Assembly and use of new task rules in fronto-parietal cortex

Severe capacity limits, closely associated with fluid intelligence, arise in learning and use of new task rules. We used fMRI to investigate these limits in a series of multirule tasks involving different stimuli, rules, and response keys. Data were analyzed both during presentation of instructions and during later task execution. Between tasks, we manipulated the number of rules specified in task instructions, and within tasks, we manipulated the number of rules operative in each trial block. Replicating previous results, rule failures were strongly predicted by fluid intelligence and increased with the number of operative rules. In fMRI data, analyses of the instruction period showed that the bilateral inferior frontal sulcus, intraparietal sulcus, and presupplementary motor area were phasically active with presentation of each new rule. In a broader range of frontal and parietal regions, baseline activity gradually increased as successive rules were instructed. During task performance, we observed contrasting fronto-parietal patterns of sustained (block-related) and transient (trial-related) activity. Block, but not trial, activity showed effects of task complexity. We suggest that, as a new task is learned, a fronto-parietal representation of relevant rules and facts is assembled for future control of behavior. Capacity limits in learning and executing new rules, and their association with fluid intelligence, may be mediated by this load-sensitive fronto-parietal network

Adaptive EWMA Control Charts with a Time Varying Smoothing Parameter

It is known that time-weighted charts like EWMA or CUSUM are designed to be optimal to detect a specific shift. If they are designed to detect, for instance, a very small shift, they can be inefficient to detect moderate or large shifts. In the literature, several alternatives have been proposed to circumvent this limitation, like the use of control charts with variable parameters or adaptive control charts. This paper has as main goal to propose some adaptive EWMA control charts (AEWMA) based on the assessment of a potential misadjustment, which is translated into a time-varying smoothing parameter. The resulting control charts can be seen as a smooth combination between Shewhart and EWMA control charts that can be efficient for a wide range of shifts. Markov chain procedures are established to analyze and design the proposed charts. Comparisons with other adaptive and traditional control charts show the advantages of the proposals.Acknowledgements: financial support from the Spanish Ministry of Education and Science, research project ECO2012-38442

Improving Shewhart Control Chart Performance in the Presence of Measurement Error Using Multiple Measurements and Two-Stage Sampling

The usual Shewhart control chart efficiently detects large shifts in the mean of a quality characteristic and has been extensively studied in the literature. Most proposed alternatives to the Shewhart chart aim to improve either the signal performance for smaller mean shifts or reduce the sampling effort required to detect a larger shift. Measurement error has been shown in the literature to result in reduced power to detect process shifts. The combination of multiple measurements and two-stage sampling is considered here as a strategy for both regaining power lost due to measurement error and specifically tuning the charts for shifts of a particular size. It is shown that both the average total sample size and the average run length are improved relative to the standard Shewhart control chart under the same measurement error conditions. Chart designs are recommended to achieve particular control objectives

Elastic scaling of cloud application performance based on Western Electric rules by injection of aspect-oriented code

The main benefit of cloud computing lies in the elasticity of virtual resources that are provided to end users. Cloud users do not have to pay fixed hardware costs and are charged for consumption of computing resources only. While this might be an improvement for software developers who use the cloud, application users and consumers might rather be interested in paying for application performance than resource consumption. However there is little effort on providing elasticity based on performance goals instead of resource consumption. In this paper an autoscaling method is presented which aims at providing increased application performance as it is demanded by consumers. Elastic scaling is based on “statistical process monitoring and control” and “Western Electric rules”. By demonstrating the architecture of the autoscaling method and providing performance measurements gathered in an OpenStack cloud environment, we show how the injection of aspect-oriented code into cloud applications allows for improving application performance by automatically adapting the underlying virtual environment to pre-defined performance goals. The effectiveness of the autoscaling method is verified by an experiment with a test program which shows that the program executes in only half of the time which is required if no autoscaling capabilities are provided

Using Golden Ratio Search to Improve Paired Construction of Quality Control Charts

Our purpose is to indicate a new method for determining the control limits of univariate control charts to show the effectiveness of golden ratios search. We examine a solution to a problem when signals from mean and variance charts differ. Lack of concordance in the signals from mean and variance (or standard deviation) control charts bring confusion to Quality Contro managers which in turn may lead to sub optimal management quality practices. To achieve better quality management practice, we provide a solution to the problem of finding different decision signals for in-control processes for quality control charts for mean and variability. We construct the control charts in experimental conditions for in-control average run length using the methods of simulation. Finally, we employ the golden ratio search method to identify the control limit parameters which differ from standard methods for constructing quality control charts. Last, we minimize the length of time in computation in the construction of these new quality control charts

A Modified \u3cem\u3eX̄\u3c/em\u3e Control Chart for Samples Drawn from Finite Populations

The X̄ chart works well under the assumption of random sampling from infinite populations. However, many process monitoring scenarios may consist of random sampling from finite populations. A modified X̄ chart is proposed in this article to solve the problems encountered by the standard X̄ chart when samples are drawn from finite populations