66 research outputs found

    Contextual classification of multispectral image data

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    An Introduction to Statistical Issues and Methods in Metrology for Physical Science and Engineering

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    This article provides an overview of the interplay between statistics and measurement. Measurement quality affects inference from data collected and analyzed using statistical methods while appropriate data analysis quantifies the quality of measurements. This article brings material on statistics and measurement together in one place as a resource for practitioners. Both frequentist and Bayesian methods are discussed

    An Exact Formula for the Average Run Length to False Alarm of the Generalized Shiryaev-Roberts Procedure for Change-Point Detection under Exponential Observations

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    We derive analytically an exact closed-form formula for the standard minimax Average Run Length (ARL) to false alarm delivered by the Generalized Shiryaev-Roberts (GSR) change-point detection procedure devised to detect a shift in the baseline mean of a sequence of independent exponentially distributed observations. Specifically, the formula is found through direct solution of the respective integral (renewal) equation, and is a general result in that the GSR procedure's headstart is not restricted to a bounded range, nor is there a "ceiling" value for the detection threshold. Apart from the theoretical significance (in change-point detection, exact closed-form performance formulae are typically either difficult or impossible to get, especially for the GSR procedure), the obtained formula is also useful to a practitioner: in cases of practical interest, the formula is a function linear in both the detection threshold and the headstart, and, therefore, the ARL to false alarm of the GSR procedure can be easily computed.Comment: 9 pages; Accepted for publication in Proceedings of the 12-th German-Polish Workshop on Stochastic Models, Statistics and Their Application

    Imagining the Lives of Others: Empathy in Public Relations

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    This paper asks how we might theorise empathy in public relations (PR) in the light of a widespread ā€˜turnā€™ towards emotion in the academy, as well as in popular discourse. Two distinct notions of empathy are explored: ā€˜trueā€™empathy as discussed in intercultural communication, is driven by a human concern for the other in order to understand experiences, feelings and situations that may be different from our own; whereas ā€˜instrumentalā€™ empathy, reflecting a self orientation, is said to characterise much neoliberal market discourse in which corporations are urged to understand their customers better. Thus, while empathy may seem highly desirable as a means to enter into dialogue with an organisationā€™s publics, particularly during times of social upheaval and crisis, it is important to pay attention to empathy in public relations discourses including whose goals are served by empathetic engagement; and the type(s) of empathy called upon within a PR context. A literature review identified a socio-cultural definition of empathy as ā€˜imaginary effortā€™. A review of the public relations literature, however, found that while empathy is considered an important principle and personal attribute, notions of empathy, with a few exceptions, are under-explored. Nonfunctionalist, socio-cultural research which examines the meanings that practitioners associate with empathy is distinctly lacking; therefore in order to gain further insight into empathy, two sources of data were explored. The analysis of a popular online practitioner blog showed that other-centred empathic skill is discursively framed as instrumental in achieving clientsā€™ business objectives. The analysis of three empathy statements drawn from 12 in-depth interviews with practitioners revealed complex empathic discourse in practitioner-client relationships. While the findings are limited to illustrative analyses only, this paper challenges researchers to develop conceptualisations and perspectives of empathy as imaginary effort in public relations

    Applying Quality Control Charts to the Analysis of Single-Subject Data Sequences

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    Techniques from the field of quality control can be used to classify the quality of individual samples of physical or cognitive performance. After stable baselines have been established for an individual, deviations in performance can be evaluated using control charts. The effectiveness of this approach in evaluating cognitive performance was tested using databases collected under a variety of risk factors. The sensitivity and specificity characteristics of Shewhart, cumulativesum (CUSUM), and exponentially weighted moving average (EWMA) control charts were determined for a total of 174 trials involving 10 participants and 23 cognitive performance assessment measures. The most effective technique in each case was typically a function of the specific performance measure and the type of performance change being evaluated. Sensitivity and specificity for the best techniques were as high as 100%. This study demonstrated the usefulness of quality control charts as a tool to evaluate individual participant performance over time. Actual or potential applications of this research include readiness-to-perform screening of industrial workers in order to improve the health and safety of the workforce.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

    Computational Methods for Protein Identification from Mass Spectrometry Data

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    Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology

    Bayes Inference for a Tractable New Class of Non-symmetric Distributions for 3-Dimensional Rotations

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    Both existing models for non-symmetric distributions on 3-dimensional rotations and their associated one-sample inference methods have serious limitations in terms of both interpretability and ease of use. Based on the intuitively appealing Uniform Axis- Random Spin (UARS) construction of Bingham, Nordman, and Vardeman (2009) for symmetric families of distributions, we propose new highly interpretable and tractable classes of non-symmetric distributions that are derived from mixing UARS distributions. These have an appealing Preferred Axis-Random Spin (PARS) construction and (unlike existing models) directly interpretable parameters. Non-informative one-sample Bayes inference in these models is a direct generalization of UARS methods introduced in Bingham, Vardeman, and Nordman (2009), where credible levels were found to be essentially equivalent to frequentist coverage probabilities. We apply the new models and inference methods to a problem in biomechanics, where comparison of model parameters provides meaningful comparisons for the nature of movement about the calcaneocuboid joint of three different primate subjects.The final publication is available at Springer via http://dx.doi.org/10.1007/s13253-012-0107-9</p
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