WE-G-110-06: Introduction to the AAPM Task Group No. 195 - Monte Carlo Reference Data Sets for Imaging Research

2011 ◽  
Vol 38 (6Part33) ◽  
pp. 3834-3834
Author(s):  
I Sechopoulos ◽  
S Abboud ◽  
E Ali ◽  
A Badal ◽  
A Badano ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reference Data ◽  
Task Group ◽  
Data Sets ◽  
Imaging Research

scholarly journals Monte Carlo reference data sets for imaging research: Executive summary of the report of AAPM Research Committee Task Group 195

2015 ◽  
Vol 42 (10) ◽  
pp. 5679-5691 ◽  
Author(s):  
Ioannis Sechopoulos ◽  
Elsayed S. M. Ali ◽  
Andreu Badal ◽  
Aldo Badano ◽  
John M. Boone ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reference Data ◽  
Task Group ◽  
Data Sets ◽  
Research Committee ◽  
Executive Summary ◽  
Imaging Research

Multiple data parameter identification for nonlinear conceptual models

1997 ◽  
Vol 36 (5) ◽  
pp. 61-68 ◽  
Author(s):  
Hermann Eberl ◽  
Amar Khelil ◽  
Peter Wilderer
Keyword(s):  
Optimization Problem ◽  
Conceptual Models ◽  
Reference Data ◽  
Transport Model ◽  
Calibration Method ◽  
Data Sets ◽  
Multiple Data ◽  
Marquardt Algorithm ◽  
Multicriteria Optimization Problem ◽  
Higher Order Differential Equations

A numerical method for the identification of parameters of nonlinear higher order differential equations is presented, which is based on the Levenberg-Marquardt algorithm. The estimation of the parameters can be performed by using several reference data sets simultaneously. This leads to a multicriteria optimization problem, which will be treated by using the Pareto optimality concept. In this paper, the emphasis is put on the presentation of the calibration method. As an example identification of the parameters of a nonlinear hydrological transport model for urban runoff is included, but the method can be applied to other problems as well.

scholarly journals Critical Aspects of Person Counting and Density Estimation

2021 ◽  
Vol 7 (2) ◽  
pp. 21
Author(s):  
Roland Perko ◽  
Manfred Klopschitz ◽  
Alexander Almer ◽  
Peter M. Roth
Keyword(s):  
Density Estimation ◽  
Network Architecture ◽  
Reference Data ◽  
State Of The Art ◽  
Limit State ◽  
Ground Truth ◽  
Data Sets ◽  
Ground Truth Generation ◽  
Baseline Approach ◽  
Critical Aspects

Many scientific studies deal with person counting and density estimation from single images. Recently, convolutional neural networks (CNNs) have been applied for these tasks. Even though often better results are reported, it is often not clear where the improvements are resulting from, and if the proposed approaches would generalize. Thus, the main goal of this paper was to identify the critical aspects of these tasks and to show how these limit state-of-the-art approaches. Based on these findings, we show how to mitigate these limitations. To this end, we implemented a CNN-based baseline approach, which we extended to deal with identified problems. These include the discovery of bias in the reference data sets, ambiguity in ground truth generation, and mismatching of evaluation metrics w.r.t. the training loss function. The experimental results show that our modifications allow for significantly outperforming the baseline in terms of the accuracy of person counts and density estimation. In this way, we get a deeper understanding of CNN-based person density estimation beyond the network architecture. Furthermore, our insights would allow to advance the field of person density estimation in general by highlighting current limitations in the evaluation protocols.

scholarly journals Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo

2020 ◽  
Author(s):  
Gregory Ross ◽  
Ellery Russell ◽  
Yuqing Deng ◽  
Chao Lu ◽  
Edward Harder ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Free Energy ◽  
Drug Discovery ◽  
Free Energy Calculations ◽  
Data Sets ◽  
Grand Canonical Monte Carlo ◽  
Enhanced Sampling ◽  
Computational Performance ◽  
Energy Perturbation ◽  
Grand Canonical

<div>The prediction of protein-ligand binding affinities using free energy perturbation (FEP) is becoming increasingly routine in structure-based drug discovery. Most FEP packages use molecular dynamics (MD) to sample the configurations of proteins and ligands, as MD is well-suited to capturing coupled motion. However, MD can be prohibitively inefficient at sampling water molecules that are buried within binding sites, which has severely limited the domain of applicability of FEP and its prospective usage in drug discovery. In this paper, we present an advancement of FEP that augments MD with grand canonical Monte Carlo (GCMC), an enhanced sampling method, to overcome the problem of sampling water. We accomplished this without degrading computational performance. On both old and newly assembled data sets of proteinligand complexes, we show that the use of GCMC in FEP is essential for accurate and robust predictions for ligand perturbations that disrupt buried water. <br></div>

scholarly journals An Explainable Bayesian Decision Tree Algorithm

2021 ◽  
Vol 7 ◽  
Author(s):  
Giuseppe Nuti ◽  
Lluís Antoni Jiménez Rugama ◽  
Andreea-Ingrid Cross
Keyword(s):  
Monte Carlo ◽  
Decision Tree ◽  
Decision Trees ◽  
Medical Industry ◽  
Data Sets ◽  
Bayesian Decision ◽  
Decision Tree Algorithm ◽  
Probabilistic Framework ◽  
Classification Problems ◽  
Tree Algorithm

Bayesian Decision Trees provide a probabilistic framework that reduces the instability of Decision Trees while maintaining their explainability. While Markov Chain Monte Carlo methods are typically used to construct Bayesian Decision Trees, here we provide a deterministic Bayesian Decision Tree algorithm that eliminates the sampling and does not require a pruning step. This algorithm generates the greedy-modal tree (GMT) which is applicable to both regression and classification problems. We tested the algorithm on various benchmark classification data sets and obtained similar accuracies to other known techniques. Furthermore, we show that we can statistically analyze how was the GMT derived from the data and demonstrate this analysis with a financial example. Notably, the GMT allows for a technique that provides explainable simpler models which is often a prerequisite for applications in finance or the medical industry.

scholarly journals An updated global COI barcode reference data set for Fall Armyworm (Spodoptera frugiperda) and first record of this species in Bhutan

2020 ◽  
Author(s):  
Kiran Mahat ◽  
Andrew Mitchell ◽  
Tshelthrim Zangpo
Keyword(s):  
Spodoptera Frugiperda ◽  
Reference Data ◽  
Dna Barcode ◽  
Fall Armyworm ◽  
First Record ◽  
Molecular Techniques ◽  
Data Sets ◽  
Pest Species ◽  
Maize Crop ◽  
Data Set

AbstractWe report the first detection of Fall Armyworm (FAW), Spodoptera frugiperda (Smith, 1797), in Bhutan. FAW feeds on more than 300 plant species and is a serious pest of many. It has been spreading through Africa since 2016 and Asia since 2018. In Bhutan, this species was first detected in maize fields in the western part of the country in September 2019 and subsequently found infesting maize crop in southern parts of the country in December 2019 and April 2020. Using morphological and molecular techniques the presence of the first invading populations of S. frugiperda in Bhutan is confirmed through this study. We present an updated reference DNA barcode data set for FAW comprising 374 sequences, which can be used to reliably identify this serious pest species, and discuss some of the reasons why such compiled reference data sets are necessary, despite the publicly availability of the underlying data. We also report on a second armyworm species, the Northern Armyworm, Mythimna separata (Walker, 1865), in rice, maize and other crops in eighteen districts of Bhutan.

scholarly journals Demonstrated Wavelength Portability of Raman Reference Data for Explosives and Chemical Detection

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Timothy J. Johnson ◽  
Yin-Fong Su ◽  
Kristin H. Jarman ◽  
Brenda M. Kunkel ◽  
Jerome C. Birnbaum ◽  
...  
Keyword(s):  
Near Infrared ◽  
Reference Data ◽  
Current Sensor ◽  
Data Sets ◽  
Reference Library ◽  
Wavelength Calibration ◽  
Intensity Response ◽  
Excitation Laser ◽  
Key Issues ◽  
Infrared Excitation

As Raman spectroscopy continues to evolve, questions arise as to the portability of Raman data: dispersive versus Fourier transform, wavelength calibration, intensity calibration, and in particular the frequency of the excitation laser. While concerns about fluorescence arise in the visible or ultraviolet, most modern (portable) systems use near-infrared excitation lasers, and many of these are relatively close in wavelength. We have investigated the possibility of porting reference data sets from one NIR wavelength system to another: We have constructed a reference library consisting of 145 spectra, including 20 explosives, as well as sundry other compounds and materials using a 1064 nm spectrometer. These data were used as a reference library to evaluate the same 145 compounds whose experimental spectra were recorded using a second 785 nm spectrometer. In 128 cases of 145 (or 88.3% including 20/20 for the explosives), the compounds were correctly identified with a mean “hit score” of 954 of 1000. Adding in criteria for when to declare a correct match versus when to declare uncertainty, the approach was able to correctly categorize 134 out of 145 spectra, giving a 92.4% accuracy. For the few that were incorrectly identified, either the matched spectra were spectroscopically similar to the target or the 785 nm signal was degraded due to fluorescence. The results indicate that imported data recorded at a different NIR wavelength can be successfully used as reference libraries, but key issues must be addressed: the reference data must be of equal or higher resolution than the resolution of the current sensor, the systems require rigorous wavelength calibration, and wavelength-dependent intensity response should be accounted for in the different systems.

scholarly journals Evaluation of single-cell classifiers for single-cell RNA sequencing data sets

2019 ◽  
Vol 21 (5) ◽  
pp. 1581-1595 ◽  
Author(s):  
Xinlei Zhao ◽  
Shuang Wu ◽  
Nan Fang ◽  
Xiao Sun ◽  
Jue Fan
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Reference Data ◽  
Predictive Accuracy ◽  
Cell Types ◽  
Superior Performance ◽  
Marker Genes ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

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