scholarly journals MUPPscore: An R script for expected a posteriori scoring of multidimensional pairwise preference items

2021 ◽  
Author(s):  
Li Guan ◽  
Tianjun Sun ◽  
NATHAN T CARTER
Keyword(s):  
Latent Trait ◽  
Three Dimensional ◽  
A Posteriori ◽  
Microsoft Excel ◽  
Pairwise Preference ◽  
Bayes Modal ◽  
Computationally Intensive ◽  
Item Response Function ◽  
Scoring Algorithms ◽  
Marginal Reliability

In this manual, we present a flexible and freely available tool for obtaining latent trait scores from multi-unidimensional pairwise preference (MUPP) tests: An R script named MUPPscore. The development of the MUPPscore script provides a solution to the issue that is the previously inconvenient estimation of forced choice item pairs. Instead of using the computationally-intensive multidimensional Bayes modal procedure, the MUPPscore script employs the expected a posterior (EAP) scoring procedure, which provides plausible latent trait score estimates and is also consistent with scoring algorithms used in existing software programs intended for single stimulus measures (e.g., GGUM2004, IRTPRO). The MUPPscore script also returns the empirical marginal reliability of EAP theta estimates and outputs a series of files that can be used to easily create and modify three-dimensional surface charts for plotting MUPP item response function (IRF) in Microsoft Excel.

Effective Convergence Checks for Verifying Finite Element Stresses at Three-Dimensional Stress Concentrations

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
J. R. Beisheim ◽  
G. B. Sinclair ◽  
P. J. Roache
Keyword(s):  
Finite Element ◽  
Error Estimation ◽  
A Posteriori Error Estimates ◽  
Three Dimensional ◽  
Posteriori Error ◽  
Test Problems ◽  
A Posteriori ◽  
Stress Concentrations ◽  
Element Analysis ◽  
A Posteriori Error

Current computational capabilities facilitate the application of finite element analysis (FEA) to three-dimensional geometries to determine peak stresses. The three-dimensional stress concentrations so quantified are useful in practice provided the discretization error attending their determination with finite elements has been sufficiently controlled. Here, we provide some convergence checks and companion a posteriori error estimates that can be used to verify such three-dimensional FEA, and thus enable engineers to control discretization errors. These checks are designed to promote conservative error estimation. They are applied to twelve three-dimensional test problems that have exact solutions for their peak stresses. Error levels in the FEA of these peak stresses are classified in accordance with: 1–5%, satisfactory; 1/5–1%, good; and <1/5%, excellent. The present convergence checks result in 111 error assessments for the test problems. For these 111, errors are assessed as being at the same level as true exact errors on 99 occasions, one level worse for the other 12. Hence, stress error estimation that is largely reasonably accurate (89%), and otherwise modestly conservative (11%).

scholarly journals Three-Dimensional Magnetic Inversion Based on an Adaptive Quadtree Data Compression

2020 ◽  
Vol 10 (21) ◽  
pp. 7636
Author(s):  
Dandan Jiang ◽  
Zhaofa Zeng ◽  
Shuai Zhou ◽  
Yanwu Guan ◽  
Tao Lin ◽  
...  
Keyword(s):  
Data Compression ◽  
Large Scale ◽  
Mineral Exploration ◽  
Three Dimensional ◽  
Original Data ◽  
Magnetic Data ◽  
The Best Approximation ◽  
Magnetic Inversion ◽  
Iterative Inversion ◽  
Computationally Intensive

Three-dimensional magnetic inversion allows the distribution of magnetic parameters to be obtained, and it is an important tool for geological exploration and interpretation. However, because of the redundancy of the data obtained from large-scale investigations or high-density sampling, it is very computationally intensive to use these data for iterative inversion calculations. In this paper, we propose a method for compressing magnetic data by using an adaptive quadtree decomposition method, which divides the two-dimensional data region into four quadrants and progressively subdivides them by recursion until the data in each quadrant meets the regional consistency criterion. The method allows for dense sampling at the abnormal boundaries with large amplitude changes and sparse sampling at regions with small amplitude changes, and achieves the best approximation to the original data with the least amount of data, thus retaining more anomalous information while achieving the purpose of data compression. In addition, assigning values to the data in the quadrants using the averaging method is essentially equivalent to average filtering, which reduces the noise of the magnetic data. Testing the synthetic model and applying the method to mineral exploration a prove that it can effectively compress the magnetic data and greatly improve the computational efficiency.

scholarly journals A Uniform Architecture Design for Accelerating 2D and 3D CNNs on FPGAs

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 65 ◽  
Author(s):  
Zhiqiang Liu ◽  
Paul Chow ◽  
Jinwei Xu ◽  
Jingfei Jiang ◽  
Yong Dou ◽  
...  
Keyword(s):  
Design Space ◽  
State Of The Art ◽  
Three Dimensional ◽  
Architecture Design ◽  
Computer Vision Applications ◽  
Computationally Intensive ◽  
On Chip ◽  
3D Cnn ◽  
High Level ◽  
2D And 3D

Three-dimensional convolutional neural networks (3D CNNs) have gained popularity in many complicated computer vision applications. Many customized accelerators based on FPGAs are proposed for 2D CNNs, while very few are for 3D CNNs. Three-D CNNs are far more computationally intensive and the design space for 3D CNN acceleration has been further expanded since one more dimension is introduced, making it a big challenge to accelerate 3D CNNs on FPGAs. Motivated by the finding that the computation patterns of 2D and 3D CNNs are very similar, we propose a uniform architecture design for accelerating both 2D and 3D CNNs in this paper. The uniform architecture is based on the idea of mapping convolutions to matrix multiplications. A customized mapping module is developed to generate the feature matrix tilings with no need to store the entire enlarged feature matrix on-chip or off-chip, a splitting strategy is adopted to reconstruct a convolutional layer to adapt to the on-chip memory capacity, and a 2D multiply-and-accumulate (MAC) array is adopted to compute matrix multiplications efficiently. For demonstration, we implement an accelerator prototype with a high-level synthesis (HLS) methodology on a Xilinx VC709 board and test the accelerator on three typical CNN models: AlexNet, VGG16, and C3D. Experimental results show that the accelerator achieves state-of-the-art throughput performance on both 2D and 3D CNNs, with much better energy efficiency than the CPU and GPU.

1997 Best Paper Award—Education Committee: A Three-Dimensional Turbine Engine Analysis Compressor Code (TEACC) for Steady-State Inlet Distortion

1998 ◽  
Vol 120 (3) ◽  
pp. 422-430 ◽  
Author(s):  
A. Hale ◽  
W. O’Brien
Keyword(s):  
Three Dimensional ◽  
Simulation Tool ◽  
Source Terms ◽  
Turbine Engine ◽  
Streamline Curvature ◽  
Inlet Distortion ◽  
Blade Row ◽  
Practical Design ◽  
Distorted Region ◽  
Computationally Intensive

The direct approach of modeling the flow between all blade passages for each blade row in the compressor is too computationally intensive for practical design and analysis investigations with inlet distortion. Therefore a new simulation tool called the Turbine Engine Analysis Compressor Code (TEACC) has been developed. TEACC solves the compressible, time-dependent, three-dimensional Euler equations modified to include turbomachinery source terms, which represent the effect of the blades. The source terms are calculated for each blade row by the application of a streamline curvature code. TEACC was validated against experimental data from the transonic NASA rotor, Rotor 1B, for a clean inlet and for an inlet distortion produced by a 90-deg, one-per-revolution distortion screen. TEACC revealed that strong swirl produced by the rotor caused the compressor to increase in loading in the direction of rotor rotation through the distorted region and decrease in loading circumferentially away from the distorted region.

Nonparametric Item Response Function Estimates with the EM Algorithm

2002 ◽  
Vol 27 (3) ◽  
pp. 291-317 ◽  
Author(s):  
Natasha Rossi ◽  
Xiaohui Wang ◽  
James O. Ramsay
Keyword(s):  
Em Algorithm ◽  
Item Response ◽  
Functional Data ◽  
Latent Variable ◽  
Marginal Likelihood ◽  
Latent Trait ◽  
Functional Variation ◽  
The Em Algorithm ◽  
A New Technique ◽  
Item Response Function

The methods of functional data analysis are used to estimate item response functions (IRFs) nonparametrically. The EM algorithm is used to maximize the penalized marginal likelihood of the data. The penalty controls the smoothness of the estimated IRFs, and is chosen so that, as the penalty is increased, the estimates converge to shapes closely represented by the three-parameter logistic family. The one-dimensional latent trait model is recast as a problem of estimating a space curve or manifold, and, expressed in this way, the model no longer involves any latent constructs, and is invariant with respect to choice of latent variable. Some results from differential geometry are used to develop a data-anchored measure of ability and a new technique for assessing item discriminability. Functional data-analytic techniques are used to explore the functional variation in the estimated IRFs. Applications involving simulated and actual data are included.

scholarly journals Maximum a posteriori estimation of crystallographic phases in X-ray diffraction tomography

2015 ◽  
Vol 373 (2043) ◽  
pp. 20140392 ◽  
Author(s):  
Doĝa Gürsoy ◽  
Tekin Biçer ◽  
Jonathan D. Almer ◽  
Raj Kettimuthu ◽  
Stuart R. Stock ◽  
...  
Keyword(s):  
National Laboratory ◽  
Spinous Process ◽  
Argonne National Laboratory ◽  
Three Dimensional ◽  
Polycrystalline Materials ◽  
Reconstruction Method ◽  
Diffraction Tomography ◽  
A Posteriori ◽  
X Ray Diffraction ◽  
X Ray

A maximum a posteriori approach is proposed for X-ray diffraction tomography for reconstructing three-dimensional spatial distribution of crystallographic phases and orientations of polycrystalline materials. The approach maximizes the a posteriori density which includes a Poisson log-likelihood and an a priori term that reinforces expected solution properties such as smoothness or local continuity. The reconstruction method is validated with experimental data acquired from a section of the spinous process of a porcine vertebra collected at the 1-ID-C beamline of the Advanced Photon Source, at Argonne National Laboratory. The reconstruction results show significant improvement in the reduction of aliasing and streaking artefacts, and improved robustness to noise and undersampling compared to conventional analytical inversion approaches. The approach has the potential to reduce data acquisition times, and significantly improve beamtime efficiency.

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