’s sensitivity and false positive rate to varying MLC errors. By balancing these two characteristics, an optimal tolerance value can be derived. Methods: An un-modified SBRT Liver IMRT plan containing 7 fields was randomly selected as a representative clinical case. The active MLC positions for all fields were perturbed randomly from a square distribution of varying width (±1mm to ±5mm). These unmodified and modified plans were measured multiple times each by the IQM (a large area ion chamber mounted to a TrueBeam linac head). Measurements were analyzed relative to the initial, unmodified measurement. IQM readingsmore » are analyzed as a function of control points. In order to examine sensitivity to errors along a field’s delivery, each measured field was divided into 5 groups of control points, and the maximum error in each group was recorded. Since the plans have known errors, we compared how well the IQM is able to differentiate between unmodified and error plans. ROC curves and logistic regression were used to analyze this, independent of thresholds. Results: A likelihood-ratio Chi-square test showed that the IQM could significantly predict whether a plan had MLC errors, with the exception of the beginning and ending control points. Upon further examination, we determined there was ramp-up occurring at the beginning of delivery. Once the linac AFC was tuned, the subsequent measurements (relative to a new baseline) showed significant (p <0.005) abilities to predict MLC errors. Using the area under the curve, we show the IQM’s ability to detect errors increases with increasing MLC error (Spearman’s Rho=0.8056, p<0.0001). The optimal IQM count thresholds from the ROC curves are ±3%, ±2%, and ±7% for the beginning, middle 3, and end segments, respectively. Conclusion: The IQM has proven to be able to detect not only MLC errors, but also differences in beam tuning (ramp-up). Partially supported by the Susan Scott Foundation.« less

Evaluation of Mycology Laboratory Proficiency Testing

PubMed Central

Reilly, Andrew A.; Salkin, Ira F.; McGinnis, Michael R.; Gromadzki, Sally; Pasarell, Lester; Kemna, Maggi; Higgins, Nancy; Salfinger, Max

1999-01-01

Changes over the last decade in overt proficiency testing (OPT) regulations have been ostensibly directed at improving laboratory performance on patient samples. However, the overt (unblinded) format of the tests and regulatory penalties associated with incorrect values allow and encourage laboratorians to take extra precautions with OPT analytes. As a result OPT may measure optimal laboratory performance instead of the intended target of typical performance attained during routine patient testing. This study addresses this issue by evaluating medical mycology OPT and comparing its fungal specimen identification error rates to those obtained in a covert (blinded) proficiency testing (CPT) program. Identifications from 188 laboratories participating in the New York State mycology OPT from 1982 to 1994 were compared with the identifications of the same fungi recovered from patient specimens in 1989 and 1994 as part of the routine procedures of 88 of these laboratories. The consistency in the identification of OPT specimens was sufficient to make accurate predictions of OPT error rates. However, while the error rates in OPT and CPT were similar for Candida albicans, significantly higher error rates were found in CPT for Candida tropicalis, Candida glabrata, and other common pathogenic fungi. These differences may, in part, be due to OPT’s use of ideal organism representatives cultured under optimum growth conditions. This difference, as well as the organism-dependent error rate differences, reflects the limitations of OPT as a means of assessing the quality of routine laboratory performance in medical mycology. PMID:10364601

Prediction of penetration rate of rotary-percussive drilling using artificial neural networks - a case study / Prognozowanie postępu wiercenia przy użyciu wiertła udarowo-obrotowego przy wykorzystaniu sztucznych sieci neuronowych - studium przypadku

NASA Astrophysics Data System (ADS)

Aalizad, Seyed Ali; Rashidinejad, Farshad

2012-12-01

Penetration rate in rocks is one of the most important parameters of determination of drilling economics. Total drilling costs can be determined by predicting the penetration rate and utilized for mine planning. The factors which affect penetration rate are exceedingly numerous and certainly are not completely understood. For the prediction of penetration rate in rotary-percussive drilling, four types of rocks in Sangan mine have been chosen. Sangan is situated in Khorasan-Razavi province in Northeastern Iran. The selected parameters affect penetration rate is divided in three categories: rock properties, drilling condition and drilling pattern. The rock properties are: density, rock quality designation (RQD), uni-axial compressive strength, Brazilian tensile strength, porosity, Mohs hardness, Young modulus, P-wave velocity. Drilling condition parameters are: percussion, rotation, feed (thrust load) and flushing pressure; and parameters for drilling pattern are: blasthole diameter and length. Rock properties were determined in the laboratory, and drilling condition and drilling pattern were determined in the field. For create a correlation between penetration rate and rock properties, drilling condition and drilling pattern, artificial neural networks (ANN) were used. For this purpose, 102 blastholes were observed and drilling condition, drilling pattern and time of drilling in each blasthole were recorded. To obtain a correlation between this data and prediction of penetration rate, MATLAB software was used. To train the pattern of ANN, 77 data has been used and 25 of them found for testing the pattern. Performance of ANN models was assessed through the root mean square error (RMSE) and correlation coefficient (R2). For optimized model (14-14-10-1) RMSE and R2 is 0.1865 and 86%, respectively, and its sensitivity analysis showed that there is a strong correlation between penetration rate and RQD, rotation and blasthole diameter. High correlation coefficient and low root mean square error of these models showed that the ANN is a suitable tool for penetration rate prediction.

Applying EVM to Satellite on Ground and In-Orbit Testing - Better Data in Less Time

NASA Technical Reports Server (NTRS)

Peters, Robert; Lebbink, Elizabeth-Klein; Lee, Victor; Model, Josh; Wezalis, Robert; Taylor, John

2008-01-01

Using Error Vector Magnitude (EVM) in satellite integration and test allows rapid verification of the Bit Error Rate (BER) performance of a satellite link and is particularly well suited to measurement of low bit rate satellite links where it can result in a major reduction in test time (about 3 weeks per satellite for the Geosynchronous Operational Environmental Satellite [GOES] satellites during ground test) and can provide diagnostic information. Empirical techniques developed to predict BER performance from EVM measurements and lessons learned about applying these techniques during GOES N, O, and P integration test and post launch testing, are discussed.

The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS)

PubMed Central

2016-01-01

Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d–1 (RTD-model) and 0.03 ± 0.03 L d–1 (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d–1 and 0.0437 ± 0.02 L d–1, respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations. PMID:27363449

[Fire behavior of Mongolian oak leaves fuel-bed under no-wind and zero-slope conditions. I. Factors affecting fire spread rate and modeling].

PubMed

Jin, Sen; Liu, Bo-Fei; Di, Xue-Ying; Chu, Teng-Fei; Zhang, Ji-Li

2012-01-01

Aimed to understand the fire behavior of Mongolian oak leaves fuel-bed under field condition, the leaves of a secondary Mongolian oak forest in Northeast Forestry University experimental forest farm were collected and brought into laboratory to construct fuel-beds with varied loading, height, and moisture content, and a total of 100 experimental fires were burned under no-wind and zero-slope conditions. It was observed that the fire spread rate of the fuel-beds was less than 0.5 m x min(-1). Fuel-bed loading, height, and moisture contents all had significant effects on the fire spread rate. The effect of fuel-bed moisture content on the fire spread had no significant correlations with fuel-bed loading and height, but the effect of fuel-bed height was related to the fuel-bed loading. The packing ratio of fuel-beds had less effect on the fire spread rate. Taking the fuel-bed loading, height, and moisture content as predictive variables, a prediction model for the fire spread rate of Mongolian oak leaves fuel-bed was established, which could explain 83% of the variance of the fire spread rate, with a mean absolute error 0.04 m x min(-1) and a mean relative error less than 17%.

Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia.

PubMed

Ansari, Mozafar; Othman, Faridah; Abunama, Taher; El-Shafie, Ahmed

2018-04-01

The function of a sewage treatment plant is to treat the sewage to acceptable standards before being discharged into the receiving waters. To design and operate such plants, it is necessary to measure and predict the influent flow rate. In this research, the influent flow rate of a sewage treatment plant (STP) was modelled and predicted by autoregressive integrated moving average (ARIMA), nonlinear autoregressive network (NAR) and support vector machine (SVM) regression time series algorithms. To evaluate the models' accuracy, the root mean square error (RMSE) and coefficient of determination (R 2 ) were calculated as initial assessment measures, while relative error (RE), peak flow criterion (PFC) and low flow criterion (LFC) were calculated as final evaluation measures to demonstrate the detailed accuracy of the selected models. An integrated model was developed based on the individual models' prediction ability for low, average and peak flow. An initial assessment of the results showed that the ARIMA model was the least accurate and the NAR model was the most accurate. The RE results also prove that the SVM model's frequency of errors above 10% or below - 10% was greater than the NAR model's. The influent was also forecasted up to 44 weeks ahead by both models. The graphical results indicate that the NAR model made better predictions than the SVM model. The final evaluation of NAR and SVM demonstrated that SVM made better predictions at peak flow and NAR fit well for low and average inflow ranges. The integrated model developed includes the NAR model for low and average influent and the SVM model for peak inflow.

Stereotype threat can reduce older adults' memory errors

PubMed Central

Barber, Sarah J.; Mather, Mara

2014-01-01

Stereotype threat often incurs the cost of reducing the amount of information that older adults accurately recall. In the current research we tested whether stereotype threat can also benefit memory. According to the regulatory focus account of stereotype threat, threat induces a prevention focus in which people become concerned with avoiding errors of commission and are sensitive to the presence or absence of losses within their environment (Seibt & Förster, 2004). Because of this, we predicted that stereotype threat might reduce older adults' memory errors. Results were consistent with this prediction. Older adults under stereotype threat had lower intrusion rates during free-recall tests (Experiments 1 & 2). They also reduced their false alarms and adopted more conservative response criteria during a recognition test (Experiment 2). Thus, stereotype threat can decrease older adults' false memories, albeit at the cost of fewer veridical memories, as well. PMID:24131297

Frequency, probability, and prediction: easy solutions to cognitive illusions?

PubMed

Griffin, D; Buehler, R

1999-02-01

Many errors in probabilistic judgment have been attributed to people's inability to think in statistical terms when faced with information about a single case. Prior theoretical analyses and empirical results imply that the errors associated with case-specific reasoning may be reduced when people make frequentistic predictions about a set of cases. In studies of three previously identified cognitive biases, we find that frequency-based predictions are different from-but no better than-case-specific judgments of probability. First, in studies of the "planning fallacy, " we compare the accuracy of aggregate frequency and case-specific probability judgments in predictions of students' real-life projects. When aggregate and single-case predictions are collected from different respondents, there is little difference between the two: Both are overly optimistic and show little predictive validity. However, in within-subject comparisons, the aggregate judgments are significantly more conservative than the single-case predictions, though still optimistically biased. Results from studies of overconfidence in general knowledge and base rate neglect in categorical prediction underline a general conclusion. Frequentistic predictions made for sets of events are no more statistically sophisticated, nor more accurate, than predictions made for individual events using subjective probability. Copyright 1999 Academic Press.

Soil pH Errors Propagation from Measurements to Spatial Predictions - Cost Benefit Analysis and Risk Assessment Implications for Practitioners and Modelers

NASA Astrophysics Data System (ADS)

Owens, P. R.; Libohova, Z.; Seybold, C. A.; Wills, S. A.; Peaslee, S.; Beaudette, D.; Lindbo, D. L.

2017-12-01

The measurement errors and spatial prediction uncertainties of soil properties in the modeling community are usually assessed against measured values when available. However, of equal importance is the assessment of errors and uncertainty impacts on cost benefit analysis and risk assessments. Soil pH was selected as one of the most commonly measured soil properties used for liming recommendations. The objective of this study was to assess the error size from different sources and their implications with respect to management decisions. Error sources include measurement methods, laboratory sources, pedotransfer functions, database transections, spatial aggregations, etc. Several databases of measured and predicted soil pH were used for this study including the United States National Cooperative Soil Survey Characterization Database (NCSS-SCDB), the US Soil Survey Geographic (SSURGO) Database. The distribution of errors among different sources from measurement methods to spatial aggregation showed a wide range of values. The greatest RMSE of 0.79 pH units was from spatial aggregation (SSURGO vs Kriging), while the measurement methods had the lowest RMSE of 0.06 pH units. Assuming the order of data acquisition based on the transaction distance i.e. from measurement method to spatial aggregation the RMSE increased from 0.06 to 0.8 pH units suggesting an "error propagation". This has major implications for practitioners and modeling community. Most soil liming rate recommendations are based on 0.1 pH unit increments, while the desired soil pH level increments are based on 0.4 to 0.5 pH units. Thus, even when the measured and desired target soil pH are the same most guidelines recommend 1 ton ha-1 lime, which translates in 111 ha-1 that the farmer has to factor in the cost-benefit analysis. However, this analysis need to be based on uncertainty predictions (0.5-1.0 pH units) rather than measurement errors (0.1 pH units) which would translate in 555-1,111 investment that need to be assessed against the risk. The modeling community can benefit from such analysis, however, error size and spatial distribution for global and regional predictions need to be assessed against the variability of other drivers and impact on management decisions.

Development of a time-stepping sediment budget model for assessing land use impacts in large river basins.

PubMed

Wilkinson, S N; Dougall, C; Kinsey-Henderson, A E; Searle, R D; Ellis, R J; Bartley, R

2014-01-15

The use of river basin modelling to guide mitigation of non-point source pollution of wetlands, estuaries and coastal waters has become widespread. To assess and simulate the impacts of alternate land use or climate scenarios on river washload requires modelling techniques that represent sediment sources and transport at the time scales of system response. Building on the mean-annual SedNet model, we propose a new D-SedNet model which constructs daily budgets of fine sediment sources, transport and deposition for each link in a river network. Erosion rates (hillslope, gully and streambank erosion) and fine sediment sinks (floodplains and reservoirs) are disaggregated from mean annual rates based on daily rainfall and runoff. The model is evaluated in the Burdekin basin in tropical Australia, where policy targets have been set for reducing sediment and nutrient loads to the Great Barrier Reef (GBR) lagoon from grazing and cropping land. D-SedNet predicted annual loads with similar performance to that of a sediment rating curve calibrated to monitored suspended sediment concentrations. Relative to a 22-year reference load time series at the basin outlet derived from a dynamic general additive model based on monitoring data, D-SedNet had a median absolute error of 68% compared with 112% for the rating curve. RMS error was slightly higher for D-SedNet than for the rating curve due to large relative errors on small loads in several drought years. This accuracy is similar to existing agricultural system models used in arable or humid environments. Predicted river loads were sensitive to ground vegetation cover. We conclude that the river network sediment budget model provides some capacity for predicting load time-series independent of monitoring data in ungauged basins, and for evaluating the impact of land management on river sediment load time-series, which is challenging across large regions in data-poor environments. © 2013. Published by Elsevier B.V. All rights reserved.

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

Simulating Memory Impairment for Child Sexual Abuse.

PubMed

Newton, Jeremy W; Hobbs, Sue D

2015-08-01

The current study investigated effects of simulated memory impairment on recall of child sexual abuse (CSA) information. A total of 144 adults were tested for memory of a written CSA scenario in which they role-played as the victim. There were four experimental groups and two testing sessions. During Session 1, participants read a CSA story and recalled it truthfully (Genuine group), omitted CSA information (Omission group), exaggerated CSA information (Commission group), or did not recall the story at all (No Rehearsal group). One week later, at Session 2, all participants were told to recount the scenario truthfully, and their memory was then tested using free recall and cued recall questions. The Session 1 manipulation affected memory accuracy during Session 2. Specifically, compared with the Genuine group's performance, the Omission, Commission, or No Rehearsal groups' performance was characterized by increased omission and commission errors and decreased reporting of correct details. Victim blame ratings (i.e., victim responsibility and provocativeness) and participant gender predicted increased error and decreased accuracy, whereas perpetrator blame ratings predicted decreased error and increased accuracy. Findings are discussed in relation to factors that may affect memory for CSA information. Copyright © 2015 John Wiley & Sons, Ltd.

Mitigating TCP Degradation over Intermittent Link Failures using Intermediate Buffers

DTIC Science & Technology

2006-06-01

signal strength [10]. The Preemptive routing in ad hoc networks [10] attempts to predict that a route will fail by looking at the signal power of the...when the error rate is high there are non -optimal back offs in the Retransmission Timeout. And third, in the high error situation the slow start...network storage follows. In Beck et. al. [3], Logistical Networking is outlined as a means of storing data throughout the network. End to end

Preschool Inhibitory Control Predicts ADHD Group Status and Inhibitory Weakness in School.

PubMed

Jacobson, Lisa A; Schneider, Heather; Mahone, E Mark

2017-12-26

Discriminative utility of performance measures of inhibitory control was examined in preschool children with and without ADHD to determine whether performance measures added to diagnostic prediction and to prediction of informant-rated day-to-day executive function. Children ages 4-5 years (N = 105, 61% boys; 54 ADHD, medication-naïve) were assessed using performance measures (Auditory Continuous Performance Test for Preschoolers-Commission errors, Conflicting Motor Response Test, NEPSY Statue) and caregiver (parent, teacher) ratings of inhibition (Behavior Rating Inventory of Executive Function-Preschool version). Performance measures and parent and teacher reports of inhibitory control significantly and uniquely predicted ADHD group status; however, performance measures did not add to prediction of group status beyond parent reports. Performance measures did significantly predict classroom inhibitory control (teacher ratings), over and above parent reports of inhibitory control. Performance measures of inhibitory control may be adequate predictors of ADHD status and good predictors of young children's classroom inhibitory control, demonstrating utility as components of clinical assessments. © The Author(s) 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

Controlling false-negative errors in microarray differential expression analysis: a PRIM approach.

PubMed

Cole, Steve W; Galic, Zoran; Zack, Jerome A

2003-09-22

Theoretical considerations suggest that current microarray screening algorithms may fail to detect many true differences in gene expression (Type II analytic errors). We assessed 'false negative' error rates in differential expression analyses by conventional linear statistical models (e.g. t-test), microarray-adapted variants (e.g. SAM, Cyber-T), and a novel strategy based on hold-out cross-validation. The latter approach employs the machine-learning algorithm Patient Rule Induction Method (PRIM) to infer minimum thresholds for reliable change in gene expression from Boolean conjunctions of fold-induction and raw fluorescence measurements. Monte Carlo analyses based on four empirical data sets show that conventional statistical models and their microarray-adapted variants overlook more than 50% of genes showing significant up-regulation. Conjoint PRIM prediction rules recover approximately twice as many differentially expressed transcripts while maintaining strong control over false-positive (Type I) errors. As a result, experimental replication rates increase and total analytic error rates decline. RT-PCR studies confirm that gene inductions detected by PRIM but overlooked by other methods represent true changes in mRNA levels. PRIM-based conjoint inference rules thus represent an improved strategy for high-sensitivity screening of DNA microarrays. Freestanding JAVA application at http://microarray.crump.ucla.edu/focus

Expanded prediction equations of human sweat loss and water needs.

PubMed

Gonzalez, R R; Cheuvront, S N; Montain, S J; Goodman, D A; Blanchard, L A; Berglund, L G; Sawka, M N

2009-08-01

The Institute of Medicine expressed a need for improved sweating rate (msw) prediction models that calculate hourly and daily water needs based on metabolic rate, clothing, and environment. More than 25 years ago, the original Shapiro prediction equation (OSE) was formulated as msw (g.m(-2).h(-1))=27.9.Ereq.(Emax)(-0.455), where Ereq is required evaporative heat loss and Emax is maximum evaporative power of the environment; OSE was developed for a limited set of environments, exposures times, and clothing systems. Recent evidence shows that OSE often overpredicts fluid needs. Our study developed a corrected OSE and a new msw prediction equation by using independent data sets from a wide range of environmental conditions, metabolic rates (rest to 500 observations) by using a variety of metabolic rates over a range of environmental conditions (ambient temperature, 15-46 degrees C; water vapor pressure, 0.27-4.45 kPa; wind speed, 0.4-2.5 m/s), clothing, and equipment combinations and durations (2-8 h). Data are expressed as grams per square meter per hour and were analyzed using fuzzy piecewise regression. OSE overpredicted sweating rates (P<0.003) compared with observed msw. Both the correction equation (OSEC), msw=147.exp (0.0012.OSE), and a new piecewise (PW) equation, msw=147+1.527.Ereq-0.87.Emax were derived, compared with OSE, and then cross-validated against independent data (21 males and 9 females; >200 observations). OSEC and PW were more accurate predictors of sweating rate (58 and 65% more accurate, P<0.01) and produced minimal error (standard error estimate<100 g.m(-2).h(-1)) for conditions both within and outside the original OSE domain of validity. The new equations provide for more accurate sweat predictions over a broader range of conditions with applications to public health, military, occupational, and sports medicine settings.

Verbal Paradata and Survey Error: Respondent Speech, Voice, and Question-Answering Behavior Can Predict Income Item Nonresponse

ERIC Educational Resources Information Center

Jans, Matthew E.

2010-01-01

Income nonresponse is a significant problem in survey data, with rates as high as 50%, yet we know little about why it occurs. It is plausible that the way respondents answer survey questions (e.g., their voice and speech characteristics, and their question- answering behavior) can predict whether they will provide income data, and will reflect…

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.

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…

Flood loss model transfer: on the value of additional data

NASA Astrophysics Data System (ADS)

Schröter, Kai; Lüdtke, Stefan; Vogel, Kristin; Kreibich, Heidi; Thieken, Annegret; Merz, Bruno

2017-04-01

The transfer of models across geographical regions and flood events is a key challenge in flood loss estimation. Variations in local characteristics and continuous system changes require regional adjustments and continuous updating with current evidence. However, acquiring data on damage influencing factors is expensive and therefore assessing the value of additional data in terms of model reliability and performance improvement is of high relevance. The present study utilizes empirical flood loss data on direct damage to residential buildings available from computer aided telephone interviews that were carried out after the floods in 2002, 2005, 2006, 2010, 2011 and 2013 mainly in the Elbe and Danube catchments in Germany. Flood loss model performance is assessed for incrementally increased numbers of loss data which are differentiated according to region and flood event. Two flood loss modeling approaches are considered: (i) a multi-variable flood loss model approach using Random Forests and (ii) a uni-variable stage damage function. Both model approaches are embedded in a bootstrapping process which allows evaluating the uncertainty of model predictions. Predictive performance of both models is evaluated with regard to mean bias, mean absolute and mean squared errors, as well as hit rate and sharpness. Mean bias and mean absolute error give information about the accuracy of model predictions; mean squared error and sharpness about precision and hit rate is an indicator for model reliability. The results of incremental, regional and temporal updating demonstrate the usefulness of additional data to improve model predictive performance and increase model reliability, particularly in a spatial-temporal transfer setting.

Using APEX to Model Anticipated Human Error: Analysis of a GPS Navigational Aid

NASA Technical Reports Server (NTRS)

VanSelst, Mark; Freed, Michael; Shefto, Michael (Technical Monitor)

1997-01-01

The interface development process can be dramatically improved by predicting design facilitated human error at an early stage in the design process. The approach we advocate is to SIMULATE the behavior of a human agent carrying out tasks with a well-specified user interface, ANALYZE the simulation for instances of human error, and then REFINE the interface or protocol to minimize predicted error. This approach, incorporated into the APEX modeling architecture, differs from past approaches to human simulation in Its emphasis on error rather than e.g. learning rate or speed of response. The APEX model consists of two major components: (1) a powerful action selection component capable of simulating behavior in complex, multiple-task environments; and (2) a resource architecture which constrains cognitive, perceptual, and motor capabilities to within empirically demonstrated limits. The model mimics human errors arising from interactions between limited human resources and elements of the computer interface whose design falls to anticipate those limits. We analyze the design of a hand-held Global Positioning System (GPS) device used for radical and navigational decisions in small yacht recalls. The analysis demonstrates how human system modeling can be an effective design aid, helping to accelerate the process of refining a product (or procedure).

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.

MASTER: a model to improve and standardize clinical breakpoints for antimicrobial susceptibility testing using forecast probabilities.

PubMed

Blöchliger, Nicolas; Keller, Peter M; Böttger, Erik C; Hombach, Michael

2017-09-01

The procedure for setting clinical breakpoints (CBPs) for antimicrobial susceptibility has been poorly standardized with respect to population data, pharmacokinetic parameters and clinical outcome. Tools to standardize CBP setting could result in improved antibiogram forecast probabilities. We propose a model to estimate probabilities for methodological categorization errors and defined zones of methodological uncertainty (ZMUs), i.e. ranges of zone diameters that cannot reliably be classified. The impact of ZMUs on methodological error rates was used for CBP optimization. The model distinguishes theoretical true inhibition zone diameters from observed diameters, which suffer from methodological variation. True diameter distributions are described with a normal mixture model. The model was fitted to observed inhibition zone diameters of clinical Escherichia coli strains. Repeated measurements for a quality control strain were used to quantify methodological variation. For 9 of 13 antibiotics analysed, our model predicted error rates of < 0.1% applying current EUCAST CBPs. Error rates were > 0.1% for ampicillin, cefoxitin, cefuroxime and amoxicillin/clavulanic acid. Increasing the susceptible CBP (cefoxitin) and introducing ZMUs (ampicillin, cefuroxime, amoxicillin/clavulanic acid) decreased error rates to < 0.1%. ZMUs contained low numbers of isolates for ampicillin and cefuroxime (3% and 6%), whereas the ZMU for amoxicillin/clavulanic acid contained 41% of all isolates and was considered not practical. We demonstrate that CBPs can be improved and standardized by minimizing methodological categorization error rates. ZMUs may be introduced if an intermediate zone is not appropriate for pharmacokinetic/pharmacodynamic or drug dosing reasons. Optimized CBPs will provide a standardized antibiotic susceptibility testing interpretation at a defined level of probability. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Hardness assurance for proton direct ionization-induced SEEs using a high-energy proton beam

DOE PAGES

Dodds, Nathaniel Anson; Schwank, James R.; Shaneyfelt, Marty R.; ...

2014-11-06

The low-energy proton energy spectra of all shielded space environments have the same shape. This shape is easily reproduced in the laboratory by degrading a high-energy proton beam, producing a high-fidelity test environment. We use this test environment to dramatically simplify rate prediction for proton direct ionization effects, allowing the work to be done at high-energy proton facilities, on encapsulated parts, without knowledge of the IC design, and with little or no computer simulations required. Proton direct ionization (PDI) is predicted to significantly contribute to the total error rate under the conditions investigated. Scaling effects are discussed using data frommore » 65-nm, 45-nm, and 32-nm SOI SRAMs. These data also show that grazing-angle protons will dominate the PDI-induced error rate due to their higher effective LET, so PDI hardness assurance methods must account for angular effects to be conservative. As a result, we show that this angular dependence can be exploited to quickly assess whether an IC is susceptible to PDI.« less

Optimal information transfer in enzymatic networks: A field theoretic formulation

NASA Astrophysics Data System (ADS)

Samanta, Himadri S.; Hinczewski, Michael; Thirumalai, D.

2017-07-01

Signaling in enzymatic networks is typically triggered by environmental fluctuations, resulting in a series of stochastic chemical reactions, leading to corruption of the signal by noise. For example, information flow is initiated by binding of extracellular ligands to receptors, which is transmitted through a cascade involving kinase-phosphatase stochastic chemical reactions. For a class of such networks, we develop a general field-theoretic approach to calculate the error in signal transmission as a function of an appropriate control variable. Application of the theory to a simple push-pull network, a module in the kinase-phosphatase cascade, recovers the exact results for error in signal transmission previously obtained using umbral calculus [Hinczewski and Thirumalai, Phys. Rev. X 4, 041017 (2014), 10.1103/PhysRevX.4.041017]. We illustrate the generality of the theory by studying the minimal errors in noise reduction in a reaction cascade with two connected push-pull modules. Such a cascade behaves as an effective three-species network with a pseudointermediate. In this case, optimal information transfer, resulting in the smallest square of the error between the input and output, occurs with a time delay, which is given by the inverse of the decay rate of the pseudointermediate. Surprisingly, in these examples the minimum error computed using simulations that take nonlinearities and discrete nature of molecules into account coincides with the predictions of a linear theory. In contrast, there are substantial deviations between simulations and predictions of the linear theory in error in signal propagation in an enzymatic push-pull network for a certain range of parameters. Inclusion of second-order perturbative corrections shows that differences between simulations and theoretical predictions are minimized. Our study establishes that a field theoretic formulation of stochastic biological signaling offers a systematic way to understand error propagation in networks of arbitrary complexity.

Longitudinal decline of driving safety in Parkinson disease.

PubMed

Uc, Ergun Y; Rizzo, Matthew; O'Shea, Amy M J; Anderson, Steven W; Dawson, Jeffrey D

2017-11-07

To longitudinally assess and predict on-road driving safety in Parkinson disease (PD). Drivers with PD (n = 67) and healthy controls (n = 110) drove a standardized route in an instrumented vehicle and were invited to return 2 years later. A professional driving expert reviewed drive data and videos to score safety errors. At baseline, drivers with PD performed worse on visual, cognitive, and motor tests, and committed more road safety errors compared to controls (median PD 38.0 vs controls 30.5; p < 0.001). A smaller proportion of drivers with PD returned for repeat testing (42.8% vs 62.7%; p < 0.01). At baseline, returnees with PD made fewer errors than nonreturnees with PD (median 34.5 vs 40.0; p < 0.05) and performed similar to control returnees (median 33). Baseline global cognitive performance of returnees with PD was better than that of nonreturnees with PD, but worse than for control returnees ( p < 0.05). After 2 years, returnees with PD showed greater cognitive decline and larger increase in error counts than control returnees (median increase PD 13.5 vs controls 3.0; p < 0.001). Driving error count increase in the returnees with PD was predicted by greater error count and worse visual acuity at baseline, and by greater interval worsening of global cognition, Unified Parkinson's Disease Rating Scale activities of daily living score, executive functions, visual processing speed, and attention. Despite drop out of the more impaired drivers within the PD cohort, returning drivers with PD, who drove like controls without PD at baseline, showed many more driving safety errors than controls after 2 years. Driving decline in PD was predicted by baseline driving performance and deterioration of cognitive, visual, and functional abnormalities on follow-up. © 2017 American Academy of Neurology.

Comparing different models of the development of verb inflection in early child Spanish.

PubMed

Aguado-Orea, Javier; Pine, Julian M

2015-01-01

How children acquire knowledge of verb inflection is a long-standing question in language acquisition research. In the present study, we test the predictions of some current constructivist and generativist accounts of the development of verb inflection by focusing on data from two Spanish-speaking children between the ages of 2;0 and 2;6. The constructivist claim that children's early knowledge of verb inflection is only partially productive is tested by comparing the average number of different inflections per verb in matched samples of child and adult speech. The generativist claim that children's early use of verb inflection is essentially error-free is tested by investigating the rate at which the children made subject-verb agreement errors in different parts of the present tense paradigm. Our results show: 1) that, although even adults' use of verb inflection in Spanish tends to look somewhat lexically restricted, both children's use of verb inflection was significantly less flexible than that of their caregivers, and 2) that, although the rate at which the two children produced subject-verb agreement errors in their speech was very low, this overall error rate hid a consistent pattern of error in which error rates were substantially higher in low frequency than in high frequency contexts, and substantially higher for low frequency than for high frequency verbs. These results undermine the claim that children's use of verb inflection is fully productive from the earliest observable stages, and are consistent with the constructivist claim that knowledge of verb inflection develops only gradually.

New architecture for dynamic frame-skipping transcoder.

PubMed

Fung, Kai-Tat; Chan, Yui-Lam; Siu, Wan-Chi

2002-01-01

Transcoding is a key technique for reducing the bit rate of a previously compressed video signal. A high transcoding ratio may result in an unacceptable picture quality when the full frame rate of the incoming video bitstream is used. Frame skipping is often used as an efficient scheme to allocate more bits to the representative frames, so that an acceptable quality for each frame can be maintained. However, the skipped frame must be decompressed completely, which might act as a reference frame to nonskipped frames for reconstruction. The newly quantized discrete cosine transform (DCT) coefficients of the prediction errors need to be re-computed for the nonskipped frame with reference to the previous nonskipped frame; this can create undesirable complexity as well as introduce re-encoding errors. In this paper, we propose new algorithms and a novel architecture for frame-rate reduction to improve picture quality and to reduce complexity. The proposed architecture is mainly performed on the DCT domain to achieve a transcoder with low complexity. With the direct addition of DCT coefficients and an error compensation feedback loop, re-encoding errors are reduced significantly. Furthermore, we propose a frame-rate control scheme which can dynamically adjust the number of skipped frames according to the incoming motion vectors and re-encoding errors due to transcoding such that the decoded sequence can have a smooth motion as well as better transcoded pictures. Experimental results show that, as compared to the conventional transcoder, the new architecture for frame-skipping transcoder is more robust, produces fewer requantization errors, and has reduced computational complexity.

Comparison of intraocular lens power prediction using immersion ultrasound and optical biometry with and without formula optimization.

PubMed

Nemeth, Gabor; Nagy, Attila; Berta, Andras; Modis, Laszlo

2012-09-01

Comparison of postoperative refraction results using ultrasound biometry with closed immersion shell and optical biometry. Three hundred and sixty-four eyes of 306 patients (age: 70.6 ± 12.8 years) underwent cataract surgery where intraocular lenses calculated by SRK/T formula were implanted. In 159 cases immersion ultrasonic biometry, in 205 eyes optical biometry was used. Differences between predicted and actual postoperative refractions were calculated both prior to and after optimization with the SRK/T formula, after which we analysed the similar data in the case of Holladay, Haigis, and Hoffer-Q formulas. Mean absolute error (MAE) and the percentage rate of patients within ±0.5 and ±1.0 D difference in the predicted error were calculated with these four formulas. MAE was 0.5-0.7 D in cases of both methods with SRK/T, Holladay, and Hoffer-Q formula, but higher with Haigis formula. With no optimization, 60-65 % of the patients were under 0.5 D error in the immersion group (except for Haigis formula). Using the optical method, this value was slightly higher (62-67 %), however, in this case, Haigis formula also did not perform so well (45 %). Refraction results significantly improved with Holladay, Hoffer-Q, and Haigis formulas in both groups. The rate of patients under 0.5 D error increased to 65 % by the immersion technique, and up to 80 % by the optical one. According to our results, optical biometry offers only slightly better outcomes compared to those of immersion shell with no optimized formulas. However, in case of new generation formulas with both methods, the optimization of IOL-constants give significantly better results.

Slip rates on San Francisco Bay area faults from anelastic deformation of the continental lithosphere

USGS Publications Warehouse

Geist, E.L.; Andrews, D.J.

2000-01-01

Long-term slip rates on major faults in the San Francisco Bay area are predicted by modeling the anelastic deformation of the continental lithosphere in response to regional relative plate motion. The model developed by Bird and Kong [1994] is used to simulate lithospheric deformation according to a Coulomb frictional rheology of the upper crust and a dislocation creep rheology at depth. The focus of this study is the long-term motion of faults in a region extending from the creeping section of the San Andreas fault to the south up to the latitude of Cape Mendocino to the north. Boundary conditions are specified by the relative motion between the Pacific plate and the Sierra Nevada - Great Valley microplate [Argus and Gordon, 2000]. Rheologic-frictional parameters are specified as independent variables, and prediction errors are calculated with respect to geologic estimates of slip rates and maximum compressive stress directions. The model that best explains the region-wide observations is one in which the coefficient of friction on all of the major faults is less than 0.15, with the coefficient of friction for the San Andreas fault being approximately 0.09, consistent with previous inferences of San Andreas fault friction. Prediction error increases with lower fault friction on the San Andreas, indicating a lower bound of ??SAF > 0.08. Discrepancies with respect to previous slip rate estimates include a higher than expected slip rate along the peninsula segment of the San Andreas fault and a slightly lower than expected slip rate along the San Gregorio fault.

Frontal responses during learning predict vulnerability to the psychotogenic effects of ketamine: linking cognition, brain activity, and psychosis.

PubMed

Corlett, Philip R; Honey, Garry D; Aitken, Michael R F; Dickinson, Anthony; Shanks, David R; Absalom, Anthony R; Lee, Michael; Pomarol-Clotet, Edith; Murray, Graham K; McKenna, Peter J; Robbins, Trevor W; Bullmore, Edward T; Fletcher, Paul C

2006-06-01

Establishing a neurobiological account of delusion formation that links cognitive processes, brain activity, and symptoms is important to furthering our understanding of psychosis. To explore a theoretical model of delusion formation that implicates prediction error-dependent associative learning processes in a pharmacological functional magnetic resonance imaging study using the psychotomimetic drug ketamine. Within-subject, randomized, placebo-controlled study. Hospital-based clinical research facility, Addenbrooke's Hospital, Cambridge, England. The work was completed within the Wellcome Trust and Medical Research Council Behavioral and Clinical Neuroscience Institute, Cambridge. Fifteen healthy, right-handed volunteers (8 of whom were male) with a mean +/- SD age of 29 +/- 7 years and a mean +/- SD predicted full-scale IQ of 113 +/- 4 were recruited from within the local community by advertisement. Subjects were given low-dose ketamine (100 ng/mL of plasma) or placebo while performing a causal associative learning task during functional magnetic resonance imaging. In a separate session outside the scanner, the dose was increased (to 200 ng/mL of plasma) and subjects underwent a structured clinical interview. Brain activation, blood plasma levels of ketamine, and scores from psychiatric ratings scales (Brief Psychiatric Ratings Scale, Present State Examination, and Clinician-Administered Dissociative States Scale). Low-dose ketamine perturbs error-dependent learning activity in the right frontal cortex (P = .03). High-dose ketamine produces perceptual aberrations (P = .01) and delusion-like beliefs (P = .007). Critically, subjects showing the highest degree of frontal activation with placebo show the greatest occurrence of drug-induced perceptual aberrations (P = .03) and ideas or delusions of reference (P = .04). These findings relate aberrant prediction error-dependent associative learning to referential ideas and delusions via a perturbation of frontal cortical function. They are consistent with a model of delusion formation positing disruptions in error-dependent learning.

A global perspective of the limits of prediction skill based on the ECMWF ensemble

NASA Astrophysics Data System (ADS)

Zagar, Nedjeljka

2016-04-01

In this talk presents a new model of the global forecast error growth applied to the forecast errors simulated by the ensemble prediction system (ENS) of the ECMWF. The proxy for forecast errors is the total spread of the ECMWF operational ensemble forecasts obtained by the decomposition of the wind and geopotential fields in the normal-mode functions. In this way, the ensemble spread can be quantified separately for the balanced and inertio-gravity (IG) modes for every forecast range. Ensemble reliability is defined for the balanced and IG modes comparing the ensemble spread with the control analysis in each scale. The results show that initial uncertainties in the ECMWF ENS are largest in the tropical large-scale modes and their spatial distribution is similar to the distribution of the short-range forecast errors. Initially the ensemble spread grows most in the smallest scales and in the synoptic range of the IG modes but the overall growth is dominated by the increase of spread in balanced modes in synoptic and planetary scales in the midlatitudes. During the forecasts, the distribution of spread in the balanced and IG modes grows towards the climatological spread distribution characteristic of the analyses. The ENS system is found to be somewhat under-dispersive which is associated with the lack of tropical variability, primarily the Kelvin waves. The new model of the forecast error growth has three fitting parameters to parameterize the initial fast growth and a more slow exponential error growth later on. The asymptotic values of forecast errors are independent of the exponential growth rate. It is found that the asymptotic values of the errors due to unbalanced dynamics are around 10 days while the balanced and total errors saturate in 3 to 4 weeks. Reference: Žagar, N., R. Buizza, and J. Tribbia, 2015: A three-dimensional multivariate modal analysis of atmospheric predictability with application to the ECMWF ensemble. J. Atmos. Sci., 72, 4423-4444.

A Predictive Validity Study of an Assessment Center for Research and Development Supervisors.

DTIC Science & Technology

1981-09-01

Implementation, to All AFSC Organi- zation Commanders, 24 July 1974. Szilagyi , Andrew D., Jr., and Marc J. Wallace , Jr. Organ- izational Behavior and...vali- dation of’ projective personality tests. These tests attempt to assess a person through his or her fantasies and inter- pretations ( Szilagyi ...leniency, strictness, and halo errors ( Szilagyi and W’allace, 1980). Finally, a variant to supervisory ratings are jeer ratings. Peer ratings are

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.

A comparison of zero-order, first-order, and monod biotransformation models

USGS Publications Warehouse

Bekins, B.A.; Warren, E.; Godsy, E.M.

1998-01-01

Under some conditions, a first-order kinetic model is a poor representation of biodegradation in contaminated aquifers. Although it is well known that the assumption of first-order kinetics is valid only when substrate concentration, S, is much less than the half-saturation constant, K(s), this assumption is often made without verification of this condition. We present a formal error analysis showing that the relative error in the first-order approximation is S/K(S) and in the zero-order approximation the error is K(s)/S. We then examine the problems that arise when the first-order approximation is used outside the range for which it is valid. A series of numerical simulations comparing results of first- and zero-order rate approximations to Monod kinetics for a real data set illustrates that if concentrations observed in the field are higher than K(s), it may better to model degradation using a zero-order rate expression. Compared with Monod kinetics, extrapolation of a first-order rate to lower concentrations under-predicts the biotransformation potential, while extrapolation to higher concentrations may grossly over-predict the transformation rate. A summary of solubilities and Monod parameters for aerobic benzene, toluene, and xylene (BTX) degradation shows that the a priori assumption of first-order degradation kinetics at sites contaminated with these compounds is not valid. In particular, out of six published values of KS for toluene, only one is greater than 2 mg/L, indicating that when toluene is present in concentrations greater than about a part per million, the assumption of first-order kinetics may be invalid. Finally, we apply an existing analytical solution for steady-state one-dimensional advective transport with Monod degradation kinetics to a field data set.A formal error analysis is presented showing that the relative error in the first-order approximation is S/KS and in the zero-order approximation the error is KS/S where S is the substrate concentration and KS is the half-saturation constant. The problems that arise when the first-order approximation is used outside the range for which it is valid are examined. A series of numerical simulations comparing results of first- and zero-order rate approximations to Monod kinetics for a real data set illustrates that if concentrations observed in the field are higher than KS, it may be better to model degradation using a zero-order rate expression.

Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

PubMed

Papa, Ester; van der Wal, Leon; Arnot, Jon A; Gramatica, Paola

2014-02-01

Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated. © 2013.

Probability of criminal acts of violence: a test of jury predictive accuracy.

PubMed

Reidy, Thomas J; Sorensen, Jon R; Cunningham, Mark D

2013-01-01

The ability of capital juries to accurately predict future prison violence at the sentencing phase of aggravated murder trials was examined through retrospective review of the disciplinary records of 115 male inmates sentenced to either life (n = 65) or death (n = 50) in Oregon from 1985 through 2008, with a mean post-conviction time at risk of 15.3 years. Violent prison behavior was completely unrelated to predictions made by capital jurors, with bidirectional accuracy simply reflecting the base rate of assaultive misconduct in the group. Rejection of the special issue predicting future violence enjoyed 90% accuracy. Conversely, predictions that future violence was probable had 90% error rates. More than 90% of the assaultive rule violations committed by these offenders resulted in no harm or only minor injuries. Copyright © 2013 John Wiley & Sons, Ltd.

Modulation/demodulation techniques for satellite communications. Part 2: Advanced techniques. The linear channel

NASA Technical Reports Server (NTRS)

Omura, J. K.; Simon, M. K.

1982-01-01

A theory is presented for deducing and predicting the performance of transmitter/receivers for bandwidth efficient modulations suitable for use on the linear satellite channel. The underlying principle used is the development of receiver structures based on the maximum-likelihood decision rule. The application of the performance prediction tools, e.g., channel cutoff rate and bit error probability transfer function bounds to these modulation/demodulation techniques.

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

PubMed Central

Puntel, Laila A.; Sawyer, John E.; Barker, Daniel W.; Dietzel, Ranae; Poffenbarger, Hanna; Castellano, Michael J.; Moore, Kenneth J.; Thorburn, Peter; Archontoulis, Sotirios V.

2016-01-01

Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability. PMID:27891133

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation.

PubMed

Puntel, Laila A; Sawyer, John E; Barker, Daniel W; Dietzel, Ranae; Poffenbarger, Hanna; Castellano, Michael J; Moore, Kenneth J; Thorburn, Peter; Archontoulis, Sotirios V

2016-01-01

Improved prediction of optimal N fertilizer rates for corn ( Zea mays L. ) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean ( Glycine max L. ) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha -1 ) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR's were within the historical N rate error range (40-50 kg N ha -1 ). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

Analytical performance evaluation of SAR ATR with inaccurate or estimated models

NASA Astrophysics Data System (ADS)

DeVore, Michael D.

2004-09-01

Hypothesis testing algorithms for automatic target recognition (ATR) are often formulated in terms of some assumed distribution family. The parameter values corresponding to a particular target class together with the distribution family constitute a model for the target's signature. In practice such models exhibit inaccuracy because of incorrect assumptions about the distribution family and/or because of errors in the assumed parameter values, which are often determined experimentally. Model inaccuracy can have a significant impact on performance predictions for target recognition systems. Such inaccuracy often causes model-based predictions that ignore the difference between assumed and actual distributions to be overly optimistic. This paper reports on research to quantify the effect of inaccurate models on performance prediction and to estimate the effect using only trained parameters. We demonstrate that for large observation vectors the class-conditional probabilities of error can be expressed as a simple function of the difference between two relative entropies. These relative entropies quantify the discrepancies between the actual and assumed distributions and can be used to express the difference between actual and predicted error rates. Focusing on the problem of ATR from synthetic aperture radar (SAR) imagery, we present estimators of the probabilities of error in both ideal and plug-in tests expressed in terms of the trained model parameters. These estimators are defined in terms of unbiased estimates for the first two moments of the sample statistic. We present an analytical treatment of these results and include demonstrations from simulated radar data.

Can the biomass-ratio hypothesis predict mixed-species litter decomposition along a climatic gradient?

PubMed Central

Tardif, Antoine; Shipley, Bill; Bloor, Juliette M. G.; Soussana, Jean-François

2014-01-01

Background and Aims The biomass-ratio hypothesis states that ecosystem properties are driven by the characteristics of dominant species in the community. In this study, the hypothesis was operationalized as community-weighted means (CWMs) of monoculture values and tested for predicting the decomposition of multispecies litter mixtures along an abiotic gradient in the field. Methods Decomposition rates (mg g−1 d−1) of litter from four herb species were measured using litter-bed experiments with the same soil at three sites in central France along a correlated climatic gradient of temperature and precipitation. All possible combinations from one to four species mixtures were tested over 28 weeks of incubation. Observed mixture decomposition rates were compared with those predicted by the biomass-ratio hypothesis. Variability of the prediction errors was compared with the species richness of the mixtures, across sites, and within sites over time. Key Results Both positive and negative prediction errors occurred. Despite this, the biomass-ratio hypothesis was true as an average claim for all sites (r = 0·91) and for each site separately, except for the climatically intermediate site, which showed mainly synergistic deviations. Variability decreased with increasing species richness and in less favourable climatic conditions for decomposition. Conclusions Community-weighted mean values provided good predictions of mixed-species litter decomposition, converging to the predicted values with increasing species richness and in climates less favourable to decomposition. Under a context of climate change, abiotic variability would be important to take into account when predicting ecosystem processes. PMID:24482152

Post audit of a numerical prediction of wellfield drawdown in a semiconfined aquifer system

USGS Publications Warehouse

Stewart, M.; Langevin, C.

1999-01-01

A numerical ground water flow model was created in 1978 and revised in 1981 to predict the drawdown effects of a proposed municipal wellfield permitted to withdraw 30 million gallons per day (mgd; 1.1 x 105 m3/day) of water from the semiconfined Floridan Aquifer system. The predictions are based on the assumption that water levels in the semiconfined Floridan Aquifer reach a long-term, steady-state condition within a few days of initiation of pumping. Using this assumption, a 75 day simulation without water table recharge, pumping at the maximum permitted rates, was considered to represent a worst-case condition and the greatest drawdowns that could be experienced during wellfield operation. This method of predicting wellfield effects was accepted by the permitting agency. For this post audit, observed drawdowns were derived by taking the difference between pre-pumping and post-pumping potentiometric surface levels. Comparison of predicted and observed drawdowns suggests that actual drawdown over a 12 year period exceeds predicted drawdown by a factor of two or more. Analysis of the source of error in the 1981 predictions suggests that the values used for transmissivity, storativity, specific yield, and leakance are reasonable at the wellfield scale. Simulation using actual 1980-1992 pumping rates improves the agreement between predicted and observed drawdowns. The principal source of error is the assumption that water levels in a semiconfined aquifer achieve a steady-state condition after a few days or weeks of pumping. Simulations using a version of the 1981 model modified to include recharge and evapotranspiration suggest that it can take hundreds of days or several years for water levels in the linked Surficial and Floridan Aquifers to reach an apparent steady-state condition, and that slow declines in levels continue for years after the initiation of pumping. While the 1981 'impact' model can be used for reasonably predicting short-term, wellfield-scale effects of pumping, using a 75 day long simulation without recharge to predict the long-term behavior of the wellfield was an inappropriate application, resulting in significant underprediction of wellfield effects.A numerical ground water flow model was created in 1978 and revised in 1981 to predict the drawdown effects of a proposed municipal wellfield permitted to withdraw 30 million gallons per day (mgd; 1.1??105 m3/day) of water from the semiconfined Floridan Aquifer system. The predictions are based on the assumption that water levels in the semiconfined Floridan Aquifer reach a long-term, steady-state condition within a few days of initiation of pumping. Using this assumption, a 75 day simulation without water table recharge, pumping at the maximum permitted rates, was considered to represent a worst-case condition and the greatest drawdowns that could be experienced during wellfield operation. This method of predicting wellfield effects was accepted by the permitting agency. For this post audit, observed drawdowns were derived by taking the difference between pre-pumping and post-pumping potentiometric surface levels. Comparison of predicted and observed drawdowns suggests that actual drawdown over a 12 year period exceeds predicted drawdown by a factor of two or more. Analysis of the source of error in the 1981 predictions suggests that the values used for transmissivity, storativity, specific yield, and leakance are reasonable at the wellfield scale. Simulation using actual 1980-1992 pumping rates improves the agreement between predicted and observed drawdowns. The principal source of error is the assumption that water levels in a semiconfined aquifer achieve a steady-state condition after a few days or weeks of pumping. Simulations using a version of the 1981 model modified to include recharge and evapotranspiration suggest that it can take hundreds of days or several years for water levels in the linked Surficial and Floridan Aquifers to reach an apparent stead

Voidage correction algorithm for unresolved Euler-Lagrange simulations

NASA Astrophysics Data System (ADS)

Askarishahi, Maryam; Salehi, Mohammad-Sadegh; Radl, Stefan

2018-04-01

The effect of grid coarsening on the predicted total drag force and heat exchange rate in dense gas-particle flows is investigated using Euler-Lagrange (EL) approach. We demonstrate that grid coarsening may reduce the predicted total drag force and exchange rate. Surprisingly, exchange coefficients predicted by the EL approach deviate more significantly from the exact value compared to results of Euler-Euler (EE)-based calculations. The voidage gradient is identified as the root cause of this peculiar behavior. Consequently, we propose a correction algorithm based on a sigmoidal function to predict the voidage experienced by individual particles. Our correction algorithm can significantly improve the prediction of exchange coefficients in EL models, which is tested for simulations involving Euler grid cell sizes between 2d_p and 12d_p . It is most relevant in simulations of dense polydisperse particle suspensions featuring steep voidage profiles. For these suspensions, classical approaches may result in an error of the total exchange rate of up to 30%.

The effects of age and mood on saccadic function in older individuals.

PubMed

Shafiq-Antonacci, R; Maruff, P; Whyte, S; Tyler, P; Dudgeon, P; Currie, J

1999-11-01

To investigate the effect of age and mood on saccadic function, we recorded prosaccades, predictive saccades, and antisaccades from 238 cognitively normal, physically healthy volunteers aged 44 to 85 years old. Mood levels were measured using the State-Trait Anxiety Inventory and Center for Epidemiological Studies Depression Scale inventories. Small, but significant, positive relationships with age were observed for the mean latency and associated variability of latency for all types of saccades, as well as the antisaccade error rate. Saccade velocity or accuracy was unaffected by age. Increasing levels of depression had a minor negative influence on the antisaccade latency, whereas increasing levels of anxiety raised the antisaccade error rate marginally.

Simulating a transmon implementation of the surface code, Part I

NASA Astrophysics Data System (ADS)

Tarasinski, Brian; O'Brien, Thomas; Rol, Adriaan; Bultink, Niels; Dicarlo, Leo

Current experimental efforts aim to realize Surface-17, a distance-3 surface-code logical qubit, using transmon qubits in a circuit QED architecture. Following experimental proposals for this device, and currently achieved fidelities on physical qubits, we define a detailed error model that takes experimentally relevant error sources into account, such as amplitude and phase damping, imperfect gate pulses, and coherent errors due to low-frequency flux noise. Using the GPU-accelerated software package 'quantumsim', we simulate the density matrix evolution of the logical qubit under this error model. Combining the simulation results with a minimum-weight matching decoder, we obtain predictions for the error rate of the resulting logical qubit when used as a quantum memory, and estimate the contribution of different error sources to the logical error budget. Research funded by the Foundation for Fundamental Research on Matter (FOM), the Netherlands Organization for Scientific Research (NWO/OCW), IARPA, an ERC Synergy Grant, the China Scholarship Council, and Intel Corporation.

Counteracting structural errors in ensemble forecast of influenza outbreaks.

PubMed

Pei, Sen; Shaman, Jeffrey

2017-10-13

For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. As a consequence, quantification of the nonlinear error structure in current forecast models is needed so that this growth can be corrected and forecast skill improved. Here, we inspect the error growth of a compartmental influenza model and find that a robust error structure arises naturally from the nonlinear model dynamics. By counteracting these structural errors, diagnosed using error breeding, we develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models.Inaccuracy of influenza forecasts based on dynamical models is partly due to nonlinear error growth. Here the authors address the error structure of a compartmental influenza model, and develop a new improved forecast approach combining dynamical error correction and statistical filtering techniques.

Evaluation of MEGAN predicted biogenic isoprene emissions at urban locations in Southeast Texas

NASA Astrophysics Data System (ADS)

Kota, Sri Harsha; Schade, Gunnar; Estes, Mark; Boyer, Doug; Ying, Qi

2015-06-01

Summertime isoprene emissions in the Houston area predicted by the Model of Emissions of Gases and Aerosol from Nature (MEGAN) version 2.1 during the 2006 TexAQS study were evaluated using a source-oriented Community Multiscale Air Quality (CMAQ) Model. Predicted daytime isoprene concentrations at nine surface sites operated by the Texas Commission of Environmental Quality (TCEQ) were significantly higher than local observations when biogenic emissions dominate the total isoprene concentrations, with mean normalized bias (MNB) ranges from 2.0 to 7.7 and mean normalized error (MNE) ranges from 2.2 to 7.7. Predicted upper air isoprene and its first generation oxidation products of methacrolein (MACR) and methyl vinyl ketone (MVK) were also significantly higher (MNB = 8.6, MNE = 9.1) than observations made onboard of NOAA's WP-3 airplane, which flew over the urban area. Over-prediction of isoprene and its oxidation products both at the surface and the upper air strongly suggests that biogenic isoprene emissions in the Houston area are significantly overestimated. Reducing the emission rates by approximately 3/4 was necessary to reduce the error between predictions and observations. Comparison of gridded leaf area index (LAI), plant functional type (PFT) and gridded isoprene emission factor (EF) used in MEGAN modeling with estimates of the same factors from a field survey north of downtown Houston showed that the isoprene over-prediction is likely caused by the combined effects of a large overestimation of the gridded EF in urban Houston and an underestimation of urban LAI. Nevertheless, predicted ozone concentrations in this region were not significantly affected by the isoprene over-predictions, while predicted isoprene SOA and total SOA concentrations can be higher by as much as 50% and 13% using the higher isoprene emission rates, respectively.

System dynamic modelling of industrial growth and landscape ecology in China.

PubMed

Xu, Jian; Kang, Jian; Shao, Long; Zhao, Tianyu

2015-09-15

With the rapid development of large industrial corridors in China, the landscape ecology of the country is currently being affected. Therefore, in this study, a system dynamic model with multi-dimensional nonlinear dynamic prediction function that considers industrial growth and landscape ecology is developed and verified to allow for more sustainable development. Firstly, relationships between industrial development and landscape ecology in China are examined, and five subsystems are then established: industry, population, urban economy, environment and landscape ecology. The main influencing factors are then examined for each subsystem to establish flow charts connecting those factors. Consequently, by connecting the subsystems, an overall industry growth and landscape ecology model is established. Using actual data and landscape index calculated based on GIS of the Ha-Da-Qi industrial corridor, a typical industrial corridor in China, over the period 2005-2009, the model is validated in terms of historical behaviour, logical structure and future prediction, where for 84.8% of the factors, the error rate of the model is less than 5%, the mean error rate of all factors is 2.96% and the error of the simulation test for the landscape ecology subsystem is less than 2%. Moreover, a model application has been made to consider the changes in landscape indices under four industrial development modes, and the optimal industrial growth plan has been examined for landscape ecological protection through the simulation prediction results over 2015-2020. Copyright © 2015 Elsevier Ltd. All rights reserved.

Attentional Control and Subjective Executive Function in Treatment-Naive Adults with Attention Deficit Hyperactivity Disorder

PubMed Central

Grane, Venke Arntsberg; Endestad, Tor; Pinto, Arnfrid Farbu; Solbakk, Anne-Kristin

2014-01-01

We investigated performance-derived measures of executive control, and their relationship with self- and informant reported executive functions in everyday life, in treatment-naive adults with newly diagnosed Attention Deficit Hyperactivity Disorder (ADHD; n = 36) and in healthy controls (n = 35). Sustained attentional control and response inhibition were examined with the Test of Variables of Attention (T.O.V.A.). Delayed responses, increased reaction time variability, and higher omission error rate to Go signals in ADHD patients relative to controls indicated fluctuating levels of attention in the patients. Furthermore, an increment in NoGo commission errors when Go stimuli increased relative to NoGo stimuli suggests reduced inhibition of task-irrelevant stimuli in conditions demanding frequent responding. The ADHD group reported significantly more cognitive and behavioral executive problems than the control group on the Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A). There were overall not strong associations between task performance and ratings of everyday executive function. However, for the ADHD group, T.O.V.A. omission errors predicted self-reported difficulties on the Organization of Materials scale, and commission errors predicted informant reported difficulties on the same scale. Although ADHD patients endorsed more symptoms of depression and anxiety on the Achenbach System of Empirically Based Assessment (ASEBA) than controls, ASEBA scores were not significantly associated with T.O.V.A. performance scores. Altogether, the results indicate multifaceted alteration of attentional control in adult ADHD, and accompanying subjective difficulties with several aspects of executive function in everyday living. The relationships between the two sets of data were modest, indicating that the measures represent non-redundant features of adult ADHD. PMID:25545156

Attentional control and subjective executive function in treatment-naive adults with Attention Deficit Hyperactivity Disorder.

PubMed

Grane, Venke Arntsberg; Endestad, Tor; Pinto, Arnfrid Farbu; Solbakk, Anne-Kristin

2014-01-01

We investigated performance-derived measures of executive control, and their relationship with self- and informant reported executive functions in everyday life, in treatment-naive adults with newly diagnosed Attention Deficit Hyperactivity Disorder (ADHD; n = 36) and in healthy controls (n = 35). Sustained attentional control and response inhibition were examined with the Test of Variables of Attention (T.O.V.A.). Delayed responses, increased reaction time variability, and higher omission error rate to Go signals in ADHD patients relative to controls indicated fluctuating levels of attention in the patients. Furthermore, an increment in NoGo commission errors when Go stimuli increased relative to NoGo stimuli suggests reduced inhibition of task-irrelevant stimuli in conditions demanding frequent responding. The ADHD group reported significantly more cognitive and behavioral executive problems than the control group on the Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A). There were overall not strong associations between task performance and ratings of everyday executive function. However, for the ADHD group, T.O.V.A. omission errors predicted self-reported difficulties on the Organization of Materials scale, and commission errors predicted informant reported difficulties on the same scale. Although ADHD patients endorsed more symptoms of depression and anxiety on the Achenbach System of Empirically Based Assessment (ASEBA) than controls, ASEBA scores were not significantly associated with T.O.V.A. performance scores. Altogether, the results indicate multifaceted alteration of attentional control in adult ADHD, and accompanying subjective difficulties with several aspects of executive function in everyday living. The relationships between the two sets of data were modest, indicating that the measures represent non-redundant features of adult ADHD.

A meta-analytic study of event rate effects on Go/No-Go performance in attention-deficit/hyperactivity disorder.

PubMed

Metin, Baris; Roeyers, Herbert; Wiersema, Jan R; van der Meere, Jaap; Sonuga-Barke, Edmund

2012-12-15

According to the state regulation deficit model, event rate (ER) is an important determinant of performance of children with attention-deficit/hyperactivity disorder (ADHD). Fast ER is predicted to create overactivation and produce errors of commission, whereas slow ER is thought to create underactivation marked by slow and variable reaction times (RT) and errors of omission. To test these predictions, we conducted a systematic search of the literature to identify all reports of comparisons of ADHD and control individuals' performance on Go/No-Go tasks published between 2000 and 2011. In one analysis, we included all trials with at least two event rates and calculated the difference between ER conditions. In a second analysis, we used metaregression to test for the moderating role of ER on ADHD versus control differences seen across Go/No-Go studies. There was a significant and disproportionate slowing of reaction time in ADHD relative to controls on trials with slow event rates in both meta-analyses. For commission errors, the effect sizes were larger on trials with fast event rates. No ER effects were seen for RT variability. There were also general effects of ADHD on performance for all variables that persisted after effects of ER were taken into account. The results provide support for the state regulation deficit model of ADHD by showing the differential effects of fast and slow ER. The lack of an effect of ER on RT variability suggests that this behavioral characteristic may not be a marker of cognitive energetic effects in ADHD. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

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.

A Class of Prediction-Correction Methods for Time-Varying Convex Optimization

NASA Astrophysics Data System (ADS)

Simonetto, Andrea; Mokhtari, Aryan; Koppel, Alec; Leus, Geert; Ribeiro, Alejandro

2016-09-01

This paper considers unconstrained convex optimization problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and correction steps, while sampling the problem data at a constant rate of $1/h$, where $h$ is the length of the sampling interval. The prediction step is derived by analyzing the iso-residual dynamics of the optimality conditions. The correction step adjusts for the distance between the current prediction and the optimizer at each time step, and consists either of one or multiple gradient steps or Newton steps, which respectively correspond to the gradient trajectory tracking (GTT) or Newton trajectory tracking (NTT) algorithms. Under suitable conditions, we establish that the asymptotic error incurred by both proposed methods behaves as $O(h^2)$, and in some cases as $O(h^4)$, which outperforms the state-of-the-art error bound of $O(h)$ for correction-only methods in the gradient-correction step. Moreover, when the characteristics of the objective function variation are not available, we propose approximate gradient and Newton tracking algorithms (AGT and ANT, respectively) that still attain these asymptotical error bounds. Numerical simulations demonstrate the practical utility of the proposed methods and that they improve upon existing techniques by several orders of magnitude.

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

[Effect of stock abundance and environmental factors on the recruitment success of small yellow croaker in the East China Sea].

PubMed

Liu, Zun-lei; Yuan, Xing-wei; Yang, Lin-lin; Yan, Li-ping; Zhang, Hui; Cheng, Jia-hua

2015-02-01

Multiple hypotheses are available to explain recruitment rate. Model selection methods can be used to identify the best model that supports a particular hypothesis. However, using a single model for estimating recruitment success is often inadequate for overexploited population because of high model uncertainty. In this study, stock-recruitment data of small yellow croaker in the East China Sea collected from fishery dependent and independent surveys between 1992 and 2012 were used to examine density-dependent effects on recruitment success. Model selection methods based on frequentist (AIC, maximum adjusted R2 and P-values) and Bayesian (Bayesian model averaging, BMA) methods were applied to identify the relationship between recruitment and environment conditions. Interannual variability of the East China Sea environment was indicated by sea surface temperature ( SST) , meridional wind stress (MWS), zonal wind stress (ZWS), sea surface pressure (SPP) and runoff of Changjiang River ( RCR). Mean absolute error, mean squared predictive error and continuous ranked probability score were calculated to evaluate the predictive performance of recruitment success. The results showed that models structures were not consistent based on three kinds of model selection methods, predictive variables of models were spawning abundance and MWS by AIC, spawning abundance by P-values, spawning abundance, MWS and RCR by maximum adjusted R2. The recruitment success decreased linearly with stock abundance (P < 0.01), suggesting overcompensation effect in the recruitment success might be due to cannibalism or food competition. Meridional wind intensity showed marginally significant and positive effects on the recruitment success (P = 0.06), while runoff of Changjiang River showed a marginally negative effect (P = 0.07). Based on mean absolute error and continuous ranked probability score, predictive error associated with models obtained from BMA was the smallest amongst different approaches, while that from models selected based on the P-value of the independent variables was the highest. However, mean squared predictive error from models selected based on the maximum adjusted R2 was highest. We found that BMA method could improve the prediction of recruitment success, derive more accurate prediction interval and quantitatively evaluate model uncertainty.

Wind Prediction Accuracy for Air Traffic Management Decision Support Tools

NASA Technical Reports Server (NTRS)

Cole, Rod; Green, Steve; Jardin, Matt; Schwartz, Barry; Benjamin, Stan

2000-01-01

The performance of Air Traffic Management and flight deck decision support tools depends in large part on the accuracy of the supporting 4D trajectory predictions. This is particularly relevant to conflict prediction and active advisories for the resolution of conflicts and the conformance with of traffic-flow management flow-rate constraints (e.g., arrival metering / required time of arrival). Flight test results have indicated that wind prediction errors may represent the largest source of trajectory prediction error. The tests also discovered relatively large errors (e.g., greater than 20 knots), existing in pockets of space and time critical to ATM DST performance (one or more sectors, greater than 20 minutes), are inadequately represented by the classic RMS aggregate prediction-accuracy studies of the past. To facilitate the identification and reduction of DST-critical wind-prediction errors, NASA has lead a collaborative research and development activity with MIT Lincoln Laboratories and the Forecast Systems Lab of the National Oceanographic and Atmospheric Administration (NOAA). This activity, begun in 1996, has focussed on the development of key metrics for ATM DST performance, assessment of wind-prediction skill for state of the art systems, and development/validation of system enhancements to improve skill. A 13 month study was conducted for the Denver Center airspace in 1997. Two complementary wind-prediction systems were analyzed and compared to the forecast performance of the then standard 60 km Rapid Update Cycle - version 1 (RUC-1). One system, developed by NOAA, was the prototype 40-km RUC-2 that became operational at NCEP in 1999. RUC-2 introduced a faster cycle (1 hr vs. 3 hr) and improved mesoscale physics. The second system, Augmented Winds (AW), is a prototype en route wind application developed by MITLL based on the Integrated Terminal Wind System (ITWS). AW is run at a local facility (Center) level, and updates RUC predictions based on an optimal interpolation of the latest ACARS reports since the RUC run. This paper presents an overview of the study's results including the identification and use of new large mor wind-prediction accuracy metrics that are key to ATM DST performance.

A Comparative Study of Spectral Auroral Intensity Predictions From Multiple Electron Transport Models

NASA Astrophysics Data System (ADS)

Grubbs, Guy; Michell, Robert; Samara, Marilia; Hampton, Donald; Hecht, James; Solomon, Stanley; Jahn, Jorg-Micha

2018-01-01

It is important to routinely examine and update models used to predict auroral emissions resulting from precipitating electrons in Earth's magnetotail. These models are commonly used to invert spectral auroral ground-based images to infer characteristics about incident electron populations when in situ measurements are unavailable. In this work, we examine and compare auroral emission intensities predicted by three commonly used electron transport models using varying electron population characteristics. We then compare model predictions to same-volume in situ electron measurements and ground-based imaging to qualitatively examine modeling prediction error. Initial comparisons showed differences in predictions by the GLobal airglOW (GLOW) model and the other transport models examined. Chemical reaction rates and radiative rates in GLOW were updated using recent publications, and predictions showed better agreement with the other models and the same-volume data, stressing that these rates are important to consider when modeling auroral processes. Predictions by each model exhibit similar behavior for varying atmospheric constants, energies, and energy fluxes. Same-volume electron data and images are highly correlated with predictions by each model, showing that these models can be used to accurately derive electron characteristics and ionospheric parameters based solely on multispectral optical imaging data.

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.

Analysis tool and methodology design for electronic vibration stress understanding and prediction

NASA Astrophysics Data System (ADS)

Hsieh, Sheng-Jen; Crane, Robert L.; Sathish, Shamachary

2005-03-01

The objectives of this research were to (1) understand the impact of vibration on electronic components under ultrasound excitation; (2) model the thermal profile presented under vibration stress; and (3) predict stress level given a thermal profile of an electronic component. Research tasks included: (1) retrofit of current ultrasonic/infrared nondestructive testing system with sensory devices for temperature readings; (2) design of software tool to process images acquired from the ultrasonic/infrared system; (3) developing hypotheses and conducting experiments; and (4) modeling and evaluation of electronic vibration stress levels using a neural network model. Results suggest that (1) an ultrasonic/infrared system can be used to mimic short burst high vibration loads for electronics components; (2) temperature readings for electronic components under vibration stress are consistent and repeatable; (3) as stress load and excitation time increase, temperature differences also increase; (4) components that are subjected to a relatively high pre-stress load, followed by a normal operating load, have a higher heating rate and lower cooling rate. These findings are based on grayscale changes in images captured during experimentation. Discriminating variables and a neural network model were designed to predict stress levels given temperature and/or grayscale readings. Preliminary results suggest a 15.3% error when using grayscale change rate and 12.8% error when using average heating rate within the neural network model. Data were obtained from a high stress point (the corner) of the chip.

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.

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

Validating the relationship between 3-dimensional body acceleration and oxygen consumption in trained Steller sea lions.

PubMed

Volpov, Beth L; Rosen, David A S; Trites, Andrew W; Arnould, John P Y

2015-08-01

We tested the ability of overall dynamic body acceleration (ODBA) to predict the rate of oxygen consumption ([Formula: see text]) in freely diving Steller sea lions (Eumetopias jubatus) while resting at the surface and diving. The trained sea lions executed three dive types-single dives, bouts of multiple long dives with 4-6 dives per bout, or bouts of multiple short dives with 10-12 dives per bout-to depths of 40 m, resulting in a range of activity and oxygen consumption levels. Average metabolic rate (AMR) over the dive cycle or dive bout calculated was calculated from [Formula: see text]. We found that ODBA could statistically predict AMR when data from all dive types were combined, but that dive type was a significant model factor. However, there were no significant linear relationships between AMR and ODBA when data for each dive type were analyzed separately. The potential relationships between AMR and ODBA were not improved by including dive duration, food consumed, proportion of dive cycle spent submerged, or number of dives per bout. It is not clear whether the lack of predictive power within dive type was due to low statistical power, or whether it reflected a true absence of a relationship between ODBA and AMR. The average percent error for predicting AMR from ODBA was 7-11 %, and standard error of the estimated AMR was 5-32 %. Overall, the extensive range of dive behaviors and physiological conditions we tested indicated that ODBA was not suitable for estimating AMR in the field due to considerable error and the inconclusive effects of dive type.

Toward Biopredictive Dissolution for Enteric Coated Dosage Forms.

PubMed

Al-Gousous, J; Amidon, G L; Langguth, P

2016-06-06

The aim of this work was to develop a phosphate buffer based dissolution method for enteric-coated formulations with improved biopredictivity for fasted conditions. Two commercially available enteric-coated aspirin products were used as model formulations (Aspirin Protect 300 mg, and Walgreens Aspirin 325 mg). The disintegration performance of these products in a physiological 8 mM pH 6.5 bicarbonate buffer (representing the conditions in the proximal small intestine) was used as a standard to optimize the employed phosphate buffer molarity. To account for the fact that a pH and buffer molarity gradient exists along the small intestine, the introduction of such a gradient was proposed for products with prolonged lag times (when it leads to a release lower than 75% in the first hour post acid stage) in the proposed buffer. This would allow the method also to predict the performance of later-disintegrating products. Dissolution performance using the accordingly developed method was compared to that observed when using two well-established dissolution methods: the United States Pharmacopeia (USP) method and blank fasted state simulated intestinal fluid (FaSSIF). The resulting dissolution profiles were convoluted using GastroPlus software to obtain predicted pharmacokinetic profiles. A pharmacokinetic study on healthy human volunteers was performed to evaluate the predictions made by the different dissolution setups. The novel method provided the best prediction, by a relatively wide margin, for the difference between the lag times of the two tested formulations, indicating its being able to predict the post gastric emptying onset of drug release with reasonable accuracy. Both the new and the blank FaSSIF methods showed potential for establishing in vitro-in vivo correlation (IVIVC) concerning the prediction of Cmax and AUC0-24 (prediction errors not more than 20%). However, these predictions are strongly affected by the highly variable first pass metabolism necessitating the evaluation of an absorption rate metric that is more independent of the first-pass effect. The Cmax/AUC0-24 ratio was selected for this purpose. Regarding this metric's predictions, the new method provided very good prediction of the two products' performances relative to each other (only 1.05% prediction error in this regard), while its predictions for the individual products' values in absolute terms were borderline, narrowly missing the regulatory 20% prediction error limits (21.51% for Aspirin Protect and 22.58% for Walgreens Aspirin). The blank FaSSIF-based method provided good Cmax/AUC0-24 ratio prediction, in absolute terms, for Aspirin Protect (9.05% prediction error), but its prediction for Walgreens Aspirin (33.97% prediction error) was overwhelmingly poor. Thus it gave practically the same average but much higher maximum prediction errors compared to the new method, and it was strongly overdiscriminating as for predicting their performances relative to one another. The USP method, despite not being overdiscriminating, provided poor predictions of the individual products' Cmax/AUC0-24 ratios. This indicates that, overall, the new method is of improved biopredictivity compared to established methods.

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

A Case Study of the Impact of AIRS Temperature Retrievals on Numerical Weather Prediction

NASA Technical Reports Server (NTRS)

Reale, O.; Atlas, R.; Jusem, J. C.

2004-01-01

Large errors in numerical weather prediction are often associated with explosive cyclogenesis. Most studes focus on the under-forecasting error, i.e. cases of rapidly developing cyclones which are poorly predicted in numerical models. However, the over-forecasting error (i.e., to predict an explosively developing cyclone which does not occur in reality) is a very common error that severely impacts the forecasting skill of all models and may also present economic costs if associated with operational forecasting. Unnecessary precautions taken by marine activities can result in severe economic loss. Moreover, frequent occurrence of over-forecasting can undermine the reliance on operational weather forecasting. Therefore, it is important to understand and reduce the prdctions of extreme weather associated with explosive cyclones which do not actually develop. In this study we choose a very prominent case of over-forecasting error in the northwestern Pacific. A 960 hPa cyclone develops in less than 24 hour in the 5-day forecast, with a deepening rate of about 30 hPa in one day. The cyclone is not versed in the analyses and is thus a case of severe over-forecasting. By assimilating AIRS data, the error is largely eliminated. By following the propagation of the anomaly that generates the spurious cyclone, it is found that a small mid-tropospheric geopotential height negative anomaly over the northern part of the Indian subcontinent in the initial conditions, propagates westward, is amplified by orography, and generates a very intense jet streak in the subtropical jet stream, with consequent explosive cyclogenesis over the Pacific. The AIRS assimilation eliminates this anomaly that may have been caused by erroneous upper-air data, and represents the jet stream more correctly. The energy associated with the jet is distributed over a much broader area and as a consequence a multiple, but much more moderate cyclogenesis is observed.

Prediction of surface roughness in turning of Ti-6Al-4V using cutting parameters, forces and tool vibration

NASA Astrophysics Data System (ADS)

Sahu, Neelesh Kumar; Andhare, Atul B.; Andhale, Sandip; Raju Abraham, Roja

2018-04-01

Present work deals with prediction of surface roughness using cutting parameters along with in-process measured cutting force and tool vibration (acceleration) during turning of Ti-6Al-4V with cubic boron nitride (CBN) inserts. Full factorial design is used for design of experiments using cutting speed, feed rate and depth of cut as design variables. Prediction model for surface roughness is developed using response surface methodology with cutting speed, feed rate, depth of cut, resultant cutting force and acceleration as control variables. Analysis of variance (ANOVA) is performed to find out significant terms in the model. Insignificant terms are removed after performing statistical test using backward elimination approach. Effect of each control variables on surface roughness is also studied. Correlation coefficient (R2 pred) of 99.4% shows that model correctly explains the experiment results and it behaves well even when adjustment is made in factors or new factors are added or eliminated. Validation of model is done with five fresh experiments and measured forces and acceleration values. Average absolute error between RSM model and experimental measured surface roughness is found to be 10.2%. Additionally, an artificial neural network model is also developed for prediction of surface roughness. The prediction results of modified regression model are compared with ANN. It is found that RSM model and ANN (average absolute error 7.5%) are predicting roughness with more than 90% accuracy. From the results obtained it is found that including cutting force and vibration for prediction of surface roughness gives better prediction than considering only cutting parameters. Also, ANN gives better prediction over RSM models.

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

Dopamine Modulates Adaptive Prediction Error Coding in the Human Midbrain and Striatum.

PubMed

Diederen, Kelly M J; Ziauddeen, Hisham; Vestergaard, Martin D; Spencer, Tom; Schultz, Wolfram; Fletcher, Paul C

2017-02-15

Learning to optimally predict rewards requires agents to account for fluctuations in reward value. Recent work suggests that individuals can efficiently learn about variable rewards through adaptation of the learning rate, and coding of prediction errors relative to reward variability. Such adaptive coding has been linked to midbrain dopamine neurons in nonhuman primates, and evidence in support for a similar role of the dopaminergic system in humans is emerging from fMRI data. Here, we sought to investigate the effect of dopaminergic perturbations on adaptive prediction error coding in humans, using a between-subject, placebo-controlled pharmacological fMRI study with a dopaminergic agonist (bromocriptine) and antagonist (sulpiride). Participants performed a previously validated task in which they predicted the magnitude of upcoming rewards drawn from distributions with varying SDs. After each prediction, participants received a reward, yielding trial-by-trial prediction errors. Under placebo, we replicated previous observations of adaptive coding in the midbrain and ventral striatum. Treatment with sulpiride attenuated adaptive coding in both midbrain and ventral striatum, and was associated with a decrease in performance, whereas bromocriptine did not have a significant impact. Although we observed no differential effect of SD on performance between the groups, computational modeling suggested decreased behavioral adaptation in the sulpiride group. These results suggest that normal dopaminergic function is critical for adaptive prediction error coding, a key property of the brain thought to facilitate efficient learning in variable environments. Crucially, these results also offer potential insights for understanding the impact of disrupted dopamine function in mental illness. SIGNIFICANCE STATEMENT To choose optimally, we have to learn what to expect. Humans dampen learning when there is a great deal of variability in reward outcome, and two brain regions that are modulated by the brain chemical dopamine are sensitive to reward variability. Here, we aimed to directly relate dopamine to learning about variable rewards, and the neural encoding of associated teaching signals. We perturbed dopamine in healthy individuals using dopaminergic medication and asked them to predict variable rewards while we made brain scans. Dopamine perturbations impaired learning and the neural encoding of reward variability, thus establishing a direct link between dopamine and adaptation to reward variability. These results aid our understanding of clinical conditions associated with dopaminergic dysfunction, such as psychosis. Copyright © 2017 Diederen et al.

Support vector machine learning model for the prediction of sentinel node status in patients with cutaneous melanoma.

PubMed

Mocellin, Simone; Ambrosi, Alessandro; Montesco, Maria Cristina; Foletto, Mirto; Zavagno, Giorgio; Nitti, Donato; Lise, Mario; Rossi, Carlo Riccardo

2006-08-01

Currently, approximately 80% of melanoma patients undergoing sentinel node biopsy (SNB) have negative sentinel lymph nodes (SLNs), and no prediction system is reliable enough to be implemented in the clinical setting to reduce the number of SNB procedures. In this study, the predictive power of support vector machine (SVM)-based statistical analysis was tested. The clinical records of 246 patients who underwent SNB at our institution were used for this analysis. The following clinicopathologic variables were considered: the patient's age and sex and the tumor's histological subtype, Breslow thickness, Clark level, ulceration, mitotic index, lymphocyte infiltration, regression, angiolymphatic invasion, microsatellitosis, and growth phase. The results of SVM-based prediction of SLN status were compared with those achieved with logistic regression. The SLN positivity rate was 22% (52 of 234). When the accuracy was > or = 80%, the negative predictive value, positive predictive value, specificity, and sensitivity were 98%, 54%, 94%, and 77% and 82%, 41%, 69%, and 93% by using SVM and logistic regression, respectively. Moreover, SVM and logistic regression were associated with a diagnostic error and an SNB percentage reduction of (1) 1% and 60% and (2) 15% and 73%, respectively. The results from this pilot study suggest that SVM-based prediction of SLN status might be evaluated as a prognostic method to avoid the SNB procedure in 60% of patients currently eligible, with a very low error rate. If validated in larger series, this strategy would lead to obvious advantages in terms of both patient quality of life and costs for the health care system.

What do the experts know? Calibration, precision, and the wisdom of crowds among forensic handwriting experts.

PubMed

Martire, Kristy A; Growns, Bethany; Navarro, Danielle J

2018-04-17

Forensic handwriting examiners currently testify to the origin of questioned handwriting for legal purposes. However, forensic scientists are increasingly being encouraged to assign probabilities to their observations in the form of a likelihood ratio. This study is the first to examine whether handwriting experts are able to estimate the frequency of US handwriting features more accurately than novices. The results indicate that the absolute error for experts was lower than novices, but the size of the effect is modest, and the overall error rate even for experts is large enough as to raise questions about whether their estimates can be sufficiently trustworthy for presentation in courts. When errors are separated into effects caused by miscalibration and those caused by imprecision, we find systematic differences between individuals. Finally, we consider several ways of aggregating predictions from multiple experts, suggesting that quite substantial improvements in expert predictions are possible when a suitable aggregation method is used.

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.

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.

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

Theoretical and experimental studies of the deposition of Na2So4 from seeded combustion gases

NASA Technical Reports Server (NTRS)

Kohl, F. J.; Santoro, G. J.; Stearns, C. A.; Fryburg, G. C.; Rosner, D. E.

1977-01-01

Flames in a Mach 0.3 atmospheric pressure laboratory burner rig were doped with sea salt, NaS04, and NaCl, respectively, in an effort to validate theoretical dew point predictions made by a local thermochemical equilibrium (LTCE) method of predicting condensation temperatures of sodium sulfate in flame environments. Deposits were collected on cylindrical platinum targets placed in the combustion products, and the deposition was studied as a function of collector temperature. Experimental deposition onset temperatures checked within experimental error with LTCE-predicted temperatures. A multicomponent mass transfer equation was developed to predict the rate of deposition of Na2SO4(c) via vapor transport at temperatures below the deposition onset temperature. Agreement between maximum deposition rates predicted by this chemically frozen boundary layer (CFBL) theory and those obtained in the seeded laboratory burner experiments is good.

A maximum likelihood convolutional decoder model vs experimental data comparison

NASA Technical Reports Server (NTRS)

Chen, R. Y.

1979-01-01

This article describes the comparison of a maximum likelihood convolutional decoder (MCD) prediction model and the actual performance of the MCD at the Madrid Deep Space Station. The MCD prediction model is used to develop a subroutine that has been utilized by the Telemetry Analysis Program (TAP) to compute the MCD bit error rate for a given signal-to-noise ratio. The results indicate that that the TAP can predict quite well compared to the experimental measurements. An optimal modulation index also can be found through TAP.

The Impact of Soil Sampling Errors on Variable Rate Fertilization

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

R. L. Hoskinson; R C. Rope; L G. Blackwood

2004-07-01

Variable rate fertilization of an agricultural field is done taking into account spatial variability in the soil’s characteristics. Most often, spatial variability in the soil’s fertility is the primary characteristic used to determine the differences in fertilizers applied from one point to the next. For several years the Idaho National Engineering and Environmental Laboratory (INEEL) has been developing a Decision Support System for Agriculture (DSS4Ag) to determine the economically optimum recipe of various fertilizers to apply at each site in a field, based on existing soil fertility at the site, predicted yield of the crop that would result (and amore » predicted harvest-time market price), and the current costs and compositions of the fertilizers to be applied. Typically, soil is sampled at selected points within a field, the soil samples are analyzed in a lab, and the lab-measured soil fertility of the point samples is used for spatial interpolation, in some statistical manner, to determine the soil fertility at all other points in the field. Then a decision tool determines the fertilizers to apply at each point. Our research was conducted to measure the impact on the variable rate fertilization recipe caused by variability in the measurement of the soil’s fertility at the sampling points. The variability could be laboratory analytical errors or errors from variation in the sample collection method. The results show that for many of the fertility parameters, laboratory measurement error variance exceeds the estimated variability of the fertility measure across grid locations. These errors resulted in DSS4Ag fertilizer recipe recommended application rates that differed by up to 138 pounds of urea per acre, with half the field differing by more than 57 pounds of urea per acre. For potash the difference in application rate was up to 895 pounds per acre and over half the field differed by more than 242 pounds of potash per acre. Urea and potash differences accounted for almost 87% of the cost difference. The sum of these differences could result in a $34 per acre cost difference for the fertilization. Because of these differences, better analysis or better sampling methods may need to be done, or more samples collected, to ensure that the soil measurements are truly representative of the field’s spatial variability.« less

The Relationship Between Technical Errors and Decision Making Skills in the Junior Resident

PubMed Central

Nathwani, J. N.; Fiers, R.M.; Ray, R.D.; Witt, A.K.; Law, K. E.; DiMarco, S.M.; Pugh, C.M.

2017-01-01

Objective The purpose of this study is to co-evaluate resident technical errors and decision-making capabilities during placement of a subclavian central venous catheter (CVC). We hypothesize that there will be significant correlations between scenario based decision making skills, and technical proficiency in central line insertion. We also predict residents will have problems in anticipating common difficulties and generating solutions associated with line placement. Design Participants were asked to insert a subclavian central line on a simulator. After completion, residents were presented with a real life patient photograph depicting CVC placement and asked to anticipate difficulties and generate solutions. Error rates were analyzed using chi-square tests and a 5% expected error rate. Correlations were sought by comparing technical errors and scenario based decision making. Setting This study was carried out at seven tertiary care centers. Participants Study participants (N=46) consisted of largely first year research residents that could be followed longitudinally. Second year research and clinical residents were not excluded. Results Six checklist errors were committed more often than anticipated. Residents performed an average of 1.9 errors, significantly more than the 1 error, at most, per person expected (t(44)=3.82, p<.001). The most common error was performance of the procedure steps in the wrong order (28.5%, P<.001). Some of the residents (24%) had no errors, 30% committed one error, and 46 % committed more than one error. The number of technical errors committed negatively correlated with the total number of commonly identified difficulties and generated solutions (r(33)= −.429, p=.021, r(33)= −.383, p=.044 respectively). Conclusions Almost half of the surgical residents committed multiple errors while performing subclavian CVC placement. The correlation between technical errors and decision making skills suggests a critical need to train residents in both technique and error management. ACGME Competencies Medical Knowledge, Practice Based Learning and Improvement, Systems Based Practice PMID:27671618

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.

The Longitudinal Association between Oppositional and Depressive Symptoms across Childhood

ERIC Educational Resources Information Center

Boylan, Khrista; Georgiades, Katholiki; Szatmari, Peter

2010-01-01

Objective: Symptoms of oppositional defiant disorder (ODD) and depression show high rates of co-occurrence, both cross-sectionally and longitudinally. This study examines the extent to which variation in oppositional symptoms predict, variation in depressive symptoms over time, accounting for co-occurring depressive symptoms and measurement error.…

On Correlations, Distances and Error Rates.

ERIC Educational Resources Information Center

Dorans, Neil J.

The nature of the criterion (dependent) variable may play a useful role in structuring a list of classification/prediction problems. Such criteria are continuous in nature, binary dichotomous, or multichotomous. In this paper, discussion is limited to the continuous normally distributed criterion scenarios. For both cases, it is assumed that the…

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

Do Rare Stimuli Evoke Large P3s by Being Unexpected? A Comparison of Oddball Effects Between Standard-Oddball and Prediction-Oddball Tasks

PubMed Central

Verleger, Rolf; Śmigasiewicz, Kamila

2016-01-01

The P3 component of event-related potentials increases when stimuli are rarely presented. It has been assumed that this oddball effect (rare-frequent difference) reflects the unexpectedness of rare stimuli. The assumption of unexpectedness and its link to P3 amplitude were tested here. A standard- oddball task requiring alternative key-press responses to frequent and rare stimuli was compared with an oddball-prediction task where stimuli had to be first predicted and then confirmed by key-pressing. Oddball effects in the prediction task depended on whether the frequent or the rare stimulus had been predicted. Oddball effects on P3 amplitudes and error rates in the standard oddball task closely resembled effects after frequent predictions. This corroborates the notion that these effects occur because frequent stimuli are expected and rare stimuli are unexpected. However, a closer look at the prediction task put this notion into doubt because the modifications of oddball effects on P3 by expectancies were entirely due to effects on frequent stimuli, whereas the large P3 amplitudes evoked by rare stimuli were insensitive to predictions (unlike response times and error rates). Therefore, rare stimuli cannot be said to evoke large P3 amplitudes because they are unexpected. We discuss these diverging effects of frequency and expectancy, as well as general differences between tasks, with respect to concepts and hypotheses about P3b’s function and conclude that each discussed concept or hypothesis encounters some problems, with a conception in terms of subjective relevance assigned to stimuli offering the most consistent account of these basic effects. PMID:27512527

Systems Issues Pertaining to Holographic Optical Data Storage in Thick Bacteriorhodopsin Films

NASA Technical Reports Server (NTRS)

Downie, John D.; Timucin, Dogan A.; Gary, Charles K.; Oezcan, Meric; Smithey, Daniel T.; Crew, Marshall; Lau, Sonie (Technical Monitor)

1998-01-01

The optical data storage capacity and raw bit-error-rate achievable with thick photochromic bacteriorhodopsin (BR) films are investigated for sequential recording and read- out of angularly- and shift-multiplexed digital holograms inside a thick blue-membrane D85N BR film. We address the determination of an exposure schedule that produces equal diffraction efficiencies among each of the multiplexed holograms. This exposure schedule is determined by numerical simulations of the holographic recording process within the BR material, and maximizes the total grating strength. We also experimentally measure the shift selectivity and compare the results to theoretical predictions. Finally, we evaluate the bit-error-rate of a single hologram, and of multiple holograms stored within the film.

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 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.

SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification

PubMed Central

McClure, Matthew C.; McCarthy, John; Flynn, Paul; McClure, Jennifer C.; Dair, Emma; O'Connell, D. K.; Kearney, John F.

2018-01-01

A major use of genetic data is parentage verification and identification as inaccurate pedigrees negatively affect genetic gain. Since 2012 the international standard for single nucleotide polymorphism (SNP) verification in Bos taurus cattle has been the ISAG SNP panels. While these ISAG panels provide an increased level of parentage accuracy over microsatellite markers (MS), they can validate the wrong parent at ≤1% misconcordance rate levels, indicating that more SNP are needed if a more accurate pedigree is required. With rapidly increasing numbers of cattle being genotyped in Ireland that represent 61 B. taurus breeds from a wide range of farm types: beef/dairy, AI/pedigree/commercial, purebred/crossbred, and large to small herd size the Irish Cattle Breeding Federation (ICBF) analyzed different SNP densities to determine that at a minimum ≥500 SNP are needed to consistently predict only one set of parents at a ≤1% misconcordance rate. For parentage validation and prediction ICBF uses 800 SNP (ICBF800) selected based on SNP clustering quality, ISAG200 inclusion, call rate (CR), and minor allele frequency (MAF) in the Irish cattle population. Large datasets require sample and SNP quality control (QC). Most publications only deal with SNP QC via CR, MAF, parent-progeny conflicts, and Hardy-Weinberg deviation, but not sample QC. We report here parentage, SNP QC, and a genomic sample QC pipelines to deal with the unique challenges of >1 million genotypes from a national herd such as SNP genotype errors from mis-tagging of animals, lab errors, farm errors, and multiple other issues that can arise. We divide the pipeline into two parts: a Genotype QC and an Animal QC pipeline. The Genotype QC identifies samples with low call rate, missing or mixed genotype classes (no BB genotype or ABTG alleles present), and low genotype frequencies. The Animal QC handles situations where the genotype might not belong to the listed individual by identifying: >1 non-matching genotypes per animal, SNP duplicates, sex and breed prediction mismatches, parentage and progeny validation results, and other situations. The Animal QC pipeline make use of ICBF800 SNP set where appropriate to identify errors in a computationally efficient yet still highly accurate method. PMID:29599798

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.

Real time identification of the internal combustion engine combustion parameters based on the vibration velocity signal

NASA Astrophysics Data System (ADS)

Zhao, Xiuliang; Cheng, Yong; Wang, Limei; Ji, Shaobo

2017-03-01

Accurate combustion parameters are the foundations of effective closed-loop control of engine combustion process. Some combustion parameters, including the start of combustion, the location of peak pressure, the maximum pressure rise rate and its location, can be identified from the engine block vibration signals. These signals often include non-combustion related contributions, which limit the prompt acquisition of the combustion parameters computationally. The main component in these non-combustion related contributions is considered to be caused by the reciprocating inertia force excitation (RIFE) of engine crank train. A mathematical model is established to describe the response of the RIFE. The parameters of the model are recognized with a pattern recognition algorithm, and the response of the RIFE is predicted and then the related contributions are removed from the measured vibration velocity signals. The combustion parameters are extracted from the feature points of the renovated vibration velocity signals. There are angle deviations between the feature points in the vibration velocity signals and those in the cylinder pressure signals. For the start of combustion, a system bias is adopted to correct the deviation and the error bound of the predicted parameters is within 1.1°. To predict the location of the maximum pressure rise rate and the location of the peak pressure, algorithms based on the proportion of high frequency components in the vibration velocity signals are introduced. Tests results show that the two parameters are able to be predicted within 0.7° and 0.8° error bound respectively. The increase from the knee point preceding the peak value point to the peak value in the vibration velocity signals is used to predict the value of the maximum pressure rise rate. Finally, a monitoring frame work is inferred to realize the combustion parameters prediction. Satisfactory prediction for combustion parameters in successive cycles is achieved, which validates the proposed methods.

Sensorimotor synchronization with tempo-changing auditory sequences: Modeling temporal adaptation and anticipation.

PubMed

van der Steen, M C Marieke; Jacoby, Nori; Fairhurst, Merle T; Keller, Peter E

2015-11-11

The current study investigated the human ability to synchronize movements with event sequences containing continuous tempo changes. This capacity is evident, for example, in ensemble musicians who maintain precise interpersonal coordination while modulating the performance tempo for expressive purposes. Here we tested an ADaptation and Anticipation Model (ADAM) that was developed to account for such behavior by combining error correction processes (adaptation) with a predictive temporal extrapolation process (anticipation). While previous computational models of synchronization incorporate error correction, they do not account for prediction during tempo-changing behavior. The fit between behavioral data and computer simulations based on four versions of ADAM was assessed. These versions included a model with adaptation only, one in which adaptation and anticipation act in combination (error correction is applied on the basis of predicted tempo changes), and two models in which adaptation and anticipation were linked in a joint module that corrects for predicted discrepancies between the outcomes of adaptive and anticipatory processes. The behavioral experiment required participants to tap their finger in time with three auditory pacing sequences containing tempo changes that differed in the rate of change and the number of turning points. Behavioral results indicated that sensorimotor synchronization accuracy and precision, while generally high, decreased with increases in the rate of tempo change and number of turning points. Simulations and model-based parameter estimates showed that adaptation mechanisms alone could not fully explain the observed precision of sensorimotor synchronization. Including anticipation in the model increased the precision of simulated sensorimotor synchronization and improved the fit of model to behavioral data, especially when adaptation and anticipation mechanisms were linked via a joint module based on the notion of joint internal models. Overall results suggest that adaptation and anticipation mechanisms both play an important role during sensorimotor synchronization with tempo-changing sequences. This article is part of a Special Issue entitled SI: Prediction and Attention. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

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.

Pulse Vector-Excitation Speech Encoder

NASA Technical Reports Server (NTRS)

Davidson, Grant; Gersho, Allen

1989-01-01

Proposed pulse vector-excitation speech encoder (PVXC) encodes analog speech signals into digital representation for transmission or storage at rates below 5 kilobits per second. Produces high quality of reconstructed speech, but with less computation than required by comparable speech-encoding systems. Has some characteristics of multipulse linear predictive coding (MPLPC) and of code-excited linear prediction (CELP). System uses mathematical model of vocal tract in conjunction with set of excitation vectors and perceptually-based error criterion to synthesize natural-sounding speech.

Modeling the growth kinetics of Bacillus cereus as a function of temperature, pH, sodium lactate and sodium chloride concentrations.

PubMed

Olmez, Hülya Kaptan; Aran, Necla

2005-02-01

Mathematical models describing the growth kinetic parameters (lag phase duration and growth rate) of Bacillus cereus as a function of temperature, pH, sodium lactate and sodium chloride concentrations were obtained in this study. In order to get a residual distribution closer to a normal distribution, the natural logarithm of the growth kinetic parameters were used in modeling. For reasons of parsimony, the polynomial models were reduced to contain only the coefficients significant at a level of p

One way Doppler extractor. Volume 1: Vernier technique

NASA Technical Reports Server (NTRS)

Blasco, R. W.; Klein, S.; Nossen, E. J.; Starner, E. R.; Yanosov, J. A.

1974-01-01

A feasibility analysis, trade-offs, and implementation for a One Way Doppler Extraction system are discussed. A Doppler error analysis shows that quantization error is a primary source of Doppler measurement error. Several competing extraction techniques are compared and a Vernier technique is developed which obtains high Doppler resolution with low speed logic. Parameter trade-offs and sensitivities for the Vernier technique are analyzed, leading to a hardware design configuration. A detailed design, operation, and performance evaluation of the resulting breadboard model is presented which verifies the theoretical performance predictions. Performance tests have verified that the breadboard is capable of extracting Doppler, on an S-band signal, to an accuracy of less than 0.02 Hertz for a one second averaging period. This corresponds to a range rate error of no more than 3 millimeters per second.

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.

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

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

Prediction of HR/BP response to the spontaneous breathing trial by fluctuation dissipation theory

NASA Astrophysics Data System (ADS)

Chen, Man

2014-03-01

We applied the non-equilibrium fluctuation dissipation theorem to predict how critically-ill patients respond to treatment, based on both heart rate data and blood pressure data collected by standard hospital monitoring devices. The non-equilibrium fluctuation dissipation theorem relates the response of a system to a perturbation to the fluctuations in the stationary state of the system. It is shown that the response of patients to a standard procedure performed on patients, the spontaneous breathing trial (SBT), can be predicted by the non-equilibrium fluctuation dissipation approach. We classify patients into different groups according to the patients' characteristics. For each patient group, we extend the fluctuation dissipation theorem to predict interactions between blood pressure and beat-to-beat dynamics of heart rate in response to a perturbation (SBT), We also extract the form of the perturbation function directly from the physiological data, which may help to reduce the prediction error. We note this method is not limited to the analysis of the heart rate dynamics, but also can be applied to analyze the response of other physiological signals to other clinical interventions.

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.

PREDICTION OF VO2PEAK USING OMNI RATINGS OF PERCEIVED EXERTION FROM A SUBMAXIMAL CYCLE EXERCISE TEST

PubMed Central

Mays, Ryan J.; Goss, Fredric L.; Nagle-Stilley, Elizabeth F.; Gallagher, Michael; Schafer, Mark A.; Kim, Kevin H.; Robertson, Robert J.

2015-01-01

Summary The primary aim of this study was to develop statistical models to predict peak oxygen consumption (VO2peak) using OMNI Ratings of Perceived Exertion measured during submaximal cycle ergometry. Men (mean ± standard error: 20.90 ± 0.42 yrs) and women (21.59 ± 0.49 yrs) participants (n = 81) completed a load-incremented maximal cycle ergometer exercise test. Simultaneous multiple linear regression was used to develop separate VO2peak statistical models using submaximal ratings of perceived exertion for the overall body, legs, and chest/breathing as predictor variables. VO2peak (L·min−1) predicted for men and women from ratings of perceived exertion for the overall body (3.02 ± 0.06; 2.03 ± 0.04), legs (3.02 ± 0.06; 2.04 ± 0.04) and chest/breathing (3.02 ± 0.05; 2.03 ± 0.03) were similar with measured VO2peak (3.02 ± 0.10; 2.03 ± 0.06, ps > .05). Statistical models based on submaximal OMNI Ratings of Perceived Exertion provide an easily administered and accurate method to predict VO2peak. PMID:25068750

Combining forecast weights: Why and how?

NASA Astrophysics Data System (ADS)

Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim

2012-09-01

This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.

A recent Cleanroom success story: The Redwing project

NASA Technical Reports Server (NTRS)

Hausler, Philip A.

1992-01-01

Redwing is the largest completed Cleanroom software engineering project in IBM, both in terms of lines of code and project staffing. The product provides a decision-support facility that utilizes artificial intelligence (AI) technology for predicting and preventing complex operating problems in an MVS environment. The project used the Cleanroom process for development and realized a defect rate of 2.6 errors/KLOC, measured from first execution. This represents the total amount of errors that were found in testing and installation at three field test sites. Development productivity was 486 LOC/PM, which included all development labor expended in design specification through completion of incremental testing. In short, the Redwing team produced a complex systems software product with an extraordinarily low error rate, while maintaining high productivity. All of this was accomplished by a project team using Cleanroom for the first time. An 'introductory implementation' of Cleanroom was defined and used on Redwing. This paper describes the quality and productivity results, the Redwing project, and how Cleanroom was implemented.

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.

Verification of a SEU model for advanced 1-micron CMOS structures using heavy ions

NASA Technical Reports Server (NTRS)

Cable, J. S.; Carter, J. R.; Witteles, A. A.

1986-01-01

Modeling and test results are reported for 1 micron CMOS circuits. Analytical predictions are correlated with experimental data, and sensitivities to process and design variations are discussed. Unique features involved in predicting the SEU performance of these devices are described. The results show that the critical charge for upset exhibits a strong dependence on pulse width for very fast devices, and upset predictions must factor in the pulse shape. Acceptable SEU error rates can be achieved for a 1 micron bulk CMOS process. A thin retrograde well provides complete SEU immunity for N channel hits at normal incidence angle. Source interconnect resistance can be important parameter in determining upset rates, and Cf-252 testing can be a valuable tool for cost-effective SEU testing.

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.

Error catastrophe and phase transition in the empirical fitness landscape of HIV

NASA Astrophysics Data System (ADS)

Hart, Gregory R.; Ferguson, Andrew L.

2015-03-01

We have translated clinical sequence databases of the p6 HIV protein into an empirical fitness landscape quantifying viral replicative capacity as a function of the amino acid sequence. We show that the viral population resides close to a phase transition in sequence space corresponding to an "error catastrophe" beyond which there is lethal accumulation of mutations. Our model predicts that the phase transition may be induced by drug therapies that elevate the mutation rate, or by forcing mutations at particular amino acids. Applying immune pressure to any combination of killer T-cell targets cannot induce the transition, providing a rationale for why the viral protein can exist close to the error catastrophe without sustaining fatal fitness penalties due to adaptive immunity.

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

PubMed

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. © 2012 Diabetes Technology Society.

Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models

PubMed Central

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179

Determination of the carmine content based on spectrum fluorescence spectral and PSO-SVM

NASA Astrophysics Data System (ADS)

Wang, Shu-tao; Peng, Tao; Cheng, Qi; Wang, Gui-chuan; Kong, De-ming; Wang, Yu-tian

2018-03-01

Carmine is a widely used food pigment in various food and beverage additives. Excessive consumption of synthetic pigment shall do harm to body seriously. The food is generally associated with a variety of colors. Under the simulation context of various food pigments' coexistence, we adopted the technology of fluorescence spectroscopy, together with the PSO-SVM algorithm, so that to establish a method for the determination of carmine content in mixed solution. After analyzing the prediction results of PSO-SVM, we collected a bunch of data: the carmine average recovery rate was 100.84%, the root mean square error of prediction (RMSEP) for 1.03e-04, 0.999 for the correlation coefficient between the model output and the real value of the forecast. Compared with the prediction results of reverse transmission, the correlation coefficient of PSO-SVM was 2.7% higher, the average recovery rate for 0.6%, and the root mean square error was nearly one order of magnitude lower. According to the analysis results, it can effectively avoid the interference caused by pigment with the combination of the fluorescence spectrum technique and PSO-SVM, accurately determining the content of carmine in mixed solution with an effect better than that of BP.

Estimation of Rainfall Sampling Uncertainty: A Comparison of Two Diverse Approaches

NASA Technical Reports Server (NTRS)

Steiner, Matthias; Zhang, Yu; Baeck, Mary Lynn; Wood, Eric F.; Smith, James A.; Bell, Thomas L.; Lau, William K. M. (Technical Monitor)

2002-01-01

The spatial and temporal intermittence of rainfall causes the averages of satellite observations of rain rate to differ from the "true" average rain rate over any given area and time period, even if the satellite observations are perfectly accurate. The difference of satellite averages based on occasional observation by satellite systems and the continuous-time average of rain rate is referred to as sampling error. In this study, rms sampling error estimates are obtained for average rain rates over boxes 100 km, 200 km, and 500 km on a side, for averaging periods of 1 day, 5 days, and 30 days. The study uses a multi-year, merged radar data product provided by Weather Services International Corp. at a resolution of 2 km in space and 15 min in time, over an area of the central U.S. extending from 35N to 45N in latitude and 100W to 80W in longitude. The intervals between satellite observations are assumed to be equal, and similar In size to what present and future satellite systems are able to provide (from 1 h to 12 h). The sampling error estimates are obtained using a resampling method called "resampling by shifts," and are compared to sampling error estimates proposed by Bell based on earlier work by Laughlin. The resampling estimates are found to scale with areal size and time period as the theory predicts. The dependence on average rain rate and time interval between observations is also similar to what the simple theory suggests.

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.

Theories of Lethal Mutagenesis: From Error Catastrophe to Lethal Defection.

PubMed

Tejero, Héctor; Montero, Francisco; Nuño, Juan Carlos

2016-01-01

RNA viruses get extinct in a process called lethal mutagenesis when subjected to an increase in their mutation rate, for instance, by the action of mutagenic drugs. Several approaches have been proposed to understand this phenomenon. The extinction of RNA viruses by increased mutational pressure was inspired by the concept of the error threshold. The now classic quasispecies model predicts the existence of a limit to the mutation rate beyond which the genetic information of the wild type could not be efficiently transmitted to the next generation. This limit was called the error threshold, and for mutation rates larger than this threshold, the quasispecies was said to enter into error catastrophe. This transition has been assumed to foster the extinction of the whole population. Alternative explanations of lethal mutagenesis have been proposed recently. In the first place, a distinction is made between the error threshold and the extinction threshold, the mutation rate beyond which a population gets extinct. Extinction is explained from the effect the mutation rate has, throughout the mutational load, on the reproductive ability of the whole population. Secondly, lethal defection takes also into account the effect of interactions within mutant spectra, which have been shown to be determinant for the understanding the extinction of RNA virus due to an augmented mutational pressure. Nonetheless, some relevant issues concerning lethal mutagenesis are not completely understood yet, as so survival of the flattest, i.e. the development of resistance to lethal mutagenesis by evolving towards mutationally more robust regions of sequence space, or sublethal mutagenesis, i.e., the increase of the mutation rate below the extinction threshold which may boost the adaptability of RNA virus, increasing their ability to develop resistance to drugs (including mutagens). A better design of antiviral therapies will still require an improvement of our knowledge about lethal mutagenesis.

Effects of Parameter Uncertainty on Long-Term Simulations of Lake Alkalinity

NASA Astrophysics Data System (ADS)

Lee, Sijin; Georgakakos, Konstantine P.; Schnoor, Jerald L.

1990-03-01

A first-order second-moment uncertainty analysis has been applied to two lakes in the Adirondack Park, New York, to assess the long-term response of lakes to acid deposition. Uncertainty due to parameter error and initial condition error was considered. Because the enhanced trickle-down (ETD) model is calibrated with only 3 years of field data and is used to simulate a 50-year period, the uncertainty in the lake alkalinity prediction is relatively large. When a best estimate of parameter uncertainty is used, the annual average alkalinity is predicted to be -11 ±28 μeq/L for Lake Woods and 142 ± 139 μeq/L for Lake Panther after 50 years. Hydrologic parameters and chemical weathering rate constants contributed most to the uncertainty of the simulations. Results indicate that the uncertainty in long-range predictions of lake alkalinity increased significantly over a 5- to 10-year period and then reached a steady state.

A simplified model of a mechanical cooling tower with both a fill pack and a coil

NASA Astrophysics Data System (ADS)

Van Riet, Freek; Steenackers, Gunther; Verhaert, Ivan

2017-11-01

Cooling accounts for a large amount of the global primary energy consumption in buildings and industrial processes. A substantial part of this cooling demand is produced by mechanical cooling towers. Simulations benefit the sizing and integration of cooling towers in overall cooling networks. However, for these simulations fast-to-calculate and easy-to-parametrize models are required. In this paper, a new model is developed for a mechanical draught cooling tower with both a cooling coil and a fill pack. The model needs manufacturers' performance data at only three operational states (at varying air and water flow rates) to be parametrized. The model predicts the cooled, outgoing water temperature. These predictions were compared with experimental data for a wide range of operational states. The model was able to predict the temperature with a maximum absolute error of 0.59°C. The relative error of cooling capacity was mostly between ±5%.

The roles of word-form frequency and phonological neighbourhood density in the acquisition of Lithuanian noun morphology.

PubMed

Savičiūtė, Eglė; Ambridge, Ben; Pine, Julian M

2018-05-01

Four- and five-year-old children took part in an elicited familiar and novel Lithuanian noun production task to test predictions of input-based accounts of the acquisition of inflectional morphology. Two major findings emerged. First, as predicted by input-based accounts, correct production rates were correlated with the input frequency of the target form, and with the phonological neighbourhood density of the noun. Second, the error patterns were not compatible with the systematic substitution of target forms by either (a) the most frequent form of that noun or (b) a single morphosyntactic default form, as might be predicted by naive versions of a constructivist and generativist account, respectively. Rather, most errors reflected near-miss substitutions of singular for plural, masculine for feminine, or nominative/accusative for a less frequent case. Together, these findings provide support for an input-based approach to morphological acquisition, but are not adequately explained by any single account in its current form.

Optical PAyload for Lasercomm Science (OPALS) link validation

NASA Technical Reports Server (NTRS)

Biswas, Abhijit; Oaida, Bogdan V.; Andrews, Kenneth S.; Kovalik, Joseph M.; Abrahamson, Matthew J.; Wright, Malcolm W.

2015-01-01

Recently several day and nighttime links under diverse atmospheric conditions were completed using the Optical Payload for Lasercomm Science (OPALS) flight system on-board the International Space Station (ISS). In this paper we compare measured optical power and its variance at either end of the link with predictions that include atmospheric propagation models. For the 976 nm laser beacon mean power transmitted from the ground to the ISS the predicted mean irradiance of 10's of microwatts per square meter close to zenith and its decrease with range and increased air mass shows good agreement with predictions. The irradiance fluctuations sampled at 100 Hz also follow the expected increase in scintillation with air mass representative of atmospheric coherence lengths at zenith at 500 nm in the 3-8 cm range. The downlink predicted power of 100's of nanowatts was also reconciled within the uncertainty of the atmospheric losses. Expected link performance with uncoded bit-error rates less than 1E-4 required for the Reed-Solomon code to correct errors for video, text and file transmission was verified. The results of predicted and measured powers and fluctuations suggest the need for further study and refinement.

Confidence intervals in Flow Forecasting by using artificial neural networks

NASA Astrophysics Data System (ADS)

Panagoulia, Dionysia; Tsekouras, George

2014-05-01

One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input variable of different ANN structures [3]. The performance of each ANN structure is evaluated by the voting analysis based on eleven criteria, which are the root mean square error (RMSE), the correlation index (R), the mean absolute percentage error (MAPE), the mean percentage error (MPE), the mean percentage error (ME), the percentage volume in errors (VE), the percentage error in peak (MF), the normalized mean bias error (NMBE), the normalized root mean bias error (NRMSE), the Nash-Sutcliffe model efficiency coefficient (E) and the modified Nash-Sutcliffe model efficiency coefficient (E1). The next day flow for the test set is calculated using the best ANN structure's model. Consequently, the confidence intervals of various confidence levels for training, evaluation and test sets are compared in order to explore the generalisation dynamics of confidence intervals from training and evaluation sets. [1] H.S. Hippert, C.E. Pedreira, R.C. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Trans. on Power Systems, vol. 16, no. 1, 2001, pp. 44-55. [2] G. J. Tsekouras, N.E. Mastorakis, F.D. Kanellos, V.T. Kontargyri, C.D. Tsirekis, I.S. Karanasiou, Ch.N. Elias, A.D. Salis, P.A. Kontaxis, A.A. Gialketsi: "Short term load forecasting in Greek interconnected power system using ANN: Confidence Interval using a novel re-sampling technique with corrective Factor", WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, (CSECS '10), Vouliagmeni, Athens, Greece, December 29-31, 2010. [3] D. Panagoulia, I. Trichakis, G. J. Tsekouras: "Flow Forecasting via Artificial Neural Networks - A Study for Input Variables conditioned on atmospheric circulation", European Geosciences Union, General Assembly 2012 (NH1.1 / AS1.16 - Extreme meteorological and hydrological events induced by severe weather and climate change), Vienna, Austria, 22-27 April 2012.

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.

City traffic flow breakdown prediction based on fuzzy rough set

NASA Astrophysics Data System (ADS)

Yang, Xu; Da-wei, Hu; Bing, Su; Duo-jia, Zhang

2017-05-01

In city traffic management, traffic breakdown is a very important issue, which is defined as a speed drop of a certain amount within a dense traffic situation. In order to predict city traffic flow breakdown accurately, in this paper, we propose a novel city traffic flow breakdown prediction algorithm based on fuzzy rough set. Firstly, we illustrate the city traffic flow breakdown problem, in which three definitions are given, that is, 1) Pre-breakdown flow rate, 2) Rate, density, and speed of the traffic flow breakdown, and 3) Duration of the traffic flow breakdown. Moreover, we define a hazard function to represent the probability of the breakdown ending at a given time point. Secondly, as there are many redundant and irrelevant attributes in city flow breakdown prediction, we propose an attribute reduction algorithm using the fuzzy rough set. Thirdly, we discuss how to predict the city traffic flow breakdown based on attribute reduction and SVM classifier. Finally, experiments are conducted by collecting data from I-405 Freeway, which is located at Irvine, California. Experimental results demonstrate that the proposed algorithm is able to achieve lower average error rate of city traffic flow breakdown prediction.

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.

Modeling amphibian energetics, habitat suitability, and movements of western toads, Anaxyrus (=Bufo) boreas, across present and future landscapes

USGS Publications Warehouse

Bartelt, Paul E.; Klaver, Robert W.; Porter, Warren P.

2010-01-01

Effective conservation of amphibian populations requires the prediction of how amphibians use and move through a landscape. Amphibians are closely coupled to their physical environment. Thus an approach that uses the physiological attributes of amphibians, together with knowledge of their natural history, should be helpful. We used Niche Mapper™ to model the known movements and habitat use patterns of a population of Western toads (Anaxyrus (=Bufo) boreas) occupying forested habitats in southeastern Idaho. Niche Mapper uses first principles of environmental biophysics to combine features of topography, climate, land cover, and animal features to model microclimates and animal physiology and behavior across landscapes. Niche Mapper reproduced core body temperatures (Tc) and evaporation rates of live toads with average errors of 1.6 ± 0.4 °C and 0.8 ± 0.2 g/h, respectively. For four different habitat types, it reproduced similar mid-summer daily temperature patterns as those measured in the field and calculated evaporation rates (g/h) with an average error rate of 7.2 ± 5.5%. Sensitivity analyses indicate these errors do not significantly affect estimates of food consumption or activity. Using Niche Mapper we predicted the daily habitats used by free-ranging toads; our accuracy for female toads was greater than for male toads (74.2 ± 6.8% and 53.6 ± 15.8%, respectively), reflecting the stronger patterns of habitat selection among females. Using these changing to construct a cost surface, we also reconstructed movement paths that were consistent with field observations. The effect of climate warming on toads depends on the interaction of temperature and atmospheric moisture. If climate change occurs as predicted, results from Niche Mapper suggests that climate warming will increase the physiological cost of landscapes thereby limiting the activity for toads in different habitats.

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.

Accuracy of the energy-corrected sudden (ECS) scaling procedure for rotational excitation of CO by collisions with Ar

NASA Technical Reports Server (NTRS)

Green, S.; Cochrane, D. L.; Truhlar, D. G.

1986-01-01

The utility of the energy-corrected sudden (ECS) scaling method is evaluated on the basis of how accurately it predicts the entire matrix of state-to-state rate constants, when the fundamental rate constants are independently known. It is shown for the case of Ar-CO collisions at 500 K that when a critical impact parameter is about 1.75-2.0 A, the ECS method yields excellent excited state rates on the average and has an rms error of less than 20 percent.

Assessing the Impact of Observations on the Prediction of Effective Atmospheric Angular Momentum from NAVGEM

NASA Astrophysics Data System (ADS)

Baker, N. L.; Langland, R.

2016-12-01

Variations in Earth rotation are measured by comparing a time based on Earth's variable rotation rate about its axis to a time standard based on an internationally coordinated ensemble of atomic clocks that provide a uniform time scale. The variability of Earth's rotation is partly due to the changes in angular momentum that occur in the atmosphere and ocean as weather patterns and ocean features develop, propagate, and dissipate. The NAVGEM Effective Atmospheric Angular Momentum Functions (EAAMF) and their predictions are computed following Barnes et al. (1983), and provided to the U.S. Naval Observatory daily. These along with similar data from the NOAA GFS model are used to calculate and predict the Earth orientation parameters (Stamatakos et al., 2016). The Navy's high-resolution global weather prediction system consists of the Navy Global Environmental Model (NAVGEM; Hogan et al., 2014) and a hybrid four-dimensional variational data assimilation system (4DVar) (Kuhl et al., 2013). An important component of NAVGEM is the Forecast Sensitivity Observation Impact (FSOI). FSOI is a mathematical method to quantify the contribution of individual observations or sets of observations to the reduction in the 24-hr forecast error (Langland and Baker, 2004). The FSOI allows for dynamic monitoring of the relative quality and value of the observations assimilated by NAVGEM, and the relative ability of the data assimilation system to effectively use the observation information to generate an improved forecast. For this study, along with the FSOI based on the global moist energy error norm, we computed the FSOI using an error norm based on the Effective Angular Momentum Functions. This modification allowed us to assess which observations were most beneficial in reducing the 24-hr forecast error for the atmospheric angular momentum.

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.

The measurement and prediction of proton upset

NASA Astrophysics Data System (ADS)

Shimano, Y.; Goka, T.; Kuboyama, S.; Kawachi, K.; Kanai, T.

1989-12-01

The authors evaluate tolerance to proton upset for three kinds of memories and one microprocessor unit for space use by irradiating them with high-energy protons up to nearly 70 MeV. They predict the error rates of these memories using a modified semi-empirical equation of Bendel and Petersen (1983). A two-parameter method was used instead of Bendel's one-parameter method. There is a large difference between these two methods with regard to the fitted parameters. The calculation of upset rates in orbits were carried out using these parameters and NASA AP8MAC, AP8MIC. For the 93419 RAM the result of this calculation was compared with the in-orbit data taken on the MOS-1 spacecraft. A good agreement was found between the two sets of upset-rate data.

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.

Four-dimensional data coupled to alternating weighted residue constraint quadrilinear decomposition model applied to environmental analysis: Determination of polycyclic aromatic hydrocarbons

NASA Astrophysics Data System (ADS)

Liu, Tingting; Zhang, Ling; Wang, Shutao; Cui, Yaoyao; Wang, Yutian; Liu, Lingfei; Yang, Zhe

2018-03-01

Qualitative and quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) was carried out by three-dimensional fluorescence spectroscopy combining with Alternating Weighted Residue Constraint Quadrilinear Decomposition (AWRCQLD). The experimental subjects were acenaphthene (ANA) and naphthalene (NAP). Firstly, in order to solve the redundant information of the three-dimensional fluorescence spectral data, the wavelet transform was used to compress data in preprocessing. Then, the four-dimensional data was constructed by using the excitation-emission fluorescence spectra of different concentration PAHs. The sample data was obtained from three solvents that are methanol, ethanol and Ultra-pure water. The four-dimensional spectral data was analyzed by AWRCQLD, then the recovery rate of PAHs was obtained from the three solvents and compared respectively. On one hand, the results showed that PAHs can be measured more accurately by the high-order data, and the recovery rate was higher. On the other hand, the results presented that AWRCQLD can better reflect the superiority of four-dimensional algorithm than the second-order calibration and other third-order calibration algorithms. The recovery rate of ANA was 96.5% 103.3% and the root mean square error of prediction was 0.04 μgL- 1. The recovery rate of NAP was 96.7% 115.7% and the root mean square error of prediction was 0.06 μgL- 1.

Experimental measurement of the orbital paths of particles sedimenting within a rotating viscous fluid as influenced by gravity

NASA Technical Reports Server (NTRS)

Wolf, David A.; Schwarz, Ray P.

1992-01-01

Measurements were taken of the path of a simulated typical tissue segment or 'particle' within a rotating fluid as a function of gravitational strength, fluid rotation rate, particle sedimentation rate, and particle initial position. Parameters were examined within the useful range for tissue culture in the NASA rotating wall culture vessels. The particle moves along a nearly circular path through the fluid (as observed from the rotating reference frame of the fluid) at the same speed as its linear terminal sedimentation speed for the external gravitational field. This gravitationally induced motion causes an increasing deviation of the particle from its original position within the fluid for a decreased rotational rate, for a more rapidly sedimenting particle, and for an increased gravitational strength. Under low gravity conditions (less than 0.1 G), the particle's motion through the fluid and its deviation from its original position become negligible. Under unit gravity conditions, large distortions (greater than 0.25 inch) occur even for particles of slow sedimentation rate (less than 1.0 cm/sec). The particle's motion is nearly independent of the particle's initial position. Comparison with mathematically predicted particle paths show that a significant error in the mathematically predicted path occurs for large particle deviations. This results from a geometric approximation and numerically accumulating error in the mathematical technique.

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.

Correcting for sequencing error in maximum likelihood phylogeny inference.

PubMed

Kuhner, Mary K; McGill, James

2014-11-04

Accurate phylogenies are critical to taxonomy as well as studies of speciation processes and other evolutionary patterns. Accurate branch lengths in phylogenies are critical for dating and rate measurements. Such accuracy may be jeopardized by unacknowledged sequencing error. We use simulated data to test a correction for DNA sequencing error in maximum likelihood phylogeny inference. Over a wide range of data polymorphism and true error rate, we found that correcting for sequencing error improves recovery of the branch lengths, even if the assumed error rate is up to twice the true error rate. Low error rates have little effect on recovery of the topology. When error is high, correction improves topological inference; however, when error is extremely high, using an assumed error rate greater than the true error rate leads to poor recovery of both topology and branch lengths. The error correction approach tested here was proposed in 2004 but has not been widely used, perhaps because researchers do not want to commit to an estimate of the error rate. This study shows that correction with an approximate error rate is generally preferable to ignoring the issue. Copyright © 2014 Kuhner and McGill.

LOOP- SIMULATION OF THE AUTOMATIC FREQUENCY CONTROL SUBSYSTEM OF A DIFFERENTIAL MINIMUM SHIFT KEYING RECEIVER

NASA Technical Reports Server (NTRS)

Davarian, F.

1994-01-01

The LOOP computer program was written to simulate the Automatic Frequency Control (AFC) subsystem of a Differential Minimum Shift Keying (DMSK) receiver with a bit rate of 2400 baud. The AFC simulated by LOOP is a first order loop configuration with a first order R-C filter. NASA has been investigating the concept of mobile communications based on low-cost, low-power terminals linked via geostationary satellites. Studies have indicated that low bit rate transmission is suitable for this application, particularly from the frequency and power conservation point of view. A bit rate of 2400 BPS is attractive due to its applicability to the linear predictive coding of speech. Input to LOOP includes the following: 1) the initial frequency error; 2) the double-sided loop noise bandwidth; 3) the filter time constants; 4) the amount of intersymbol interference; and 5) the bit energy to noise spectral density. LOOP output includes: 1) the bit number and the frequency error of that bit; 2) the computed mean of the frequency error; and 3) the standard deviation of the frequency error. LOOP is written in MS SuperSoft FORTRAN 77 for interactive execution and has been implemented on an IBM PC operating under PC DOS with a memory requirement of approximately 40K of 8 bit bytes. This program was developed in 1986.

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.

Prediction of oxygen consumption in cardiac rehabilitation patients performing leg ergometry

NASA Astrophysics Data System (ADS)

Alvarez, John Gershwin

The purpose of this study was two-fold. First, to determine the validity of the ACSM leg ergometry equation in the prediction of steady-state oxygen consumption (VO2) in a heterogeneous population of cardiac patients. Second, to determine whether a more accurate prediction equation could be developed for use in the cardiac population. Thirty-one cardiac rehabilitation patients participated in the study of which 24 were men and 7 were women. Biometric variables (mean +/- sd) of the participants were as follows: age = 61.9 +/- 9.5 years; height = 172.6 +/- 1.6 cm; and body mass = 82.3 +/- 10.6 kg. Subjects exercised on a MonarchTM cycle ergometer at 0, 180, 360, 540 and 720 kgm ˙ min-1. The length of each stage was five minutes. Heart rate, ECG, and VO2 were continuously monitored. Blood pressure and heart rate were collected at the end of each stage. Steady state VO 2 was calculated for each stage using the average of the last two minutes. Correlation coefficients, standard error of estimate, coefficient of determination, total error, and mean bias were used to determine the accuracy of the ACSM equation (1995). The analysis found the ACSM equation to be a valid means of estimating VO2 in cardiac patients. Simple linear regression was used to develop a new equation. Regression analysis found workload to be a significant predictor of VO2. The following equation is the result: VO2 = (1.6 x kgm ˙ min-1) + 444 ml ˙ min-1. The r of the equation was .78 (p < .05) and the standard error of estimate was 211 ml ˙ min-1. Analysis of variance was used to determine significant differences between means for actual and predicted VO2 values for each equation. The analysis found the ACSM and new equation to significantly (p < .05) under predict VO2 during unloaded pedaling. Furthermore, the ACSM equation was found to significantly (p < .05) under predict VO 2 during the first loaded stage of exercise. When the accuracy of the ACSM and new equations were compared based on correlation coefficients, coefficients of determinations, SEEs, total error, and mean bias the new equation was found to have equal or better accuracy at all workloads. The final form of the new equation is: VO2 (ml ˙ min-1) = (kgm ˙ min-1 x 1.6 ml ˙ kgm-1) + (3.5 ml ˙ kg-1 ˙ min-1 x body mass in kg) + 156 ml ˙ min-1.

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

Integrated modelling of H-mode pedestal and confinement in JET-ILW

NASA Astrophysics Data System (ADS)

Saarelma, S.; Challis, C. D.; Garzotti, L.; Frassinetti, L.; Maggi, C. F.; Romanelli, M.; Stokes, C.; Contributors, JET

2018-01-01

A pedestal prediction model Europed is built on the existing EPED1 model by coupling it with core transport simulation using a Bohm-gyroBohm transport model to self-consistently predict JET-ILW power scan for hybrid plasmas that display weaker power degradation than the IPB98(y, 2) scaling of the energy confinement time. The weak power degradation is reproduced in the coupled core-pedestal simulation. The coupled core-pedestal model is further tested for a 3.0 MA plasma with the highest stored energy achieved in JET-ILW so far, giving a prediction of the stored plasma energy within the error margins of the measured experimental value. A pedestal density prediction model based on the neutral penetration is tested on a JET-ILW database giving a prediction with an average error of 17% from the experimental data when a parameter taking into account the fuelling rate is added into the model. However the model fails to reproduce the power dependence of the pedestal density implying missing transport physics in the model. The future JET-ILW deuterium campaign with increased heating power is predicted to reach plasma energy of 11 MJ, which would correspond to 11-13 MW of fusion power in equivalent deuterium-tritium plasma but with isotope effects on pedestal stability and core transport ignored.

Application of empirical Bayes methods to predict the rate of decline in ERG at the individual level among patients with retinitis pigmentosa.

PubMed

Qiu, Weiliang; Sandberg, Michael A; Rosner, Bernard

2018-05-31

Retinitis pigmentosa is one of the most common forms of inherited retinal degeneration. The electroretinogram (ERG) can be used to determine the severity of retinitis pigmentosa-the lower the ERG amplitude, the more severe the disease is. In practice for career, lifestyle, and treatment counseling, it is of interest to predict the ERG amplitude of a patient at a future time. One approach is prediction based on the average rate of decline for individual patients. However, there is considerable variation both in initial amplitude and in rate of decline. In this article, we propose an empirical Bayes (EB) approach to incorporate the variations in initial amplitude and rate of decline for the prediction of ERG amplitude at the individual level. We applied the EB method to a collection of ERGs from 898 patients with 3 or more visits over 5 or more years of follow-up tested in the Berman-Gund Laboratory and observed that the predicted values at the last (kth) visit obtained by using the proposed method based on data for the first k-1 visits are highly correlated with the observed values at the kth visit (Spearman correlation =0.93) and have a higher correlation with the observed values than those obtained based on either the population average decline rate or those obtained based on the individual decline rate. The mean square errors for predicted values obtained by the EB method are also smaller than those predicted by the other methods. Copyright © 2018 John Wiley & Sons, Ltd.

Forensic comparison and matching of fingerprints: using quantitative image measures for estimating error rates through understanding and predicting difficulty.

PubMed

Kellman, Philip J; Mnookin, Jennifer L; Erlikhman, Gennady; Garrigan, Patrick; Ghose, Tandra; Mettler, Everett; Charlton, David; Dror, Itiel E

2014-01-01

Latent fingerprint examination is a complex task that, despite advances in image processing, still fundamentally depends on the visual judgments of highly trained human examiners. Fingerprints collected from crime scenes typically contain less information than fingerprints collected under controlled conditions. Specifically, they are often noisy and distorted and may contain only a portion of the total fingerprint area. Expertise in fingerprint comparison, like other forms of perceptual expertise, such as face recognition or aircraft identification, depends on perceptual learning processes that lead to the discovery of features and relations that matter in comparing prints. Relatively little is known about the perceptual processes involved in making comparisons, and even less is known about what characteristics of fingerprint pairs make particular comparisons easy or difficult. We measured expert examiner performance and judgments of difficulty and confidence on a new fingerprint database. We developed a number of quantitative measures of image characteristics and used multiple regression techniques to discover objective predictors of error as well as perceived difficulty and confidence. A number of useful predictors emerged, and these included variables related to image quality metrics, such as intensity and contrast information, as well as measures of information quantity, such as the total fingerprint area. Also included were configural features that fingerprint experts have noted, such as the presence and clarity of global features and fingerprint ridges. Within the constraints of the overall low error rates of experts, a regression model incorporating the derived predictors demonstrated reasonable success in predicting objective difficulty for print pairs, as shown both in goodness of fit measures to the original data set and in a cross validation test. The results indicate the plausibility of using objective image metrics to predict expert performance and subjective assessment of difficulty in fingerprint comparisons.

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

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.

Erratum: Raman linewidths and rotationally inelastic collision rates in nitrogen [J. Chem. Phys. 98, 257 (1993)

NASA Astrophysics Data System (ADS)

Green, Sheldon

1993-09-01

A computer program error led to erroneous results in the titled paper. Corrected generalized IOS cross sections are significantly changed, especially at lower collision energies. These changes tend to cancel in predicted Raman linewidths; there is a systematic increase of 10-15 %, changing quantitative, but not qualitative, comparisons with experimental data.

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.

The application of improved neural network in hydrocarbon reservoir prediction

NASA Astrophysics Data System (ADS)

Peng, Xiaobo

2013-03-01

This paper use BP neural network techniques to realize hydrocarbon reservoir predication easier and faster in tarim basin in oil wells. A grey - cascade neural network model is proposed and it is faster convergence speed and low error rate. The new method overcomes the shortcomings of traditional BP neural network convergence slow, easy to achieve extreme minimum value. This study had 220 sets of measured logging data to the sample data training mode. By changing the neuron number and types of the transfer function of hidden layers, the best work prediction model is analyzed. The conclusion is the model which can produce good prediction results in general, and can be used for hydrocarbon reservoir prediction.

Predictive modeling of surimi cake shelf life at different storage temperatures

NASA Astrophysics Data System (ADS)

Wang, Yatong; Hou, Yanhua; Wang, Quanfu; Cui, Bingqing; Zhang, Xiangyu; Li, Xuepeng; Li, Yujin; Liu, Yuanping

2017-04-01

The Arrhenius model of the shelf life prediction which based on the TBARS index was established in this study. The results showed that the significant changed of AV, POV, COV and TBARS with temperature increased, and the reaction rate constants k was obtained by the first order reaction kinetics model. Then the secondary model fitting was based on the Arrhenius equation. There was the optimal fitting accuracy of TBARS in the first and the secondary model fitting (R2≥0.95). The verification test indicated that the relative error between the shelf life model prediction value and actual value was within ±10%, suggesting the model could predict the shelf life of surimi cake.

Kinetics of Methane Production from Swine Manure and Buffalo Manure.

PubMed

Sun, Chen; Cao, Weixing; Liu, Ronghou

2015-10-01

The degradation kinetics of swine and buffalo manure for methane production was investigated. Six kinetic models were employed to describe the corresponding experimental data. These models were evaluated by two statistical measurements, which were root mean square prediction error (RMSPE) and Akaike's information criterion (AIC). The results showed that the logistic and Fitzhugh models could predict the experimental data very well for the digestion of swine and buffalo manure, respectively. The predicted methane yield potential for swine and buffalo manure was 487.9 and 340.4 mL CH4/g volatile solid (VS), respectively, which was close to experimental values, when the digestion temperature was 36 ± 1 °C in the biochemical methane potential assays. Besides, the rate constant revealed that swine manure had a much faster methane production rate than buffalo manure.

Optimization of Artificial Neural Network using Evolutionary Programming for Prediction of Cascading Collapse Occurrence due to the Hidden Failure Effect

NASA Astrophysics Data System (ADS)

Idris, N. H.; Salim, N. A.; Othman, M. M.; Yasin, Z. M.

2018-03-01

This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).

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.

Short communication: Prediction of retention pay-off using a machine learning algorithm.

PubMed

Shahinfar, Saleh; Kalantari, Afshin S; Cabrera, Victor; Weigel, Kent

2014-05-01

Replacement decisions have a major effect on dairy farm profitability. Dynamic programming (DP) has been widely studied to find the optimal replacement policies in dairy cattle. However, DP models are computationally intensive and might not be practical for daily decision making. Hence, the ability of applying machine learning on a prerun DP model to provide fast and accurate predictions of nonlinear and intercorrelated variables makes it an ideal methodology. Milk class (1 to 5), lactation number (1 to 9), month in milk (1 to 20), and month of pregnancy (0 to 9) were used to describe all cows in a herd in a DP model. Twenty-seven scenarios based on all combinations of 3 levels (base, 20% above, and 20% below) of milk production, milk price, and replacement cost were solved with the DP model, resulting in a data set of 122,716 records, each with a calculated retention pay-off (RPO). Then, a machine learning model tree algorithm was used to mimic the evaluated RPO with DP. The correlation coefficient factor was used to observe the concordance of RPO evaluated by DP and RPO predicted by the model tree. The obtained correlation coefficient was 0.991, with a corresponding value of 0.11 for relative absolute error. At least 100 instances were required per model constraint, resulting in 204 total equations (models). When these models were used for binary classification of positive and negative RPO, error rates were 1% false negatives and 9% false positives. Applying this trained model from simulated data for prediction of RPO for 102 actual replacement records from the University of Wisconsin-Madison dairy herd resulted in a 0.994 correlation with 0.10 relative absolute error rate. Overall results showed that model tree has a potential to be used in conjunction with DP to assist farmers in their replacement decisions. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Predicting protein folding rate change upon point mutation using residue-level coevolutionary information.

PubMed

Mallik, Saurav; Das, Smita; Kundu, Sudip

2016-01-01

Change in folding kinetics of globular proteins upon point mutation is crucial to a wide spectrum of biological research, such as protein misfolding, toxicity, and aggregations. Here we seek to address whether residue-level coevolutionary information of globular proteins can be informative to folding rate changes upon point mutations. Generating residue-level coevolutionary networks of globular proteins, we analyze three parameters: relative coevolution order (rCEO), network density (ND), and characteristic path length (CPL). A point mutation is considered to be equivalent to a node deletion of this network and respective percentage changes in rCEO, ND, CPL are found linearly correlated (0.84, 0.73, and -0.61, respectively) with experimental folding rate changes. The three parameters predict the folding rate change upon a point mutation with 0.031, 0.045, and 0.059 standard errors, respectively. © 2015 Wiley Periodicals, Inc.

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

Decisions to shoot in a weapon identification task: The influence of cultural stereotypes and perceived threat on false positive errors.

PubMed

Fleming, Kevin K; Bandy, Carole L; Kimble, Matthew O

2010-01-01

The decision to shoot a gun engages executive control processes that can be biased by cultural stereotypes and perceived threat. The neural locus of the decision to shoot is likely to be found in the anterior cingulate cortex (ACC), where cognition and affect converge. Male military cadets at Norwich University (N=37) performed a weapon identification task in which they made rapid decisions to shoot when images of guns appeared briefly on a computer screen. Reaction times, error rates, and electroencephalogram (EEG) activity were recorded. Cadets reacted more quickly and accurately when guns were primed by images of Middle-Eastern males wearing traditional clothing. However, cadets also made more false positive errors when tools were primed by these images. Error-related negativity (ERN) was measured for each response. Deeper ERNs were found in the medial-frontal cortex following false positive responses. Cadets who made fewer errors also produced deeper ERNs, indicating stronger executive control. Pupil size was used to measure autonomic arousal related to perceived threat. Images of Middle-Eastern males in traditional clothing produced larger pupil sizes. An image of Osama bin Laden induced the largest pupil size, as would be predicted for the exemplar of Middle East terrorism. Cadets who showed greater increases in pupil size also made more false positive errors. Regression analyses were performed to evaluate predictions based on current models of perceived threat, stereotype activation, and cognitive control. Measures of pupil size (perceived threat) and ERN (cognitive control) explained significant proportions of the variance in false positive errors to Middle-Eastern males in traditional clothing, while measures of reaction time, signal detection response bias, and stimulus discriminability explained most of the remaining variance.

Decisions to Shoot in a Weapon Identification Task: The Influence of Cultural Stereotypes and Perceived Threat on False Positive Errors

PubMed Central

Fleming, Kevin K.; Bandy, Carole L.; Kimble, Matthew O.

2014-01-01

The decision to shoot engages executive control processes that can be biased by cultural stereotypes and perceived threat. The neural locus of the decision to shoot is likely to be found in the anterior cingulate cortex (ACC) where cognition and affect converge. Male military cadets at Norwich University (N=37) performed a weapon identification task in which they made rapid decisions to shoot when images of guns appeared briefly on a computer screen. Reaction times, error rates, and EEG activity were recorded. Cadets reacted more quickly and accurately when guns were primed by images of middle-eastern males wearing traditional clothing. However, cadets also made more false positive errors when tools were primed by these images. Error-related negativity (ERN) was measured for each response. Deeper ERN’s were found in the medial-frontal cortex following false positive responses. Cadets who made fewer errors also produced deeper ERN’s, indicating stronger executive control. Pupil size was used to measure autonomic arousal related to perceived threat. Images of middle-eastern males in traditional clothing produced larger pupil sizes. An image of Osama bin Laden induced the largest pupil size, as would be predicted for the exemplar of Middle East terrorism. Cadets who showed greater increases in pupil size also made more false positive errors. Regression analyses were performed to evaluate predictions based on current models of perceived threat, stereotype activation, and cognitive control. Measures of pupil size (perceived threat) and ERN (cognitive control) explained significant proportions of the variance in false positive errors to middle-eastern males in traditional clothing, while measures of reaction time, signal detection response bias, and stimulus discriminability explained most of the remaining variance. PMID:19813139

Steady-state phase error for a phase-locked loop subjected to periodic Doppler inputs

NASA Technical Reports Server (NTRS)

Chen, C.-C.; Win, M. Z.

1991-01-01

The performance of a carrier phase locked loop (PLL) driven by a periodic Doppler input is studied. By expanding the Doppler input into a Fourier series and applying the linearized PLL approximations, it is easy to show that, for periodic frequency disturbances, the resulting steady state phase error is also periodic. Compared to the method of expanding frequency excursion into a power series, the Fourier expansion method can be used to predict the maximum phase error excursion for a periodic Doppler input. For systems with a large Doppler rate fluctuation, such as an optical transponder aboard an Earth orbiting spacecraft, the method can be applied to test whether a lower order tracking loop can provide satisfactory tracking and thereby save the effect of a higher order loop design.

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

Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models.

PubMed

Yilmaz, Banu; Aras, Egemen; Nacar, Sinan; Kankal, Murat

2018-05-23

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Information systems and human error in the lab.

PubMed

Bissell, Michael G

2004-01-01

Health system costs in clinical laboratories are incurred daily due to human error. Indeed, a major impetus for automating clinical laboratories has always been the opportunity it presents to simultaneously reduce cost and improve quality of operations by decreasing human error. But merely automating these processes is not enough. To the extent that introduction of these systems results in operators having less practice in dealing with unexpected events or becoming deskilled in problemsolving, however new kinds of error will likely appear. Clinical laboratories could potentially benefit by integrating findings on human error from modern behavioral science into their operations. Fully understanding human error requires a deep understanding of human information processing and cognition. Predicting and preventing negative consequences requires application of this understanding to laboratory operations. Although the occurrence of a particular error at a particular instant cannot be absolutely prevented, human error rates can be reduced. The following principles are key: an understanding of the process of learning in relation to error; understanding the origin of errors since this knowledge can be used to reduce their occurrence; optimal systems should be forgiving to the operator by absorbing errors, at least for a time; although much is known by industrial psychologists about how to write operating procedures and instructions in ways that reduce the probability of error, this expertise is hardly ever put to use in the laboratory; and a feedback mechanism must be designed into the system that enables the operator to recognize in real time that an error has occurred.

Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2014-06-01

The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge, in the form of a prior distribution on an uncertainty class of feature-label distributions to which the true, but unknown, feature-distribution belongs, can facilitate accurate error estimation (in the mean-square sense) in circumstances where accurate completely model-free error estimation is impossible. This paper provides analytic asymptotically exact finite-sample approximations for various performance metrics of the resulting Bayesian Minimum Mean-Square-Error (MMSE) error estimator in the case of linear discriminant analysis (LDA) in the multivariate Gaussian model. These performance metrics include the first, second, and cross moments of the Bayesian MMSE error estimator with the true error of LDA, and therefore, the Root-Mean-Square (RMS) error of the estimator. We lay down the theoretical groundwork for Kolmogorov double-asymptotics in a Bayesian setting, which enables us to derive asymptotic expressions of the desired performance metrics. From these we produce analytic finite-sample approximations and demonstrate their accuracy via numerical examples. Various examples illustrate the behavior of these approximations and their use in determining the necessary sample size to achieve a desired RMS. The Supplementary Material contains derivations for some equations and added figures.

Novel modes and adaptive block scanning order for intra prediction in AV1

NASA Astrophysics Data System (ADS)

Hadar, Ofer; Shleifer, Ariel; Mukherjee, Debargha; Joshi, Urvang; Mazar, Itai; Yuzvinsky, Michael; Tavor, Nitzan; Itzhak, Nati; Birman, Raz

2017-09-01

The demand for streaming video content is on the rise and growing exponentially. Networks bandwidth is very costly and therefore there is a constant effort to improve video compression rates and enable the sending of reduced data volumes while retaining quality of experience (QoE). One basic feature that utilizes the spatial correlation of pixels for video compression is Intra-Prediction, which determines the codec's compression efficiency. Intra prediction enables significant reduction of the Intra-Frame (I frame) size and, therefore, contributes to efficient exploitation of bandwidth. In this presentation, we propose new Intra-Prediction algorithms that improve the AV1 prediction model and provide better compression ratios. Two (2) types of methods are considered: )1( New scanning order method that maximizes spatial correlation in order to reduce prediction error; and )2( New Intra-Prediction modes implementation in AVI. Modern video coding standards, including AVI codec, utilize fixed scan orders in processing blocks during intra coding. The fixed scan orders typically result in residual blocks with high prediction error mainly in blocks with edges. This means that the fixed scan orders cannot fully exploit the content-adaptive spatial correlations between adjacent blocks, thus the bitrate after compression tends to be large. To reduce the bitrate induced by inaccurate intra prediction, the proposed approach adaptively chooses the scanning order of blocks according to criteria of firstly predicting blocks with maximum number of surrounding, already Inter-Predicted blocks. Using the modified scanning order method and the new modes has reduced the MSE by up to five (5) times when compared to conventional TM mode / Raster scan and up to two (2) times when compared to conventional CALIC mode / Raster scan, depending on the image characteristics (which determines the percentage of blocks predicted with Inter-Prediction, which in turn impacts the efficiency of the new scanning method). For the same cases, the PSNR was shown to improve by up to 7.4dB and up to 4 dB, respectively. The new modes have yielded 5% improvement in BD-Rate over traditionally used modes, when run on K-Frame, which is expected to yield 1% of overall improvement.

A predictive multi-linear regression model for organic micropollutants, based on a laboratory-scale column study simulating the river bank filtration process.

PubMed

Bertelkamp, C; Verliefde, A R D; Reynisson, J; Singhal, N; Cabo, A J; de Jonge, M; van der Hoek, J P

2016-03-05

This study investigated relationships between OMP biodegradation rates and the functional groups present in the chemical structure of a mixture of 31 OMPs. OMP biodegradation rates were determined from lab-scale columns filled with soil from RBF site Engelse Werk of the drinking water company Vitens in The Netherlands. A statistically significant relationship was found between OMP biodegradation rates and the functional groups of the molecular structures of OMPs in the mixture. The OMP biodegradation rate increased in the presence of carboxylic acids, hydroxyl groups, and carbonyl groups, but decreased in the presence of ethers, halogens, aliphatic ethers, methyl groups and ring structures in the chemical structure of the OMPs. The predictive model obtained from the lab-scale soil column experiment gave an accurate qualitative prediction of biodegradability for approximately 70% of the OMPs monitored in the field (80% excluding the glymes). The model was found to be less reliable for the more persistent OMPs (OMPs with predicted biodegradation rates lower or around the standard error=0.77d(-1)) and OMPs containing amide or amine groups. These OMPs should be carefully monitored in the field to determine their removal during RBF. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

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

Forecasting Financial Extremes: A Network Degree Measure of Super-Exponential Growth.

PubMed

Yan, Wanfeng; van Tuyll van Serooskerken, Edgar

2015-01-01

Investors in stock market are usually greedy during bull markets and scared during bear markets. The greed or fear spreads across investors quickly. This is known as the herding effect, and often leads to a fast movement of stock prices. During such market regimes, stock prices change at a super-exponential rate and are normally followed by a trend reversal that corrects the previous overreaction. In this paper, we construct an indicator to measure the magnitude of the super-exponential growth of stock prices, by measuring the degree of the price network, generated from the price time series. Twelve major international stock indices have been investigated. Error diagram tests show that this new indicator has strong predictive power for financial extremes, both peaks and troughs. By varying the parameters used to construct the error diagram, we show the predictive power is very robust. The new indicator has a better performance than the LPPL pattern recognition indicator.

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.

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.

The Relevance of External Quality Assessment for Molecular Testing for ALK Positive Non-Small Cell Lung Cancer: Results from Two Pilot Rounds Show Room for Optimization

PubMed Central

Tembuyser, Lien; Tack, Véronique; Zwaenepoel, Karen; Pauwels, Patrick; Miller, Keith; Bubendorf, Lukas; Kerr, Keith; Schuuring, Ed; Thunnissen, Erik; Dequeker, Elisabeth M. C.

2014-01-01

Background and Purpose Molecular profiling should be performed on all advanced non-small cell lung cancer with non-squamous histology to allow treatment selection. Currently, this should include EGFR mutation testing and testing for ALK rearrangements. ROS1 is another emerging target. ALK rearrangement status is a critical biomarker to predict response to tyrosine kinase inhibitors such as crizotinib. To promote high quality testing in non-small cell lung cancer, the European Society of Pathology has introduced an external quality assessment scheme. This article summarizes the results of the first two pilot rounds organized in 2012–2013. Materials and Methods Tissue microarray slides consisting of cell-lines and resection specimens were distributed with the request for routine ALK testing using IHC or FISH. Participation in ALK FISH testing included the interpretation of four digital FISH images. Results Data from 173 different laboratories was obtained. Results demonstrate decreased error rates in the second round for both ALK FISH and ALK IHC, although the error rates were still high and the need for external quality assessment in laboratories performing ALK testing is evident. Error rates obtained by FISH were lower than by IHC. The lowest error rates were observed for the interpretation of digital FISH images. Conclusion There was a large variety in FISH enumeration practices. Based on the results from this study, recommendations for the methodology, analysis, interpretation and result reporting were issued. External quality assessment is a crucial element to improve the quality of molecular testing. PMID:25386659

Incorporating historical information in biosimilar trials: Challenges and a hybrid Bayesian-frequentist approach.

PubMed

Mielke, Johanna; Schmidli, Heinz; Jones, Byron

2018-05-01

For the approval of biosimilars, it is, in most cases, necessary to conduct large Phase III clinical trials in patients to convince the regulatory authorities that the product is comparable in terms of efficacy and safety to the originator product. As the originator product has already been studied in several trials beforehand, it seems natural to include this historical information into the showing of equivalent efficacy. Since all studies for the regulatory approval of biosimilars are confirmatory studies, it is required that the statistical approach has reasonable frequentist properties, most importantly, that the Type I error rate is controlled-at least in all scenarios that are realistic in practice. However, it is well known that the incorporation of historical information can lead to an inflation of the Type I error rate in the case of a conflict between the distribution of the historical data and the distribution of the trial data. We illustrate this issue and confirm, using the Bayesian robustified meta-analytic-predictive (MAP) approach as an example, that simultaneously controlling the Type I error rate over the complete parameter space and gaining power in comparison to a standard frequentist approach that only considers the data in the new study, is not possible. We propose a hybrid Bayesian-frequentist approach for binary endpoints that controls the Type I error rate in the neighborhood of the center of the prior distribution, while improving the power. We study the properties of this approach in an extensive simulation study and provide a real-world example. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The relevance of external quality assessment for molecular testing for ALK positive non-small cell lung cancer: results from two pilot rounds show room for optimization.

PubMed

Tembuyser, Lien; Tack, Véronique; Zwaenepoel, Karen; Pauwels, Patrick; Miller, Keith; Bubendorf, Lukas; Kerr, Keith; Schuuring, Ed; Thunnissen, Erik; Dequeker, Elisabeth M C

2014-01-01

Molecular profiling should be performed on all advanced non-small cell lung cancer with non-squamous histology to allow treatment selection. Currently, this should include EGFR mutation testing and testing for ALK rearrangements. ROS1 is another emerging target. ALK rearrangement status is a critical biomarker to predict response to tyrosine kinase inhibitors such as crizotinib. To promote high quality testing in non-small cell lung cancer, the European Society of Pathology has introduced an external quality assessment scheme. This article summarizes the results of the first two pilot rounds organized in 2012-2013. Tissue microarray slides consisting of cell-lines and resection specimens were distributed with the request for routine ALK testing using IHC or FISH. Participation in ALK FISH testing included the interpretation of four digital FISH images. Data from 173 different laboratories was obtained. Results demonstrate decreased error rates in the second round for both ALK FISH and ALK IHC, although the error rates were still high and the need for external quality assessment in laboratories performing ALK testing is evident. Error rates obtained by FISH were lower than by IHC. The lowest error rates were observed for the interpretation of digital FISH images. There was a large variety in FISH enumeration practices. Based on the results from this study, recommendations for the methodology, analysis, interpretation and result reporting were issued. External quality assessment is a crucial element to improve the quality of molecular testing.

Predictive model for the growth of spoilage bacteria on modified atmosphere packaged Atlantic salmon produced in Australia.

PubMed

Powell, S M; Ratkowsky, D A; Tamplin, M L

2015-05-01

Most existing models for the spoilage of modified atmosphere packed Atlantic salmon are based on the growth of the spoilage organism Photobacterium phosphoreum. However, there is evidence that this organism is not the specific spoilage organism on salmon produced and packaged in Australia. We developed a predictive model for the growth of bacteria in Australian-produced Atlantic salmon stored under modified atmosphere conditions (30-98% carbon dioxide in nitrogen) at refrigeration temperatures (0-10 °C). As expected, both higher levels of carbon dioxide and lower temperatures decreased the observed growth rates of the total population. A Bělehrádek-type model for growth rate fitted the data best with an acceptably low root mean square error. At low temperatures (∼0 °C) the growth rates in this study were similar to those predicted by other models but at higher temperatures (∼10 °C) the growth rates were significantly lower in the current study. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

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.

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

USGS Publications Warehouse

Wright, C.; Gallant, Alisa L.

2007-01-01

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub–shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.

Determination of the Proper Rest Time for a Cyclic Mental Task Using ACT-R Architecture.

PubMed

Atashfeshan, Nooshin; Razavi, Hamideh

2017-03-01

Objective Analysis of the effect of mental fatigue on a cognitive task and determination of the right start time for rest breaks in work environments. Background Mental fatigue has been recognized as one of the most important factors influencing individual performance. Subjective and physiological measures are popular methods for analyzing fatigue, but they are restricted to physical experiments. Computational cognitive models are useful for predicting operator performance and can be used for analyzing fatigue in the design phase, particularly in industrial operations and inspections where cognitive tasks are frequent and the effects of mental fatigue are crucial. Method A cyclic mental task is modeled by the ACT-R architecture, and the effect of mental fatigue on response time and error rate is studied. The task includes visual inspections in a production line or control workstation where an operator has to check products' conformity to specifications. Initially, simulated and experimental results are compared using correlation coefficients and paired t test statistics. After validation of the model, the effects are studied by human and simulated results, which are obtained by running 50-minute tests. Results It is revealed that during the last 20 minutes of the tests, the response time increased by 20%, and during the last 12.5 minutes, the error rate increased by 7% on average. Conclusion The proper start time for the rest period can be identified by setting a limit on the error rate or response time. Application The proposed model can be applied early in production planning to decrease the negative effects of mental fatigue by predicting the operator performance. It can also be used for determining the rest breaks in the design phase without an operator in the loop.

Validity of the two-level model for Viterbi decoder gap-cycle performance

NASA Technical Reports Server (NTRS)

Dolinar, S.; Arnold, S.

1990-01-01

A two-level model has previously been proposed for approximating the performance of a Viterbi decoder which encounters data received with periodically varying signal-to-noise ratio. Such cyclically gapped data is obtained from the Very Large Array (VLA), either operating as a stand-alone system or arrayed with Goldstone. This approximate model predicts that the decoder error rate will vary periodically between two discrete levels with the same period as the gap cycle. It further predicts that the length of the gapped portion of the decoder error cycle for a constraint length K decoder will be about K-1 bits shorter than the actual duration of the gap. The two-level model for Viterbi decoder performance with gapped data is subjected to detailed validation tests. Curves showing the cyclical behavior of the decoder error burst statistics are compared with the simple square-wave cycles predicted by the model. The validity of the model depends on a parameter often considered irrelevant in the analysis of Viterbi decoder performance, the overall scaling of the received signal or the decoder's branch-metrics. Three scaling alternatives are examined: optimum branch-metric scaling and constant branch-metric scaling combined with either constant noise-level scaling or constant signal-level scaling. The simulated decoder error cycle curves roughly verify the accuracy of the two-level model for both the case of optimum branch-metric scaling and the case of constant branch-metric scaling combined with constant noise-level scaling. However, the model is not accurate for the case of constant branch-metric scaling combined with constant signal-level scaling.

Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

PubMed Central

Hanauer, D.A.

2014-01-01

Summary Background Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity. Objective To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows. Methods Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost. Results From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods. Conclusions This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient’s show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity. PMID:25298821

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

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.

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.

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.

Estimation of Handling Qualities Parameters of the Tu-144 Supersonic Transport Aircraft from Flight Test Data

NASA Technical Reports Server (NTRS)

Curry, Timothy J.; Batterson, James G. (Technical Monitor)

2000-01-01

Low order equivalent system (LOES) models for the Tu-144 supersonic transport aircraft were identified from flight test data. The mathematical models were given in terms of transfer functions with a time delay by the military standard MIL-STD-1797A, "Flying Qualities of Piloted Aircraft," and the handling qualities were predicted from the estimated transfer function coefficients. The coefficients and the time delay in the transfer functions were estimated using a nonlinear equation error formulation in the frequency domain. Flight test data from pitch, roll, and yaw frequency sweeps at various flight conditions were used for parameter estimation. Flight test results are presented in terms of the estimated parameter values, their standard errors, and output fits in the time domain. Data from doublet maneuvers at the same flight conditions were used to assess the predictive capabilities of the identified models. The identified transfer function models fit the measured data well and demonstrated good prediction capabilities. The Tu-144 was predicted to be between level 2 and 3 for all longitudinal maneuvers and level I for all lateral maneuvers. High estimates of the equivalent time delay in the transfer function model caused the poor longitudinal rating.

MLS (Microwave Landing System) Multipath Studies, Phase 3. Volume 3. Application of Models to MLS Assessment Issues. Part 1. Chapters 1 through 4.

DTIC Science & Technology

1981-06-08

generated by the AWOP panel members from the FRG, Netherlands, U.K., and U.S. 1-3 Airport-specific simulations, intended to predict MLS performance at a...05. C.𔃼 C. 00 0,5 r, !,. . lSTM FIN I l "i Fig. 2-89. Elevation rate error filter output for scenario 4. 2-116...Report No. 1, Texas Instruments Incorporated, Dallas, Texas (25 June 1975), ALEX(03)-TR-75-01. 50. J. Makhoul, "Linear Prediction ; A Tutorial Review

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.

Variational Continuous Assimilation of TMI and SSM/I Rain Rates: Impact on GEOS-3 Hurricane Analyses and Forecasts

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; Reale, Oreste

2003-01-01

We describe a variational continuous assimilation (VCA) algorithm for assimilating tropical rainfall data using moisture and temperature tendency corrections as the control variable to offset model deficiencies. For rainfall assimilation, model errors are of special concern since model-predicted precipitation is based on parameterized moist physics, which can have substantial systematic errors. This study examines whether a VCA scheme using the forecast model as a weak constraint offers an effective pathway to precipitation assimilation. The particular scheme we exarnine employs a '1+1' dimension precipitation observation operator based on a 6-h integration of a column model of moist physics from the Goddard Earth Observing System (GEOS) global data assimilation system DAS). In earlier studies, we tested a simplified version of this scheme and obtained improved monthly-mean analyses and better short-range forecast skills. This paper describes the full implementation ofthe 1+1D VCA scheme using background and observation error statistics, and examines how it may improve GEOS analyses and forecasts of prominent tropical weather systems such as hurricanes. Parallel assimilation experiments with and without rainfall data for Hurricanes Bonnie and Floyd show that assimilating 6-h TMI and SSM/I surfice rain rates leads to more realistic storm features in the analysis, which, in turn, provide better initial conditions for 5-day storm track prediction and precipitation forecast. These results provide evidence that addressing model deficiencies in moisture tendency may be crucial to making effective use of precipitation information in data assimilation.

Modeling and validation of autoinducer-mediated bacterial gene expression in microfluidic environments

PubMed Central

Austin, Caitlin M.; Stoy, William; Su, Peter; Harber, Marie C.; Bardill, J. Patrick; Hammer, Brian K.; Forest, Craig R.

2014-01-01

Biosensors exploiting communication within genetically engineered bacteria are becoming increasingly important for monitoring environmental changes. Currently, there are a variety of mathematical models for understanding and predicting how genetically engineered bacteria respond to molecular stimuli in these environments, but as sensors have miniaturized towards microfluidics and are subjected to complex time-varying inputs, the shortcomings of these models have become apparent. The effects of microfluidic environments such as low oxygen concentration, increased biofilm encapsulation, diffusion limited molecular distribution, and higher population densities strongly affect rate constants for gene expression not accounted for in previous models. We report a mathematical model that accurately predicts the biological response of the autoinducer N-acyl homoserine lactone-mediated green fluorescent protein expression in reporter bacteria in microfluidic environments by accommodating these rate constants. This generalized mass action model considers a chain of biomolecular events from input autoinducer chemical to fluorescent protein expression through a series of six chemical species. We have validated this model against experimental data from our own apparatus as well as prior published experimental results. Results indicate accurate prediction of dynamics (e.g., 14% peak time error from a pulse input) and with reduced mean-squared error with pulse or step inputs for a range of concentrations (10 μM–30 μM). This model can help advance the design of genetically engineered bacteria sensors and molecular communication devices. PMID:25379076

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.

Integration of Harvest and Time-to-Event Data Used to Estimate Demographic Parameters for White-tailed Deer

NASA Astrophysics Data System (ADS)

Norton, Andrew S.

An integral component of managing game species is an understanding of population dynamics and relative abundance. Harvest data are frequently used to estimate abundance of white-tailed deer. Unless harvest age-structure is representative of the population age-structure and harvest vulnerability remains constant from year to year, these data alone are of limited value. Additional model structure and auxiliary information has accommodated this shortcoming. Specifically, integrated age-at-harvest (AAH) state-space population models can formally combine multiple sources of data, and regularization via hierarchical model structure can increase flexibility of model parameters. I collected known fates data, which I evaluated and used to inform trends in survival parameters for an integrated AAH model. I used temperature and snow depth covariates to predict survival outside of the hunting season, and opening weekend temperature and percent of corn harvest covariates to predict hunting season survival. When auxiliary empirical data were unavailable for the AAH model, moderately informative priors provided sufficient information for convergence and parameter estimates. The AAH model was most sensitive to errors in initial abundance, but this error was calibrated after 3 years. Among vital rates, the AAH model was most sensitive to reporting rates (percentage of mortality during the hunting season related to harvest). The AAH model, using only harvest data, was able to track changing abundance trends due to changes in survival rates even when prior models did not inform these changes (i.e. prior models were constant when truth varied). I also compared AAH model results with estimates from the Wisconsin Department of Natural Resources (WIDNR). Trends in abundance estimates from both models were similar, although AAH model predictions were systematically higher than WIDNR estimates in the East study area. When I incorporated auxiliary information (i.e. integrated AAH model) about survival outside the hunting season from known fates data, predicted trends appeared more closely related to what was expected. Disagreements between the AAH model and WIDNR estimates in the East were likely related to biased predictions for reporting and survival rates from the AAH model.

Research on On-Line Modeling of Fed-Batch Fermentation Process Based on v-SVR

NASA Astrophysics Data System (ADS)

Ma, Yongjun

The fermentation process is very complex and non-linear, many parameters are not easy to measure directly on line, soft sensor modeling is a good solution. This paper introduces v-support vector regression (v-SVR) for soft sensor modeling of fed-batch fermentation process. v-SVR is a novel type of learning machine. It can control the accuracy of fitness and prediction error by adjusting the parameter v. An on-line training algorithm is discussed in detail to reduce the training complexity of v-SVR. The experimental results show that v-SVR has low error rate and better generalization with appropriate v.

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.

The Mote In Thy Brother's Eye, and The Beam in Thine Own: Predicting One's Own and Others' Personality Test Scores.

ERIC Educational Resources Information Center

Furnham, Adrian; Henderson, Monika

1983-01-01

Examined the similarity between subjects' (N=63) ratings of themselves and others, on various tests of personality. Results revealed that subjects correctly estimated several of their own scores, but only two scores of another person. They believed themselves to be similar to their friend, thereby showing attributional errors. (JAC)

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

Interplay between Hippocampal Sharp-Wave-Ripple Events and Vicarious Trial and Error Behaviors in Decision Making.

PubMed

Papale, Andrew E; Zielinski, Mark C; Frank, Loren M; Jadhav, Shantanu P; Redish, A David

2016-12-07

Current theories posit that memories encoded during experiences are subsequently consolidated into longer-term storage. Hippocampal sharp-wave-ripple (SWR) events have been linked to this consolidation process during sleep, but SWRs also occur during awake immobility, where their role remains unclear. We report that awake SWR rates at the reward site are inversely related to the prevalence of vicarious trial and error (VTE) behaviors, thought to be involved in deliberation processes. SWR rates were diminished immediately after VTE behaviors and an increase in the rate of SWR events at the reward site predicted a decrease in subsequent VTE behaviors at the choice point. Furthermore, SWR disruptions increased VTE behaviors. These results suggest an inverse relationship between SWRs and VTE behaviors and suggest that awake SWRs and associated planning and memory consolidation mechanisms are engaged specifically in the context of higher levels of behavioral certainty. Copyright © 2016 Elsevier Inc. All rights reserved.

Four-dimensional data coupled to alternating weighted residue constraint quadrilinear decomposition model applied to environmental analysis: Determination of polycyclic aromatic hydrocarbons.

PubMed

Liu, Tingting; Zhang, Ling; Wang, Shutao; Cui, Yaoyao; Wang, Yutian; Liu, Lingfei; Yang, Zhe

2018-03-15

Qualitative and quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) was carried out by three-dimensional fluorescence spectroscopy combining with Alternating Weighted Residue Constraint Quadrilinear Decomposition (AWRCQLD). The experimental subjects were acenaphthene (ANA) and naphthalene (NAP). Firstly, in order to solve the redundant information of the three-dimensional fluorescence spectral data, the wavelet transform was used to compress data in preprocessing. Then, the four-dimensional data was constructed by using the excitation-emission fluorescence spectra of different concentration PAHs. The sample data was obtained from three solvents that are methanol, ethanol and Ultra-pure water. The four-dimensional spectral data was analyzed by AWRCQLD, then the recovery rate of PAHs was obtained from the three solvents and compared respectively. On one hand, the results showed that PAHs can be measured more accurately by the high-order data, and the recovery rate was higher. On the other hand, the results presented that AWRCQLD can better reflect the superiority of four-dimensional algorithm than the second-order calibration and other third-order calibration algorithms. The recovery rate of ANA was 96.5%~103.3% and the root mean square error of prediction was 0.04μgL -1 . The recovery rate of NAP was 96.7%~115.7% and the root mean square error of prediction was 0.06μgL -1 . Copyright © 2017 Elsevier B.V. All rights reserved.

A comparative study on improved Arrhenius-type and artificial neural network models to predict high-temperature flow behaviors in 20MnNiMo alloy.

PubMed

Quan, Guo-zheng; Yu, Chun-tang; Liu, Ying-ying; Xia, Yu-feng

2014-01-01

The stress-strain data of 20MnNiMo alloy were collected from a series of hot compressions on Gleeble-1500 thermal-mechanical simulator in the temperature range of 1173 ∼ 1473 K and strain rate range of 0.01 ∼ 10 s(-1). Based on the experimental data, the improved Arrhenius-type constitutive model and the artificial neural network (ANN) model were established to predict the high temperature flow stress of as-cast 20MnNiMo alloy. The accuracy and reliability of the improved Arrhenius-type model and the trained ANN model were further evaluated in terms of the correlation coefficient (R), the average absolute relative error (AARE), and the relative error (η). For the former, R and AARE were found to be 0.9954 and 5.26%, respectively, while, for the latter, 0.9997 and 1.02%, respectively. The relative errors (η) of the improved Arrhenius-type model and the ANN model were, respectively, in the range of -39.99% ∼ 35.05% and -3.77% ∼ 16.74%. As for the former, only 16.3% of the test data set possesses η-values within ± 1%, while, as for the latter, more than 79% possesses. The results indicate that the ANN model presents a higher predictable ability than the improved Arrhenius-type constitutive model.

Estimation of Human Body Volume (BV) from Anthropometric Measurements Based on Three-Dimensional (3D) Scan Technique.

PubMed

Liu, Xingguo; Niu, Jianwei; Ran, Linghua; Liu, Taijie

2017-08-01

This study aimed to develop estimation formulae for the total human body volume (BV) of adult males using anthropometric measurements based on a three-dimensional (3D) scanning technique. Noninvasive and reliable methods to predict the total BV from anthropometric measurements based on a 3D scan technique were addressed in detail. A regression analysis of BV based on four key measurements was conducted for approximately 160 adult male subjects. Eight total models of human BV show that the predicted results fitted by the regression models were highly correlated with the actual BV (p < 0.001). Two metrics, the mean value of the absolute difference between the actual and predicted BV (V error ) and the mean value of the ratio between V error and actual BV (RV error ), were calculated. The linear model based on human weight was recommended as the most optimal due to its simplicity and high efficiency. The proposed estimation formulae are valuable for estimating total body volume in circumstances in which traditional underwater weighing or air displacement plethysmography is not applicable or accessible. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Antipsychotic dose modulates behavioral and neural responses to feedback during reinforcement learning in schizophrenia.

PubMed

Insel, Catherine; Reinen, Jenna; Weber, Jochen; Wager, Tor D; Jarskog, L Fredrik; Shohamy, Daphna; Smith, Edward E

2014-03-01

Schizophrenia is characterized by an abnormal dopamine system, and dopamine blockade is the primary mechanism of antipsychotic treatment. Consistent with the known role of dopamine in reward processing, prior research has demonstrated that patients with schizophrenia exhibit impairments in reward-based learning. However, it remains unknown how treatment with antipsychotic medication impacts the behavioral and neural signatures of reinforcement learning in schizophrenia. The goal of this study was to examine whether antipsychotic medication modulates behavioral and neural responses to prediction error coding during reinforcement learning. Patients with schizophrenia completed a reinforcement learning task while undergoing functional magnetic resonance imaging. The task consisted of two separate conditions in which participants accumulated monetary gain or avoided monetary loss. Behavioral results indicated that antipsychotic medication dose was associated with altered behavioral approaches to learning, such that patients taking higher doses of medication showed increased sensitivity to negative reinforcement. Higher doses of antipsychotic medication were also associated with higher learning rates (LRs), suggesting that medication enhanced sensitivity to trial-by-trial feedback. Neuroimaging data demonstrated that antipsychotic dose was related to differences in neural signatures of feedback prediction error during the loss condition. Specifically, patients taking higher doses of medication showed attenuated prediction error responses in the striatum and the medial prefrontal cortex. These findings indicate that antipsychotic medication treatment may influence motivational processes in patients with schizophrenia.

The efficacy of protoporphyrin as a predictive biomarker for lead exposure in canvasback ducks: effect of sample storage time

USGS Publications Warehouse

Franson, J.C.; Hohman, W.L.; Moore, J.L.; Smith, M.R.

1996-01-01

We used 363 blood samples collected from wild canvasback dueks (Aythya valisineria) at Catahoula Lake, Louisiana, U.S.A. to evaluate the effect of sample storage time on the efficacy of erythrocytic protoporphyrin as an indicator of lead exposure. The protoporphyrin concentration of each sample was determined by hematofluorometry within 5 min of blood collection and after refrigeration at 4 °C for 24 and 48 h. All samples were analyzed for lead by atomic absorption spectrophotometry. Based on a blood lead concentration of ≥0.2 ppm wet weight as positive evidence for lead exposure, the protoporphyrin technique resulted in overall error rates of 29%, 20%, and 19% and false negative error rates of 47%, 29% and 25% when hematofluorometric determinations were made on blood at 5 min, 24 h, and 48 h, respectively. False positive error rates were less than 10% for all three measurement times. The accuracy of the 24-h erythrocytic protoporphyrin classification of blood samples as positive or negative for lead exposure was significantly greater than the 5-min classification, but no improvement in accuracy was gained when samples were tested at 48 h. The false negative errors were probably due, at least in part, to the lag time between lead exposure and the increase of blood protoporphyrin concentrations. False negatives resulted in an underestimation of the true number of canvasbacks exposed to lead, indicating that hematofluorometry provides a conservative estimate of lead exposure.

Estimating top-of-atmosphere thermal infrared radiance using MERRA-2 atmospheric data

NASA Astrophysics Data System (ADS)

Kleynhans, Tania; Montanaro, Matthew; Gerace, Aaron; Kanan, Christopher

2017-05-01

Thermal infrared satellite images have been widely used in environmental studies. However, satellites have limited temporal resolution, e.g., 16 day Landsat or 1 to 2 day Terra MODIS. This paper investigates the use of the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data product, produced by NASA's Global Modeling and Assimilation Office (GMAO) to predict global topof-atmosphere (TOA) thermal infrared radiance. The high temporal resolution of the MERRA-2 data product presents opportunities for novel research and applications. Various methods were applied to estimate TOA radiance from MERRA-2 variables namely (1) a parameterized physics based method, (2) Linear regression models and (3) non-linear Support Vector Regression. Model prediction accuracy was evaluated using temporally and spatially coincident Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data as reference data. This research found that Support Vector Regression with a radial basis function kernel produced the lowest error rates. Sources of errors are discussed and defined. Further research is currently being conducted to train deep learning models to predict TOA thermal radiance

Estimation of State Transition Probabilities: A Neural Network Model

NASA Astrophysics Data System (ADS)

Saito, Hiroshi; Takiyama, Ken; Okada, Masato

2015-12-01

Humans and animals can predict future states on the basis of acquired knowledge. This prediction of the state transition is important for choosing the best action, and the prediction is only possible if the state transition probability has already been learned. However, how our brains learn the state transition probability is unknown. Here, we propose a simple algorithm for estimating the state transition probability by utilizing the state prediction error. We analytically and numerically confirmed that our algorithm is able to learn the probability completely with an appropriate learning rate. Furthermore, our learning rule reproduced experimentally reported psychometric functions and neural activities in the lateral intraparietal area in a decision-making task. Thus, our algorithm might describe the manner in which our brains learn state transition probabilities and predict future states.

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.

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.

Development of steady-state model for MSPT and detailed analyses of receiver

NASA Astrophysics Data System (ADS)

Yuasa, Minoru; Sonoda, Masanori; Hino, Koichi

2016-05-01

Molten salt parabolic trough system (MSPT) uses molten salt as heat transfer fluid (HTF) instead of synthetic oil. The demonstration plant of MSPT was constructed by Chiyoda Corporation and Archimede Solar Energy in Italy in 2013. Chiyoda Corporation developed a steady-state model for predicting the theoretical behavior of the demonstration plant. The model was designed to calculate the concentrated solar power and heat loss using ray tracing of incident solar light and finite element modeling of thermal energy transferred into the medium. This report describes the verification of the model using test data on the demonstration plant, detailed analyses on the relation between flow rate and temperature difference on the metal tube of receiver and the effect of defocus angle on concentrated power rate, for solar collector assembly (SCA) development. The model is accurate to an extent of 2.0% as systematic error and 4.2% as random error. The relationships between flow rate and temperature difference on metal tube and the effect of defocus angle on concentrated power rate are shown.

Growth and food consumption by tiger muskellunge: Effects of temperature and ration level on bioenergetic model predictions

USGS Publications Warehouse

Chipps, S.R.; Einfalt, L.M.; Wahl, David H.

2000-01-01

We measured growth of age-0 tiger muskellunge as a function of ration size (25, 50, 75, and 100% C(max))and water temperature (7.5-25??C) and compared experimental results with those predicted from a bioenergetic model. Discrepancies between actual and predicted values varied appreciably with water temperature and growth rate. On average, model output overestimated winter consumption rates at 10 and 7.5??C by 113 to 328%, respectively, whereas model predictions in summer and autumn (20-25??C) were in better agreement with actual values (4 to 58%). We postulate that variation in model performance was related to seasonal changes in esocid metabolic rate, which were not accounted for in the bioenergetic model. Moreover, accuracy of model output varied with feeding and growth rate of tiger muskellunge. The model performed poorly for fish fed low rations compared with estimates based on fish fed ad libitum rations and was attributed, in part, to the influence of growth rate on the accuracy of bioenergetic predictions. Based on modeling simulations, we found that errors associated with bioenergetic parameters had more influence on model output when growth rate was low, which is consistent with our observations. In addition, reduced conversion efficiency at high ration levels may contribute to variable model performance, thereby implying that waste losses should be modeled as a function of ration size for esocids. Our findings support earlier field tests of the esocid bioenergetic model and indicate that food consumption is generally overestimated by the model, particularly in winter months and for fish exhibiting low feeding and growth rates.

Statistical process control methods allow the analysis and improvement of anesthesia care.

PubMed

Fasting, Sigurd; Gisvold, Sven E

2003-10-01

Quality aspects of the anesthetic process are reflected in the rate of intraoperative adverse events. The purpose of this report is to illustrate how the quality of the anesthesia process can be analyzed using statistical process control methods, and exemplify how this analysis can be used for quality improvement. We prospectively recorded anesthesia-related data from all anesthetics for five years. The data included intraoperative adverse events, which were graded into four levels, according to severity. We selected four adverse events, representing important quality and safety aspects, for statistical process control analysis. These were: inadequate regional anesthesia, difficult emergence from general anesthesia, intubation difficulties and drug errors. We analyzed the underlying process using 'p-charts' for statistical process control. In 65,170 anesthetics we recorded adverse events in 18.3%; mostly of lesser severity. Control charts were used to define statistically the predictable normal variation in problem rate, and then used as a basis for analysis of the selected problems with the following results: Inadequate plexus anesthesia: stable process, but unacceptably high failure rate; Difficult emergence: unstable process, because of quality improvement efforts; Intubation difficulties: stable process, rate acceptable; Medication errors: methodology not suited because of low rate of errors. By applying statistical process control methods to the analysis of adverse events, we have exemplified how this allows us to determine if a process is stable, whether an intervention is required, and if quality improvement efforts have the desired effect.

Solving Upwind-Biased Discretizations: Defect-Correction Iterations

NASA Technical Reports Server (NTRS)

Diskin, Boris; Thomas, James L.

1999-01-01

This paper considers defect-correction solvers for a second order upwind-biased discretization of the 2D convection equation. The following important features are reported: (1) The asymptotic convergence rate is about 0.5 per defect-correction iteration. (2) If the operators involved in defect-correction iterations have different approximation order, then the initial convergence rates may be very slow. The number of iterations required to get into the asymptotic convergence regime might grow on fine grids as a negative power of h. In the case of a second order target operator and a first order driver operator, this number of iterations is roughly proportional to h-1/3. (3) If both the operators have the second approximation order, the defect-correction solver demonstrates the asymptotic convergence rate after three iterations at most. The same three iterations are required to converge algebraic error below the truncation error level. A novel comprehensive half-space Fourier mode analysis (which, by the way, can take into account the influence of discretized outflow boundary conditions as well) for the defect-correction method is developed. This analysis explains many phenomena observed in solving non-elliptic equations and provides a close prediction of the actual solution behavior. It predicts the convergence rate for each iteration and the asymptotic convergence rate. As a result of this analysis, a new very efficient adaptive multigrid algorithm solving the discrete problem to within a given accuracy is proposed. Numerical simulations confirm the accuracy of the analysis and the efficiency of the proposed algorithm. The results of the numerical tests are reported.

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.

New Generalized Equation for Predicting Maximal Oxygen Uptake (from the Fitness Registry and the Importance of Exercise National Database).

PubMed

Kokkinos, Peter; Kaminsky, Leonard A; Arena, Ross; Zhang, Jiajia; Myers, Jonathan

2017-08-15

Impaired cardiorespiratory fitness (CRF) is closely linked to chronic illness and associated with adverse events. The American College of Sports Medicine (ACSM) regression equations (ACSM equations) developed to estimate oxygen uptake have known limitations leading to well-documented overestimation of CRF, especially at higher work rates. Thus, there is a need to explore alternative equations to more accurately predict CRF. We assessed maximal oxygen uptake (VO 2 max) obtained directly by open-circuit spirometry in 7,983 apparently healthy subjects who participated in the Fitness Registry and the Importance of Exercise National Database (FRIEND). We randomly sampled 70% of the participants from each of the following age categories: <40, 40 to 50, 50 to 70, and ≥70 and used the remaining 30% for validation. Multivariable linear regression analysis was applied to identify the most relevant variables and construct the best prediction model for VO 2 max. Treadmill speed and treadmill speed × grade were considered in the final model as predictors of measured VO 2 max and the following equation was generated: VO 2 max in ml O 2 /kg/min = speed (m/min) × (0.17 + fractional grade × 0.79) + 3.5. The FRIEND equation predicted VO 2 max with an overall error >4 times lower than the error associated with the traditional ACSM equations (5.1 ± 18.3% vs 21.4 ± 24.9%, respectively). Overestimation associated with the ACSM equation was accentuated when different protocols were considered separately. In conclusion, The FRIEND equation predicts VO 2 max more precisely than the traditional ACSM equations with an overall error >4 times lower than that associated with the ACSM equations. Published by Elsevier Inc.

Decodability of Reward Learning Signals Predicts Mood Fluctuations.

PubMed

Eldar, Eran; Roth, Charlotte; Dayan, Peter; Dolan, Raymond J

2018-05-07

Our mood often fluctuates without warning. Recent accounts propose that these fluctuations might be preceded by changes in how we process reward. According to this view, the degree to which reward improves our mood reflects not only characteristics of the reward itself (e.g., its magnitude) but also how receptive to reward we happen to be. Differences in receptivity to reward have been suggested to play an important role in the emergence of mood episodes in psychiatric disorders [1-16]. However, despite substantial theory, the relationship between reward processing and daily fluctuations of mood has yet to be tested directly. In particular, it is unclear whether the extent to which people respond to reward changes from day to day and whether such changes are followed by corresponding shifts in mood. Here, we use a novel mobile-phone platform with dense data sampling and wearable heart-rate and electroencephalographic sensors to examine mood and reward processing over an extended period of one week. Subjects regularly performed a trial-and-error choice task in which different choices were probabilistically rewarded. Subjects' choices revealed two complementary learning processes, one fast and one slow. Reward prediction errors [17, 18] indicative of these two processes were decodable from subjects' physiological responses. Strikingly, more accurate decodability of prediction-error signals reflective of the fast process predicted improvement in subjects' mood several hours later, whereas more accurate decodability of the slow process' signals predicted better mood a whole day later. We conclude that real-life mood fluctuations follow changes in responsivity to reward at multiple timescales. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

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

Predicting areas of sustainable error growth in quasigeostrophic flows using perturbation alignment properties

NASA Astrophysics Data System (ADS)

Rivière, G.; Hua, B. L.

2004-10-01

A new perturbation initialization method is used to quantify error growth due to inaccuracies of the forecast model initial conditions in a quasigeostrophic box ocean model describing a wind-driven double gyre circulation. This method is based on recent analytical results on Lagrangian alignment dynamics of the perturbation velocity vector in quasigeostrophic flows. More specifically, it consists in initializing a unique perturbation from the sole knowledge of the control flow properties at the initial time of the forecast and whose velocity vector orientation satisfies a Lagrangian equilibrium criterion. This Alignment-based Initialization method is hereafter denoted as the AI method.In terms of spatial distribution of the errors, we have compared favorably the AI error forecast with the mean error obtained with a Monte-Carlo ensemble prediction. It is shown that the AI forecast is on average as efficient as the error forecast initialized with the leading singular vector for the palenstrophy norm, and significantly more efficient than that for total energy and enstrophy norms. Furthermore, a more precise examination shows that the AI forecast is systematically relevant for all control flows whereas the palenstrophy singular vector forecast leads sometimes to very good scores and sometimes to very bad ones.A principal component analysis at the final time of the forecast shows that the AI mode spatial structure is comparable to that of the first eigenvector of the error covariance matrix for a "bred mode" ensemble. Furthermore, the kinetic energy of the AI mode grows at the same constant rate as that of the "bred modes" from the initial time to the final time of the forecast and is therefore characterized by a sustained phase of error growth. In this sense, the AI mode based on Lagrangian dynamics of the perturbation velocity orientation provides a rationale of the "bred mode" behavior.

A visual detection model for DCT coefficient quantization

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Watson, Andrew B.

1994-01-01

The discrete cosine transform (DCT) is widely used in image compression and is part of the JPEG and MPEG compression standards. The degree of compression and the amount of distortion in the decompressed image are controlled by the quantization of the transform coefficients. The standards do not specify how the DCT coefficients should be quantized. One approach is to set the quantization level for each coefficient so that the quantization error is near the threshold of visibility. Results from previous work are combined to form the current best detection model for DCT coefficient quantization noise. This model predicts sensitivity as a function of display parameters, enabling quantization matrices to be designed for display situations varying in luminance, veiling light, and spatial frequency related conditions (pixel size, viewing distance, and aspect ratio). It also allows arbitrary color space directions for the representation of color. A model-based method of optimizing the quantization matrix for an individual image was developed. The model described above provides visual thresholds for each DCT frequency. These thresholds are adjusted within each block for visual light adaptation and contrast masking. For given quantization matrix, the DCT quantization errors are scaled by the adjusted thresholds to yield perceptual errors. These errors are pooled nonlinearly over the image to yield total perceptual error. With this model one may estimate the quantization matrix for a particular image that yields minimum bit rate for a given total perceptual error, or minimum perceptual error for a given bit rate. Custom matrices for a number of images show clear improvement over image-independent matrices. Custom matrices are compatible with the JPEG standard, which requires transmission of the quantization matrix.

Development of a scale of executive functioning for the RBANS.

PubMed

Spencer, Robert J; Kitchen Andren, Katherine A; Tolle, Kathryn A

2018-01-01

The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) is a cognitive battery that contains scales of several cognitive abilities, but no scale in the instrument is exclusively dedicated to executive functioning. Although the subtests allow for observation of executive-type errors, each error is of fairly low base rate, and healthy and clinical normative data are lacking on the frequency of these types of errors, making their significance difficult to interpret in isolation. The aim of this project was to create an RBANS executive errors scale (RBANS EE) with items comprised of qualitatively dysexecutive errors committed throughout the test. Participants included Veterans referred for outpatient neuropsychological testing. Items were initially selected based on theoretical literature and were retained based on item-total correlations. The RBANS EE (a percentage calculated by dividing the number of dysexecutive errors by the total number of responses) was moderately related to each of seven established measures of executive functioning and was strongly predictive of dichotomous classification of executive impairment. Thus, the scale had solid concurrent validity, justifying its use as a supplementary scale. The RBANS EE requires no additional administration time and can provide a quantified measure of otherwise unmeasured aspects of executive functioning.

A stitch in time saves nine: external quality assessment rounds demonstrate improved quality of biomarker analysis in lung cancer

PubMed Central

Keppens, Cleo; Tack, Véronique; Hart, Nils ‘t; Tembuyser, Lien; Ryska, Ales; Pauwels, Patrick; Zwaenepoel, Karen; Schuuring, Ed; Cabillic, Florian; Tornillo, Luigi; Warth, Arne; Weichert, Wilko; Dequeker, Elisabeth

2018-01-01

Biomarker analysis has become routine practice in the treatment of non-small cell lung cancer (NSCLC). To ensure high quality testing, participation to external quality assessment (EQA) schemes is essential. This article provides a longitudinal overview of the EQA performance for EGFR, ALK, and ROS1 analyses in NSCLC between 2012 and 2015. The four scheme years were organized by the European Society of Pathology according to the ISO 17043 standard. Participants were asked to analyze the provided tissue using their routine procedures. Analysis scores improved for individual laboratories upon participation to more EQA schemes, except for ROS1 immunohistochemistry (IHC). For EGFR analysis, scheme error rates were 18.8%, 14.1% and 7.5% in 2013, 2014 and 2015 respectively. For ALK testing, error rates decreased between 2012 and 2015 by 5.2%, 3.2% and 11.8% for the fluorescence in situ hybridization (FISH), FISH digital, and IHC subschemes, respectively. In contrast, for ROS1 error rates increased between 2014 and 2015 for FISH and IHC by 3.2% and 9.3%. Technical failures decreased over the years for all three markers. Results show that EQA contributes to an ameliorated performance for most predictive biomarkers in NSCLC. Room for improvement is still present, especially for ROS1 analysis. PMID:29755669

Bias correction for selecting the minimal-error classifier from many machine learning models.

PubMed

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Prediction of hot deformation behavior of high phosphorus steel using artificial neural network

NASA Astrophysics Data System (ADS)

Singh, Kanchan; Rajput, S. K.; Soota, T.; Verma, Vijay; Singh, Dharmendra

2018-03-01

To predict the hot deformation behavior of high phosphorus steel, the hot compression experiments were performed with the help of thermo-mechanical simulator Gleeble® 3800 in the temperatures ranging from 750 °C to 1050 °C and strain rates of 0.001 s-1, 0.01 s-1, 0.1 s-1, 0.5 s-1, 1.0 s-1 and 10 s-1. The experimental stress-strain data are employed to develop artificial neural network (ANN) model and their predictability. Using different combination of temperature, strain and strain rate as a input parameter and obtained experimental stress as a target, a multi-layer ANN model based on feed-forward back-propagation algorithm is trained, to predict the flow stress for a given processing condition. The relative error between predicted and experimental stress are in the range of ±3.5%, whereas the correlation coefficient (R2) of training and testing data are 0.99986 and 0.99999 respectively. This shows that a well-trained ANN model has excellent capability to predict the hot deformation behavior of materials. Comparative study shows quite good agreement of predicted and experimental values.

Assessing the Effects of Climate Variability on Orange Yield in Florida to Reduce Production Forecast Errors

NASA Astrophysics Data System (ADS)

Concha Larrauri, P.

2015-12-01

Orange production in Florida has experienced a decline over the past decade. Hurricanes in 2004 and 2005 greatly affected production, almost to the same degree as strong freezes that occurred in the 1980's. The spread of the citrus greening disease after the hurricanes has also contributed to a reduction in orange production in Florida. The occurrence of hurricanes and diseases cannot easily be predicted but the additional effects of climate on orange yield can be studied and incorporated into existing production forecasts that are based on physical surveys, such as the October Citrus forecast issued every year by the USDA. Specific climate variables ocurring before and after the October forecast is issued can have impacts on flowering, orange drop rates, growth, and maturation, and can contribute to the forecast error. Here we present a methodology to incorporate local climate variables to predict the USDA's orange production forecast error, and we study the local effects of climate on yield in different counties in Florida. This information can aid farmers to gain an insight on what is to be expected during the orange production cycle, and can help supply chain managers to better plan their strategy.

Social conflicts elicit an N400-like component.

PubMed

Huang, Yi; Kendrick, Keith M; Yu, Rongjun

2014-12-01

When people have different opinions, they often adjust their own attitude to match that of others, known as social conformity. How social conflicts trigger subsequent conformity remains unclear. One possibility is that a conflict with the group opinion is perceived as a violation of social information, analogous to using wrong grammar, and activates conflict monitoring and adjustment mechanisms. Using event related potential (ERP) recording combined with a face attractiveness judgment task, we investigated the neural encoding of social conflicts. We found that social conflicts elicit an N400-like negative deflection, being more negative for conflict with group opinions than no-conflict condition. The social conflict related signals also have a bi-directional profile similar to reward prediction error signals: it was more negative for under-estimation (i.e. one׳s own ratings were smaller than group ratings) than over-estimation, and the larger the differences between ratings, the larger the N400 amplitude. The N400 effects were significantly diminished in the non-social condition. We conclude that social conflicts are encoded in a bidirectional fashion in the N400-like component, similar to the pattern of reward-based prediction error signals. Our findings also suggest that the N400, a well-established ERP component encoding semantic violation, might be involved in social conflict processing and social learning. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Phenomenological Constitutive Modeling of High-Temperature Flow Behavior Incorporating Individual and Coupled Effects of Processing Parameters in Super-austenitic Stainless Steel

NASA Astrophysics Data System (ADS)

Roy, Swagata; Biswas, Srija; Babu, K. Arun; Mandal, Sumantra

2018-05-01

A novel constitutive model has been developed for predicting flow responses of super-austenitic stainless steel over a wide range of strains (0.05-0.6), temperatures (1173-1423 K) and strain rates (0.001-1 s-1). Further, the predictability of this new model has been compared with the existing Johnson-Cook (JC) and modified Zerilli-Armstrong (M-ZA) model. The JC model is not befitted for flow prediction as it is found to be exhibiting very high ( 36%) average absolute error (δ) and low ( 0.92) correlation coefficient (R). On the contrary, the M-ZA model has demonstrated relatively lower δ ( 13%) and higher R ( 0.96) for flow prediction. The incorporation of couplings of processing parameters in M-ZA model has led to exhibit better prediction than JC model. However, the flow analyses of the studied alloy have revealed the additional synergistic influences of strain and strain rate as well as strain, temperature, and strain rate apart from those considered in M-ZA model. Hence, the new phenomenological model has been formulated incorporating all the individual and synergistic effects of processing parameters and a `strain-shifting' parameter. The proposed model predicted the flow behavior of the alloy with much better correlation and generalization than M-ZA model as substantiated by its lower δ ( 7.9%) and higher R ( 0.99) of prediction.

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.

Patterns of Post-Stroke Brain Damage that Predict Speech Production Errors in Apraxia of Speech and Aphasia Dissociate

PubMed Central

Basilakos, Alexandra; Rorden, Chris; Bonilha, Leonardo; Moser, Dana; Fridriksson, Julius

2015-01-01

Background and Purpose Acquired apraxia of speech (AOS) is a motor speech disorder caused by brain damage. AOS often co-occurs with aphasia, a language disorder in which patients may also demonstrate speech production errors. The overlap of speech production deficits in both disorders has raised questions regarding if AOS emerges from a unique pattern of brain damage or as a sub-element of the aphasic syndrome. The purpose of this study was to determine whether speech production errors in AOS and aphasia are associated with distinctive patterns of brain injury. Methods Forty-three patients with history of a single left-hemisphere stroke underwent comprehensive speech and language testing. The Apraxia of Speech Rating Scale was used to rate speech errors specific to AOS versus speech errors that can also be associated with AOS and/or aphasia. Localized brain damage was identified using structural MRI, and voxel-based lesion-impairment mapping was used to evaluate the relationship between speech errors specific to AOS, those that can occur in AOS and/or aphasia, and brain damage. Results The pattern of brain damage associated with AOS was most strongly associated with damage to cortical motor regions, with additional involvement of somatosensory areas. Speech production deficits that could be attributed to AOS and/or aphasia were associated with damage to the temporal lobe and the inferior pre-central frontal regions. Conclusion AOS likely occurs in conjunction with aphasia due to the proximity of the brain areas supporting speech and language, but the neurobiological substrate for each disorder differs. PMID:25908457

Patterns of poststroke brain damage that predict speech production errors in apraxia of speech and aphasia dissociate.

PubMed

Basilakos, Alexandra; Rorden, Chris; Bonilha, Leonardo; Moser, Dana; Fridriksson, Julius

2015-06-01

Acquired apraxia of speech (AOS) is a motor speech disorder caused by brain damage. AOS often co-occurs with aphasia, a language disorder in which patients may also demonstrate speech production errors. The overlap of speech production deficits in both disorders has raised questions on whether AOS emerges from a unique pattern of brain damage or as a subelement of the aphasic syndrome. The purpose of this study was to determine whether speech production errors in AOS and aphasia are associated with distinctive patterns of brain injury. Forty-three patients with history of a single left-hemisphere stroke underwent comprehensive speech and language testing. The AOS Rating Scale was used to rate speech errors specific to AOS versus speech errors that can also be associated with both AOS and aphasia. Localized brain damage was identified using structural magnetic resonance imaging, and voxel-based lesion-impairment mapping was used to evaluate the relationship between speech errors specific to AOS, those that can occur in AOS or aphasia, and brain damage. The pattern of brain damage associated with AOS was most strongly associated with damage to cortical motor regions, with additional involvement of somatosensory areas. Speech production deficits that could be attributed to AOS or aphasia were associated with damage to the temporal lobe and the inferior precentral frontal regions. AOS likely occurs in conjunction with aphasia because of the proximity of the brain areas supporting speech and language, but the neurobiological substrate for each disorder differs. © 2015 American Heart Association, Inc.

A Transient Dopamine Signal Represents Avoidance Value and Causally Influences the Demand to Avoid

PubMed Central

Pultorak, Katherine J.; Schelp, Scott A.; Isaacs, Dominic P.; Krzystyniak, Gregory

2018-01-01

Abstract While an extensive literature supports the notion that mesocorticolimbic dopamine plays a role in negative reinforcement, recent evidence suggests that dopamine exclusively encodes the value of positive reinforcement. In the present study, we employed a behavioral economics approach to investigate whether dopamine plays a role in the valuation of negative reinforcement. Using rats as subjects, we first applied fast-scan cyclic voltammetry (FSCV) to determine that dopamine concentration decreases with the number of lever presses required to avoid electrical footshock (i.e., the economic price of avoidance). Analysis of the rate of decay of avoidance demand curves, which depict an inverse relationship between avoidance and increasing price, allows for inference of the worth an animal places on avoidance outcomes. Rapidly decaying demand curves indicate increased price sensitivity, or low worth placed on avoidance outcomes, while slow rates of decay indicate reduced price sensitivity, or greater worth placed on avoidance outcomes. We therefore used optogenetics to assess how inducing dopamine release causally modifies the demand to avoid electrical footshock in an economic setting. Increasing release at an avoidance predictive cue made animals more sensitive to price, consistent with a negative reward prediction error (i.e., the animal perceives they received a worse outcome than expected). Increasing release at avoidance made animals less sensitive to price, consistent with a positive reward prediction error (i.e., the animal perceives they received a better outcome than expected). These data demonstrate that transient dopamine release events represent the value of avoidance outcomes and can predictably modify the demand to avoid. PMID:29766047

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

Effects of parallel planning on agreement production.

PubMed

Veenstra, Alma; Meyer, Antje S; Acheson, Daniel J

2015-11-01

An important issue in current psycholinguistics is how the time course of utterance planning affects the generation of grammatical structures. The current study investigated the influence of parallel activation of the components of complex noun phrases on the generation of subject-verb agreement. Specifically, the lexical interference account (Gillespie & Pearlmutter, 2011b; Solomon & Pearlmutter, 2004) predicts more agreement errors (i.e., attraction) for subject phrases in which the head and local noun mismatch in number (e.g., the apple next to the pears) when nouns are planned in parallel than when they are planned in sequence. We used a speeded picture description task that yielded sentences such as the apple next to the pears is red. The objects mentioned in the noun phrase were either semantically related or unrelated. To induce agreement errors, pictures sometimes mismatched in number. In order to manipulate the likelihood of parallel processing of the objects and to test the hypothesized relationship between parallel processing and the rate of agreement errors, the pictures were either placed close together or far apart. Analyses of the participants' eye movements and speech onset latencies indicated slower processing of the first object and stronger interference from the related (compared to the unrelated) second object in the close than in the far condition. Analyses of the agreement errors yielded an attraction effect, with more errors in mismatching than in matching conditions. However, the magnitude of the attraction effect did not differ across the close and far conditions. Thus, spatial proximity encouraged parallel processing of the pictures, which led to interference of the associated conceptual and/or lexical representation, but, contrary to the prediction, it did not lead to more attraction errors. Copyright © 2015 Elsevier B.V. All rights reserved.

Financial time series prediction using spiking neural networks.

PubMed

Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

2014-01-01

In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

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.

Study of 1-min rain rate integration statistic in South Korea

NASA Astrophysics Data System (ADS)

Shrestha, Sujan; Choi, Dong-You

2017-03-01

The design of millimeter wave communication links and the study of propagation impairments at higher frequencies due to a hydrometeor, particularly rain, require the knowledge of 1-min. rainfall rate data. Signal attenuation in space communication results are due to absorption and scattering of radio wave energy. Radio wave attenuation due to rain depends on the relevance of a 1-min. integration time for the rain rate. However, in practice, securing these data over a wide range of areas is difficult. Long term precipitation data are readily available. However, there is a need for a 1-min. rainfall rate in the rain attenuation prediction models for a better estimation of the attenuation. In this paper, we classify and survey the prominent 1-min. rain rate models. Regression analysis was performed for the study of cumulative rainfall data measured experimentally for a decade in nine different regions in South Korea, with 93 different locations, using the experimental 1-min. rainfall accumulation. To visualize the 1-min. rainfall rate applicable for the whole region for 0.01% of the time, we have considered the variation in the rain rate for 40 stations across South Korea. The Kriging interpolation method was used for spatial interpolation of the rain rate values for 0.01% of the time into a regular grid to obtain a highly consistent and predictable rainfall variation. The rain rate exceeded the 1-min. interval that was measured through the rain gauge compared to the rainfall data estimated using the International Telecommunication Union Radio Communication Sector model (ITU-R P.837-6) along with the empirical methods as Segal, Burgueno et al., Chebil and Rahman, logarithmic, exponential and global coefficients, second and third order polynomial fits, and Model 1 for Icheon regions under the regional and average coefficient set. The ITU-R P. 837-6 exhibits a lower relative error percentage of 3.32% and 12.59% in the 5- and 10-min. to 1-min. conversion, whereas the higher error percentages of 24.64%, 46.44% and 58.46% for the 20-, 30- and 60-min. to 1-min., conversion were obtained in the Icheon region. The available experimental rainfall data were sampled on equiprobable rain-rate values where the application of these models to experimentally obtained data exhibits a variable error rate. This paper aims to provide a better survey of various conversion methods to model a 1-min. rain rate applicable to the South Korea regions with a suitable contour plot at 0.01% of the time.

Optical phase-locked loop (OPLL) for free-space laser communications with heterodyne detection

NASA Technical Reports Server (NTRS)

Win, Moe Z.; Chen, Chien-Chung; Scholtz, Robert A.

1991-01-01

Several advantages of coherent free-space optical communications are outlined. Theoretical analysis is formulated for an OPLL disturbed by shot noise, modulation noise, and frequency noise consisting of a white component, a 1/f component, and a 1/f-squared component. Each of the noise components is characterized by its associated power spectral density. It is shown that the effect of modulation depends only on the ratio of loop bandwidth and data rate, and is negligible for an OPLL with loop bandwidth smaller than one fourth the data rate. Total phase error variance as a function of loop bandwidth is displayed for several values of carrier signal to noise ratio. Optimal loop bandwidth is also calculated as a function of carrier signal to noise ratio. An OPLL experiment is performed, where it is shown that the measured phase error variance closely matches the theoretical predictions.

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.

Deducing the Kinetics of Protein Synthesis In Vivo from the Transition Rates Measured In Vitro

PubMed Central

Rudorf, Sophia; Thommen, Michael; Rodnina, Marina V.; Lipowsky, Reinhard

2014-01-01

The molecular machinery of life relies on complex multistep processes that involve numerous individual transitions, such as molecular association and dissociation steps, chemical reactions, and mechanical movements. The corresponding transition rates can be typically measured in vitro but not in vivo. Here, we develop a general method to deduce the in-vivo rates from their in-vitro values. The method has two basic components. First, we introduce the kinetic distance, a new concept by which we can quantitatively compare the kinetics of a multistep process in different environments. The kinetic distance depends logarithmically on the transition rates and can be interpreted in terms of the underlying free energy barriers. Second, we minimize the kinetic distance between the in-vitro and the in-vivo process, imposing the constraint that the deduced rates reproduce a known global property such as the overall in-vivo speed. In order to demonstrate the predictive power of our method, we apply it to protein synthesis by ribosomes, a key process of gene expression. We describe the latter process by a codon-specific Markov model with three reaction pathways, corresponding to the initial binding of cognate, near-cognate, and non-cognate tRNA, for which we determine all individual transition rates in vitro. We then predict the in-vivo rates by the constrained minimization procedure and validate these rates by three independent sets of in-vivo data, obtained for codon-dependent translation speeds, codon-specific translation dynamics, and missense error frequencies. In all cases, we find good agreement between theory and experiment without adjusting any fit parameter. The deduced in-vivo rates lead to smaller error frequencies than the known in-vitro rates, primarily by an improved initial selection of tRNA. The method introduced here is relatively simple from a computational point of view and can be applied to any biomolecular process, for which we have detailed information about the in-vitro kinetics. PMID:25358034

General Solution for Theoretical Packet Data Loss Rate

NASA Technical Reports Server (NTRS)

Lansdowne, Chatwin; Schlesinger, Adam

2006-01-01

Communications systems which transfer blocks ("frames") of data must use a marker ("frame synchronization pattern") for identifying where a block begins. A technique ("frame synchronization strategy") is used to locate the start of each frame and maintain synchronization as additional blocks are processed. A device which strips out the frame synchronization pattern [FSP] and provides an "end of frame" pulse is called a frame synchronizer. As clock and data errors are introduced into the system, the start-of-block marker becomes displaced and/or corrupted. The capability of the frame synchronizer to stay locked to the pattern under these conditions is a figure of merit for the frame synchronization strategy. It is important to select a strategy which will stay locked nearly all the time at bit error rates where the data is usable. ("Bit error rate" [BER] is the fraction of binary bits which are inverted by passage through a communication system.) The fraction of frames that are discarded because the frame synchronizer is not locked is called "Percent Data Loss" or "Packet Data Loss rate" [PDL]. A general approach for accurately predicting PDL given BER was developed in Theoretical Percent Data Loss Calculation and Measurement Accuracy, T. P. Kelly, LESC-30554, December 1992. Kelly gave a solution in terms of matrix equations, and only addressed "level" channel encoding. This paper goes on to give a closed-form polynomial solution for the most common class of frame synchronizer strategies, and will also address "mark" and "space" (differential) channel encoding, and burst error environments. The paper is divided into four sections and follows a logically ordered presentation, with results developed before they are evaluated. However, most readers will derive the greatest benefit from this paper by treating the results as reference material. The result developed for differential encoding can be extended to other applications (like block codes) where the probability is needed that a block contains only a certain number of errors.

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.

Essays in applied macroeconomics: Asymmetric price adjustment, exchange rate and treatment effect

NASA Astrophysics Data System (ADS)

Gu, Jingping

This dissertation consists of three essays. Chapter II examines the possible asymmetric response of gasoline prices to crude oil price changes using an error correction model with GARCH errors. Recent papers have looked at this issue. Some of these papers estimate a form of error correction model, but none of them accounts for autoregressive heteroskedasticity in estimation and testing for asymmetry and none of them takes the response of crude oil price into consideration. We find that time-varying volatility of gasoline price disturbances is an important feature of the data, and when we allow for asymmetric GARCH errors and investigate the system wide impulse response function, we find evidence of asymmetric adjustment to crude oil price changes in weekly retail gasoline prices. Chapter III discusses the relationship between fiscal deficit and exchange rate. Economic theory predicts that fiscal deficits can significantly affect real exchange rate movements, but existing empirical evidence reports only a weak impact of fiscal deficits on exchange rates. Based on US dollar-based real exchange rates in G5 countries and a flexible varying coefficient model, we show that the previously documented weak relationship between fiscal deficits and exchange rates may be the result of additive specifications, and that the relationship is stronger if we allow fiscal deficits to impact real exchange rates non-additively as well as nonlinearly. We find that the speed of exchange rate adjustment toward equilibrium depends on the state of the fiscal deficit; a fiscal contraction in the US can lead to less persistence in the deviation of exchange rates from fundamentals, and faster mean reversion to the equilibrium. Chapter IV proposes a kernel method to deal with the nonparametric regression model with only discrete covariates as regressors. This new approach is based on recently developed least squares cross-validation kernel smoothing method. It can not only automatically smooth the irrelevant variables out of the nonparametric regression model, but also avoid the problem of loss of efficiency related to the traditional nonparametric frequency-based method and the problem of misspecification based on parametric model.

Integrated Model for Performance Analysis of All-Optical Multihop Packet Switches

NASA Astrophysics Data System (ADS)

Jeong, Han-You; Seo, Seung-Woo

2000-09-01

The overall performance of an all-optical packet switching system is usually determined by two criteria, i.e., switching latency and packet loss rate. In some real-time applications, however, in which packets arriving later than a timeout period are discarded as loss, the packet loss rate becomes the most dominant criterion for system performance. Here we focus on evaluating the performance of all-optical packet switches in terms of the packet loss rate, which normally arises from the insufficient hardware or the degradation of an optical signal. Considering both aspects, we propose what we believe is a new analysis model for the packet loss rate that reflects the complicated interactions between physical impairments and system-level parameters. On the basis of the estimation model for signal quality degradation in a multihop path we construct an equivalent analysis model of a switching network for evaluating an average bit error rate. With the model constructed we then propose an integrated model for estimating the packet loss rate in three architectural examples of multihop packet switches, each of which is based on a different switching concept. We also derive the bounds on the packet loss rate induced by bit errors. Finally, it is verified through simulation studies that our analysis model accurately predicts system performance.

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.

Simultaneous Control of Error Rates in fMRI Data Analysis

PubMed Central

Kang, Hakmook; Blume, Jeffrey; Ombao, Hernando; Badre, David

2015-01-01

The key idea of statistical hypothesis testing is to fix, and thereby control, the Type I error (false positive) rate across samples of any size. Multiple comparisons inflate the global (family-wise) Type I error rate and the traditional solution to maintaining control of the error rate is to increase the local (comparison-wise) Type II error (false negative) rates. However, in the analysis of human brain imaging data, the number of comparisons is so large that this solution breaks down: the local Type II error rate ends up being so large that scientifically meaningful analysis is precluded. Here we propose a novel solution to this problem: allow the Type I error rate to converge to zero along with the Type II error rate. It works because when the Type I error rate per comparison is very small, the accumulation (or global) Type I error rate is also small. This solution is achieved by employing the Likelihood paradigm, which uses likelihood ratios to measure the strength of evidence on a voxel-by-voxel basis. In this paper, we provide theoretical and empirical justification for a likelihood approach to the analysis of human brain imaging data. In addition, we present extensive simulations that show the likelihood approach is viable, leading to ‘cleaner’ looking brain maps and operationally superiority (lower average error rate). Finally, we include a case study on cognitive control related activation in the prefrontal cortex of the human brain. PMID:26272730

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.

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.

Modeling habitat dynamics accounting for possible misclassification

USGS Publications Warehouse

Veran, Sophie; Kleiner, Kevin J.; Choquet, Remi; Collazo, Jaime; Nichols, James D.

2012-01-01

Land cover data are widely used in ecology as land cover change is a major component of changes affecting ecological systems. Landscape change estimates are characterized by classification errors. Researchers have used error matrices to adjust estimates of areal extent, but estimation of land cover change is more difficult and more challenging, with error in classification being confused with change. We modeled land cover dynamics for a discrete set of habitat states. The approach accounts for state uncertainty to produce unbiased estimates of habitat transition probabilities using ground information to inform error rates. We consider the case when true and observed habitat states are available for the same geographic unit (pixel) and when true and observed states are obtained at one level of resolution, but transition probabilities estimated at a different level of resolution (aggregations of pixels). Simulation results showed a strong bias when estimating transition probabilities if misclassification was not accounted for. Scaling-up does not necessarily decrease the bias and can even increase it. Analyses of land cover data in the Southeast region of the USA showed that land change patterns appeared distorted if misclassification was not accounted for: rate of habitat turnover was artificially increased and habitat composition appeared more homogeneous. Not properly accounting for land cover misclassification can produce misleading inferences about habitat state and dynamics and also misleading predictions about species distributions based on habitat. Our models that explicitly account for state uncertainty should be useful in obtaining more accurate inferences about change from data that include errors.

Does Working Memory Enhance or Interfere with Speech Fluency in Adults Who Do and Do Not Stutter? Evidence from a Dual-Task Paradigm

ERIC Educational Resources Information Center

Eichorn, Naomi; Marton, Klara; Schwartz, Richard G.; Melara, Robert D.; Pirutinsky, Steven

2016-01-01

Purpose: The present study examined whether engaging working memory in a secondary task benefits speech fluency. Effects of dual-task conditions on speech fluency, rate, and errors were examined with respect to predictions derived from three related theoretical accounts of disfluencies. Method: Nineteen adults who stutter and twenty adults who do…

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

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.

An improved procedure for the validation of satellite-based precipitation estimates

NASA Astrophysics Data System (ADS)

Tang, Ling; Tian, Yudong; Yan, Fang; Habib, Emad

2015-09-01

The objective of this study is to propose and test a new procedure to improve the validation of remote-sensing, high-resolution precipitation estimates. Our recent studies show that many conventional validation measures do not accurately capture the unique error characteristics in precipitation estimates to better inform both data producers and users. The proposed new validation procedure has two steps: 1) an error decomposition approach to separate the total retrieval error into three independent components: hit error, false precipitation and missed precipitation; and 2) the hit error is further analyzed based on a multiplicative error model. In the multiplicative error model, the error features are captured by three model parameters. In this way, the multiplicative error model separates systematic and random errors, leading to more accurate quantification of the uncertainties. The proposed procedure is used to quantitatively evaluate the recent two versions (Version 6 and 7) of TRMM's Multi-sensor Precipitation Analysis (TMPA) real-time and research product suite (3B42 and 3B42RT) for seven years (2005-2011) over the continental United States (CONUS). The gauge-based National Centers for Environmental Prediction (NCEP) Climate Prediction Center (CPC) near-real-time daily precipitation analysis is used as the reference. In addition, the radar-based NCEP Stage IV precipitation data are also model-fitted to verify the effectiveness of the multiplicative error model. The results show that winter total bias is dominated by the missed precipitation over the west coastal areas and the Rocky Mountains, and the false precipitation over large areas in Midwest. The summer total bias is largely coming from the hit bias in Central US. Meanwhile, the new version (V7) tends to produce more rainfall in the higher rain rates, which moderates the significant underestimation exhibited in the previous V6 products. Moreover, the error analysis from the multiplicative error model provides a clear and concise picture of the systematic and random errors, with both versions of 3B42RT have higher errors in varying degrees than their research (post-real-time) counterparts. The new V7 algorithm shows obvious improvements in reducing random errors in both winter and summer seasons, compared to its predecessors V6. Stage IV, as expected, surpasses the satellite-based datasets in all the metrics over CONUS. Based on the results, we recommend the new procedure be adopted for routine validation of satellite-based precipitation datasets, and we expect the procedure will work effectively for higher resolution data to be produced in the Global Precipitation Measurement (GPM) era.

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

DOE PAGES

Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

2017-07-14

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed bymore » simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.« less

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

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

Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed bymore » simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.« less

Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

PubMed

Jafari, Seyed Ali; Jahandideh, Sepideh; Jahandideh, Mina; Asadabadi, Ebrahim Barzegari

2015-01-01

Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

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.

Disrupted Prediction Error Links Excessive Amygdala Activation to Excessive Fear.

PubMed

Sengupta, Auntora; Winters, Bryony; Bagley, Elena E; McNally, Gavan P

2016-01-13

Basolateral amygdala (BLA) is critical for fear learning, and its heightened activation is widely thought to underpin a variety of anxiety disorders. Here we used chemogenetic techniques in rats to study the consequences of heightened BLA activation for fear learning and memory, and to specifically identify a mechanism linking increased activity of BLA glutamatergic neurons to aberrant fear. We expressed the excitatory hM3Dq DREADD in rat BLA glutamatergic neurons and showed that CNO acted selectively to increase their activity, depolarizing these neurons and increasing their firing rates. This chemogenetic excitation of BLA glutamatergic neurons had no effect on the acquisition of simple fear learning, regardless of whether this learning led to a weak or strong fear memory. However, in an associative blocking task, chemogenetic excitation of BLA glutamatergic neurons yielded significant learning to a blocked conditioned stimulus, which otherwise should not have been learned about. Moreover, in an overexpectation task, chemogenetic manipulation of BLA glutamatergic neurons prevented use of negative prediction error to reduce fear learning, leading to significant impairments in fear inhibition. These effects were not attributable to the chemogenetic manipulation enhancing arousal, increasing asymptotic levels of fear learning or fear memory consolidation. Instead, chemogenetic excitation of BLA glutamatergic neurons disrupted use of prediction error to regulate fear learning. Several neuropsychiatric disorders are characterized by heightened activation of the amygdala. This heightened activation has been hypothesized to underlie increased emotional reactivity, fear over generalization, and deficits in fear inhibition. Yet the mechanisms linking heightened amygdala activation to heightened emotional learning are elusive. Here we combined chemogenetic excitation of rat basolateral amygdala glutamatergic neurons with a variety of behavioral approaches to show that, although simple fear learning is unaffected, the use of prediction error to regulate this learning is profoundly disrupted, leading to formation of inappropriate fear associations and impaired fear inhibition. Copyright © 2016 the authors 0270-6474/16/360385-11$15.00/0.

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...

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.

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.

Feasibility of the capnogram to monitor ventilation rate during cardiopulmonary resuscitation.

PubMed

Aramendi, Elisabete; Elola, Andoni; Alonso, Erik; Irusta, Unai; Daya, Mohamud; Russell, James K; Hubner, Pia; Sterz, Fritz

2017-01-01

The rates of chest compressions (CCs) and ventilations are both important metrics to monitor the quality of cardiopulmonary resuscitation (CPR). Capnography permits monitoring ventilation, but the CCs provided during CPR corrupt the capnogram and compromise the accuracy of automatic ventilation detectors. The aim of this study was to evaluate the feasibility of an automatic algorithm based on the capnogram to detect ventilations and provide feedback on ventilation rate during CPR, specifically addressing intervals where CCs are delivered. The dataset used to develop and test the algorithm contained in-hospital and out-of-hospital cardiac arrest episodes. The method relies on adaptive thresholding to detect ventilations in the first derivative of the capnogram. The performance of the detector was reported in terms of sensitivity (SE) and Positive Predictive Value (PPV). The overall performance was reported in terms of the rate error and errors in the hyperventilation alarms. Results were given separately for the intervals with CCs. A total of 83 episodes were considered, resulting in 4880min and 46,740 ventilations (8741 during CCs). The method showed an overall SE/PPV above 99% and 97% respectively, even in intervals with CCs. The error for the ventilation rate was below 1.8min -1 in any group, and >99% of the ventilation alarms were correctly detected. A method to provide accurate feedback on ventilation rate using only the capnogram is proposed. Its accuracy was proven even in intervals where canpography signal was severely corrupted by CCs. This algorithm could be integrated into monitor/defibrillators to provide reliable feedback on ventilation rate during CPR. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Earthquake prediction analysis based on empirical seismic rate: the M8 algorithm

NASA Astrophysics Data System (ADS)

Molchan, G.; Romashkova, L.

2010-12-01

The quality of space-time earthquake prediction is usually characterized by a 2-D error diagram (n, τ), where n is the fraction of failures-to-predict and τ is the local rate of alarm averaged in space. The most reasonable averaging measure for analysis of a prediction strategy is the normalized rate of target events λ(dg) in a subarea dg. In that case the quantity H = 1 - (n + τ) determines the prediction capability of the strategy. The uncertainty of λ(dg) causes difficulties in estimating H and the statistical significance, α, of prediction results. We investigate this problem theoretically and show how the uncertainty of the measure can be taken into account in two situations, viz., the estimation of α and the construction of a confidence zone for the (n, τ)-parameters of the random strategies. We use our approach to analyse the results from prediction of M >= 8.0 events by the M8 method for the period 1985-2009 (the M8.0+ test). The model of λ(dg) based on the events Mw >= 5.5, 1977-2004, and the magnitude range of target events 8.0 <= M < 8.5 are considered as basic to this M8 analysis. We find the point and upper estimates of α and show that they are still unstable because the number of target events in the experiment is small. However, our results argue in favour of non-triviality of the M8 prediction algorithm.

Ensemble of classifiers for confidence-rated classification of NDE signal

NASA Astrophysics Data System (ADS)

Banerjee, Portia; Safdarnejad, Seyed; Udpa, Lalita; Udpa, Satish

2016-02-01

Ensemble of classifiers in general, aims to improve classification accuracy by combining results from multiple weak hypotheses into a single strong classifier through weighted majority voting. Improved versions of ensemble of classifiers generate self-rated confidence scores which estimate the reliability of each of its prediction and boost the classifier using these confidence-rated predictions. However, such a confidence metric is based only on the rate of correct classification. In existing works, although ensemble of classifiers has been widely used in computational intelligence, the effect of all factors of unreliability on the confidence of classification is highly overlooked. With relevance to NDE, classification results are affected by inherent ambiguity of classifica-tion, non-discriminative features, inadequate training samples and noise due to measurement. In this paper, we extend the existing ensemble classification by maximizing confidence of every classification decision in addition to minimizing the classification error. Initial results of the approach on data from eddy current inspection show improvement in classification performance of defect and non-defect indications.

DCT-based iris recognition.

PubMed

Monro, Donald M; Rakshit, Soumyadip; Zhang, Dexin

2007-04-01

This paper presents a novel iris coding method based on differences of discrete cosine transform (DCT) coefficients of overlapped angular patches from normalized iris images. The feature extraction capabilities of the DCT are optimized on the two largest publicly available iris image data sets, 2,156 images of 308 eyes from the CASIA database and 2,955 images of 150 eyes from the Bath database. On this data, we achieve 100 percent Correct Recognition Rate (CRR) and perfect Receiver-Operating Characteristic (ROC) Curves with no registered false accepts or rejects. Individual feature bit and patch position parameters are optimized for matching through a product-of-sum approach to Hamming distance calculation. For verification, a variable threshold is applied to the distance metric and the False Acceptance Rate (FAR) and False Rejection Rate (FRR) are recorded. A new worst-case metric is proposed for predicting practical system performance in the absence of matching failures, and the worst case theoretical Equal Error Rate (EER) is predicted to be as low as 2.59 x 10(-4) on the available data sets.

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.

Uncertainty quantification and propagation in dynamic models using ambient vibration measurements, application to a 10-story building

NASA Astrophysics Data System (ADS)

Behmanesh, Iman; Yousefianmoghadam, Seyedsina; Nozari, Amin; Moaveni, Babak; Stavridis, Andreas

2018-07-01

This paper investigates the application of Hierarchical Bayesian model updating for uncertainty quantification and response prediction of civil structures. In this updating framework, structural parameters of an initial finite element (FE) model (e.g., stiffness or mass) are calibrated by minimizing error functions between the identified modal parameters and the corresponding parameters of the model. These error functions are assumed to have Gaussian probability distributions with unknown parameters to be determined. The estimated parameters of error functions represent the uncertainty of the calibrated model in predicting building's response (modal parameters here). The focus of this paper is to answer whether the quantified model uncertainties using dynamic measurement at building's reference/calibration state can be used to improve the model prediction accuracies at a different structural state, e.g., damaged structure. Also, the effects of prediction error bias on the uncertainty of the predicted values is studied. The test structure considered here is a ten-story concrete building located in Utica, NY. The modal parameters of the building at its reference state are identified from ambient vibration data and used to calibrate parameters of the initial FE model as well as the error functions. Before demolishing the building, six of its exterior walls were removed and ambient vibration measurements were also collected from the structure after the wall removal. These data are not used to calibrate the model; they are only used to assess the predicted results. The model updating framework proposed in this paper is applied to estimate the modal parameters of the building at its reference state as well as two damaged states: moderate damage (removal of four walls) and severe damage (removal of six walls). Good agreement is observed between the model-predicted modal parameters and those identified from vibration tests. Moreover, it is shown that including prediction error bias in the updating process instead of commonly-used zero-mean error function can significantly reduce the prediction uncertainties.

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.

A cascaded coding scheme for error control and its performance analysis

NASA Technical Reports Server (NTRS)

Lin, Shu; Kasami, Tadao; Fujiwara, Tohru; Takata, Toyoo

1986-01-01

A coding scheme is investigated for error control in data communication systems. The scheme is obtained by cascading two error correcting codes, called the inner and outer codes. The error performance of the scheme is analyzed for a binary symmetric channel with bit error rate epsilon <1/2. It is shown that if the inner and outer codes are chosen properly, extremely high reliability can be attained even for a high channel bit error rate. Various specific example schemes with inner codes ranging form high rates to very low rates and Reed-Solomon codes as inner codes are considered, and their error probabilities are evaluated. They all provide extremely high reliability even for very high bit error rates. Several example schemes are being considered by NASA for satellite and spacecraft down link error control.

Identification of Matra Region and Overlapping Characters for OCR of Printed Bengali Scripts

NASA Astrophysics Data System (ADS)

Goswami, Subhra Sundar

One of the important reasons for poor recognition rate in optical character recognition (OCR) system is the error in character segmentation. In case of Bangla scripts, the errors occur due to several reasons, which include incorrect detection of matra (headline), over-segmentation and under-segmentation. We have proposed a robust method for detecting the headline region. Existence of overlapping characters (in under-segmented parts) in scanned printed documents is a major problem in designing an effective character segmentation procedure for OCR systems. In this paper, a predictive algorithm is developed for effectively identifying overlapping characters and then selecting the cut-borders for segmentation. Our method can be successfully used in achieving high recognition result.

How Many Alternatives Can Be Ranked? A Comparison of the Paired Comparison and Ranking Methods.

PubMed

Ock, Minsu; Yi, Nari; Ahn, Jeonghoon; Jo, Min-Woo

2016-01-01

To determine the feasibility of converting ranking data into paired comparison (PC) data and suggest the number of alternatives that can be ranked by comparing a PC and a ranking method. Using a total of 222 health states, a household survey was conducted in a sample of 300 individuals from the general population. Each respondent performed a PC 15 times and a ranking method 6 times (two attempts of ranking three, four, and five health states, respectively). The health states of the PC and the ranking method were constructed to overlap each other. We converted the ranked data into PC data and examined the consistency of the response rate. Applying probit regression, we obtained the predicted probability of each method. Pearson correlation coefficients were determined between the predicted probabilities of those methods. The mean absolute error was also assessed between the observed and the predicted values. The overall consistency of the response rate was 82.8%. The Pearson correlation coefficients were 0.789, 0.852, and 0.893 for ranking three, four, and five health states, respectively. The lowest mean absolute error was 0.082 (95% confidence interval [CI] 0.074-0.090) in ranking five health states, followed by 0.123 (95% CI 0.111-0.135) in ranking four health states and 0.126 (95% CI 0.113-0.138) in ranking three health states. After empirically examining the consistency of the response rate between a PC and a ranking method, we suggest that using five alternatives in the ranking method may be superior to using three or four alternatives. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Evaluation of three statistical prediction models for forensic age prediction based on DNA methylation.

PubMed

Smeers, Inge; Decorte, Ronny; Van de Voorde, Wim; Bekaert, Bram

2018-05-01

DNA methylation is a promising biomarker for forensic age prediction. A challenge that has emerged in recent studies is the fact that prediction errors become larger with increasing age due to interindividual differences in epigenetic ageing rates. This phenomenon of non-constant variance or heteroscedasticity violates an assumption of the often used method of ordinary least squares (OLS) regression. The aim of this study was to evaluate alternative statistical methods that do take heteroscedasticity into account in order to provide more accurate, age-dependent prediction intervals. A weighted least squares (WLS) regression is proposed as well as a quantile regression model. Their performances were compared against an OLS regression model based on the same dataset. Both models provided age-dependent prediction intervals which account for the increasing variance with age, but WLS regression performed better in terms of success rate in the current dataset. However, quantile regression might be a preferred method when dealing with a variance that is not only non-constant, but also not normally distributed. Ultimately the choice of which model to use should depend on the observed characteristics of the data. Copyright © 2018 Elsevier B.V. All rights reserved.

Reinforcement Learning Models and Their Neural Correlates: An Activation Likelihood Estimation Meta-Analysis

PubMed Central

Kumar, Poornima; Eickhoff, Simon B.; Dombrovski, Alexandre Y.

2015-01-01

Reinforcement learning describes motivated behavior in terms of two abstract signals. The representation of discrepancies between expected and actual rewards/punishments – prediction error – is thought to update the expected value of actions and predictive stimuli. Electrophysiological and lesion studies suggest that mesostriatal prediction error signals control behavior through synaptic modification of cortico-striato-thalamic networks. Signals in the ventromedial prefrontal and orbitofrontal cortex are implicated in representing expected value. To obtain unbiased maps of these representations in the human brain, we performed a meta-analysis of functional magnetic resonance imaging studies that employed algorithmic reinforcement learning models, across a variety of experimental paradigms. We found that the ventral striatum (medial and lateral) and midbrain/thalamus represented reward prediction errors, consistent with animal studies. Prediction error signals were also seen in the frontal operculum/insula, particularly for social rewards. In Pavlovian studies, striatal prediction error signals extended into the amygdala, while instrumental tasks engaged the caudate. Prediction error maps were sensitive to the model-fitting procedure (fixed or individually-estimated) and to the extent of spatial smoothing. A correlate of expected value was found in a posterior region of the ventromedial prefrontal cortex, caudal and medial to the orbitofrontal regions identified in animal studies. These findings highlight a reproducible motif of reinforcement learning in the cortico-striatal loops and identify methodological dimensions that may influence the reproducibility of activation patterns across studies. PMID:25665667

Reward positivity: Reward prediction error or salience prediction error?

PubMed

Heydari, Sepideh; Holroyd, Clay B

2016-08-01

The reward positivity is a component of the human ERP elicited by feedback stimuli in trial-and-error learning and guessing tasks. A prominent theory holds that the reward positivity reflects a reward prediction error signal that is sensitive to outcome valence, being larger for unexpected positive events relative to unexpected negative events (Holroyd & Coles, 2002). Although the theory has found substantial empirical support, most of these studies have utilized either monetary or performance feedback to test the hypothesis. However, in apparent contradiction to the theory, a recent study found that unexpected physical punishments also elicit the reward positivity (Talmi, Atkinson, & El-Deredy, 2013). The authors of this report argued that the reward positivity reflects a salience prediction error rather than a reward prediction error. To investigate this finding further, in the present study participants navigated a virtual T maze and received feedback on each trial under two conditions. In a reward condition, the feedback indicated that they would either receive a monetary reward or not and in a punishment condition the feedback indicated that they would receive a small shock or not. We found that the feedback stimuli elicited a typical reward positivity in the reward condition and an apparently delayed reward positivity in the punishment condition. Importantly, this signal was more positive to the stimuli that predicted the omission of a possible punishment relative to stimuli that predicted a forthcoming punishment, which is inconsistent with the salience hypothesis. © 2016 Society for Psychophysiological Research.

Accuracy of Robotic Radiosurgical Liver Treatment Throughout the Respiratory Cycle

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

Winter, Jeff D.; Wong, Raimond; Swaminath, Anand

Purpose: To quantify random uncertainties in robotic radiosurgical treatment of liver lesions with real-time respiratory motion management. Methods and Materials: We conducted a retrospective analysis of 27 liver cancer patients treated with robotic radiosurgery over 118 fractions. The robotic radiosurgical system uses orthogonal x-ray images to determine internal target position and correlates this position with an external surrogate to provide robotic corrections of linear accelerator positioning. Verification and update of this internal–external correlation model was achieved using periodic x-ray images collected throughout treatment. To quantify random uncertainties in targeting, we analyzed logged tracking information and isolated x-ray images collected immediately beforemore » beam delivery. For translational correlation errors, we quantified the difference between correlation model–estimated target position and actual position determined by periodic x-ray imaging. To quantify prediction errors, we computed the mean absolute difference between the predicted coordinates and actual modeled position calculated 115 milliseconds later. We estimated overall random uncertainty by quadratically summing correlation, prediction, and end-to-end targeting errors. We also investigated relationships between tracking errors and motion amplitude using linear regression. Results: The 95th percentile absolute correlation errors in each direction were 2.1 mm left–right, 1.8 mm anterior–posterior, 3.3 mm cranio–caudal, and 3.9 mm 3-dimensional radial, whereas 95th percentile absolute radial prediction errors were 0.5 mm. Overall 95th percentile random uncertainty was 4 mm in the radial direction. Prediction errors were strongly correlated with modeled target amplitude (r=0.53-0.66, P<.001), whereas only weak correlations existed for correlation errors. Conclusions: Study results demonstrate that model correlation errors are the primary random source of uncertainty in Cyberknife liver treatment and, unlike prediction errors, are not strongly correlated with target motion amplitude. Aggregate 3-dimensional radial position errors presented here suggest the target will be within 4 mm of the target volume for 95% of the beam delivery.« less

Predictability of Solar Radiation for Photovoltaics systems over Europe: from short-term to seasonal time-scales

NASA Astrophysics Data System (ADS)

De Felice, Matteo; Petitta, Marcello; Ruti, Paolo

2014-05-01

Photovoltaic diffusion is steadily growing on Europe, passing from a capacity of almost 14 GWp in 2011 to 21.5 GWp in 2012 [1]. Having accurate forecast is needed for planning and operational purposes, with the possibility to model and predict solar variability at different time-scales. This study examines the predictability of daily surface solar radiation comparing ECMWF operational forecasts with CM-SAF satellite measurements on the Meteosat (MSG) full disk domain. Operational forecasts used are the IFS system up to 10 days and the System4 seasonal forecast up to three months. Forecast are analysed considering average and variance of errors, showing error maps and average on specific domains with respect to prediction lead times. In all the cases, forecasts are compared with predictions obtained using persistence and state-of-art time-series models. We can observe a wide range of errors, with the performance of forecasts dramatically affected by orography and season. Lower errors are on southern Italy and Spain, with errors on some areas consistently under 10% up to ten days during summer (JJA). Finally, we conclude the study with some insight on how to "translate" the error on solar radiation to error on solar power production using available production data from solar power plants. [1] EurObserver, "Baromètre Photovoltaïque, Le journal des énergies renouvables, April 2012."

Extreme learning machine: a new alternative for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters.

PubMed

Liu, Zhijian; Li, Hao; Tang, Xindong; Zhang, Xinyu; Lin, Fan; Cheng, Kewei

2016-01-01

Heat collection rate and heat loss coefficient are crucial indicators for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, wasting too much time and manpower. To address this problem, we previously used artificial neural networks and support vector machine to develop precise knowledge-based models for predicting the heat collection rates and heat loss coefficients of water-in-glass evacuated tube solar water heaters, setting the properties measured by "portable test instruments" as the independent variables. A robust software for determination was also developed. However, in previous results, the prediction accuracy of heat loss coefficients can still be improved compared to those of heat collection rates. Also, in practical applications, even a small reduction in root mean square errors (RMSEs) can sometimes significantly improve the evaluation and business processes. As a further study, in this short report, we show that using a novel and fast machine learning algorithm-extreme learning machine can generate better predicted results for heat loss coefficient, which reduces the average RMSEs to 0.67 in testing.

QSPR prediction of the hydroxyl radical rate constant of water contaminants.

PubMed

Borhani, Tohid Nejad Ghaffar; Saniedanesh, Mohammadhossein; Bagheri, Mehdi; Lim, Jeng Shiun

2016-07-01

In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Assessing explicit error reporting in the narrative electronic medical record using keyword searching.

PubMed

Cao, Hui; Stetson, Peter; Hripcsak, George

2003-01-01

Many types of medical errors occur in and outside of hospitals, some of which have very serious consequences and increase cost. Identifying errors is a critical step for managing and preventing them. In this study, we assessed the explicit reporting of medical errors in the electronic record. We used five search terms "mistake," "error," "incorrect," "inadvertent," and "iatrogenic" to survey several sets of narrative reports including discharge summaries, sign-out notes, and outpatient notes from 1991 to 2000. We manually reviewed all the positive cases and identified them based on the reporting of physicians. We identified 222 explicitly reported medical errors. The positive predictive value varied with different keywords. In general, the positive predictive value for each keyword was low, ranging from 3.4 to 24.4%. Therapeutic-related errors were the most common reported errors and these reported therapeutic-related errors were mainly medication errors. Keyword searches combined with manual review indicated some medical errors that were reported in medical records. It had a low sensitivity and a moderate positive predictive value, which varied by search term. Physicians were most likely to record errors in the Hospital Course and History of Present Illness sections of discharge summaries. The reported errors in medical records covered a broad range and were related to several types of care providers as well as non-health care professionals.

Adjustment of regional regression models of urban-runoff quality using data for Chattanooga, Knoxville, and Nashville, Tennessee

USGS Publications Warehouse

Hoos, Anne B.; Patel, Anant R.

1996-01-01

Model-adjustment procedures were applied to the combined data bases of storm-runoff quality for Chattanooga, Knoxville, and Nashville, Tennessee, to improve predictive accuracy for storm-runoff quality for urban watersheds in these three cities and throughout Middle and East Tennessee. Data for 45 storms at 15 different sites (five sites in each city) constitute the data base. Comparison of observed values of storm-runoff load and event-mean concentration to the predicted values from the regional regression models for 10 constituents shows prediction errors, as large as 806,000 percent. Model-adjustment procedures, which combine the regional model predictions with local data, are applied to improve predictive accuracy. Standard error of estimate after model adjustment ranges from 67 to 322 percent. Calibration results may be biased due to sampling error in the Tennessee data base. The relatively large values of standard error of estimate for some of the constituent models, although representing significant reduction (at least 50 percent) in prediction error compared to estimation with unadjusted regional models, may be unacceptable for some applications. The user may wish to collect additional local data for these constituents and repeat the analysis, or calibrate an independent local regression model.

A method for the in vivo measurement of americium-241 at long times post-exposure

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

Neton, J.W.

1988-01-01

This study investigated an improved method for the quantitative measurement, calibration and calculation of {sup 241}Am organ burdens in humans. The techniques developed correct for cross-talk or count-rate contributions from surrounding and adjacent organ burdens and assures for the proper assignment of activity to the lungs, liver and skeleton. In order to predict the net count-rates for the measurement geometries of the skull, liver and lung, a background prediction method was developed. This method utilizes data obtained from the measurement of a group of control subjects. Based on this data, a linear prediction equation was developed for each measurement geometry.more » In order to correct for the cross-contributions among the various deposition loci, a series of surrogate human phantom structures were measured. The results of measurements of {sup 241}Am depositions in six exposure cases have been evaluated using these new techniques and have indicated that lung burden estimates could be in error by as much as 100 percent when corrections are not made for contributions to the count-rate from other organs.« less

Predictive accuracy of a ground-water model--Lessons from a postaudit

USGS Publications Warehouse

Konikow, Leonard F.

1986-01-01

Hydrogeologic studies commonly include the development, calibration, and application of a deterministic simulation model. To help assess the value of using such models to make predictions, a postaudit was conducted on a previously studied area in the Salt River and lower Santa Cruz River basins in central Arizona. A deterministic, distributed-parameter model of the ground-water system in these alluvial basins was calibrated by Anderson (1968) using about 40 years of data (1923–64). The calibrated model was then used to predict future water-level changes during the next 10 years (1965–74). Examination of actual water-level changes in 77 wells from 1965–74 indicates a poor correlation between observed and predicted water-level changes. The differences have a mean of 73 ft that is, predicted declines consistently exceeded those observed and a standard deviation of 47 ft. The bias in the predicted water-level change can be accounted for by the large error in the assumed total pumpage during the prediction period. However, the spatial distribution of errors in predicted water-level change does not correlate with the spatial distribution of errors in pumpage. Consequently, the lack of precision probably is not related only to errors in assumed pumpage, but may indicate the presence of other sources of error in the model, such as the two-dimensional representation of a three-dimensional problem or the lack of consideration of land-subsidence processes. This type of postaudit is a valuable method of verifying a model, and an evaluation of predictive errors can provide an increased understanding of the system and aid in assessing the value of undertaking development of a revised model.

Density-matrix simulation of small surface codes under current and projected experimental noise

NASA Astrophysics Data System (ADS)

O'Brien, T. E.; Tarasinski, B.; DiCarlo, L.

2017-09-01

We present a density-matrix simulation of the quantum memory and computing performance of the distance-3 logical qubit Surface-17, following a recently proposed quantum circuit and using experimental error parameters for transmon qubits in a planar circuit QED architecture. We use this simulation to optimize components of the QEC scheme (e.g., trading off stabilizer measurement infidelity for reduced cycle time) and to investigate the benefits of feedback harnessing the fundamental asymmetry of relaxation-dominated error in the constituent transmons. A lower-order approximate calculation extends these predictions to the distance-5 Surface-49. These results clearly indicate error rates below the fault-tolerance threshold of the surface code, and the potential for Surface-17 to perform beyond the break-even point of quantum memory. However, Surface-49 is required to surpass the break-even point of computation at state-of-the-art qubit relaxation times and readout speeds.

45 CFR 98.100 - Error Rate Report.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 45 Public Welfare 1 2013-10-01 2013-10-01 false Error Rate Report. 98.100 Section 98.100 Public Welfare DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL ADMINISTRATION CHILD CARE AND DEVELOPMENT FUND Error Rate Reporting § 98.100 Error Rate Report. (a) Applicability—The requirements of this subpart...