Composite Module Analyst: Tool for Prediction of DNA Transcription Regulation. Testing on Simulated Data

Dual-energy imaging has emerged as a superior way to recognize materials in X-ray computed tomography. To estimate material properties such as effective atomic number and density, one often generates images in terms of basis functions. This requires decomposition of the dual-energy sinograms into basis sinograms, and subsequently reconstructing the basis images. However, the presence of metal can distort the reconstructed images. In this paper we investigate how photoelectric and Compton basis functions, and synthesized monochromatic basis (SMB) functions behave in the presence of metal and its effect on estimation of effective atomic number and density. Our results indicate that SMB functions, along with edge-preserving total variation regularization, show promise for improved material estimation in the presence of metal. The results are demonstrated using both simulated data as well as data collected from a dualenergy medical CT scanner.

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Maxwell’s Equations versus Newton’s Third Law

10.26434/chemrxiv.6297185 ◽

2018 ◽

Author(s):

Glyn Kennell ◽

Richard Evitts

Keyword(s):

Electric Fields ◽

Maxwell’S Equations ◽

Maxwell's Equations ◽

Simulated Data ◽

Numerical Data ◽

Transport Equations ◽

Concentration Gradients ◽

Third Law

The presented simulated data compares concentration gradients and electric fields with experimental and numerical data of others. This data is simulated for cases involving liquid junctions and electrolytic transport. The objective of presenting this data is to support a model and theory. This theory demonstrates the incompatibility between conventional electrostatics inherent in Maxwell's equations with conventional transport equations. <br>

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Constructing and model-fitting receiver operator characteristics using continuous data.

10.31234/osf.io/qdgjc ◽

2018 ◽

Author(s):

Josephine Ann Urquhart ◽

Akira O'Connor

Keyword(s):

Signal Detection ◽

Least Squares Method ◽

Model Fitting ◽

Simulated Data ◽

Continuous Variable ◽

Continuous Data ◽

Model Parameters ◽

Operating Characteristics ◽

Unequal Variance ◽

Discrete Discrimination

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.

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The Bias of Individuals (In Crowds): Why Implicit Bias is Probably a Noisily-Measured Individual-Level Construct

10.31234/osf.io/yhv35 ◽

2020 ◽

Author(s):

Paul Robert Connor ◽

Ellen Riemke Katrien Evers

Keyword(s):

Measurement Error ◽

Present Article ◽

Implicit Bias ◽

Empirical Support ◽

Simulated Data ◽

Individual Level ◽

Substantial Error

Payne, Vuletich, and Lundberg’s bias-of-crowds model proposes that a number of empirical puzzles can be resolved by conceptualizing implicit bias as a feature of situations rather than a feature of individuals. In the present article we argue against this model and propose that, given the existing evidence, implicit bias is best understood as an individual-level construct measured with substantial error. First, using real and simulated data, we show how each of Payne and colleagues’ proposed puzzles can be explained as being the result of measurement error and its reduction via aggregation. Second, we discuss why the authors’ counterarguments against this explanation have been unconvincing. Finally, we test a hypothesis derived from the bias-of-crowds model about the effect of an individually targeted “implicit-bias-based expulsion program” within universities and show the model to lack empirical support. We conclude by considering the implications of conceptualizing implicit bias as a noisily measured individual-level construct for ongoing implicit-bias research. All data and code are available at https://osf.io/tj8u6/.

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