scholarly journals Information Graphs for Binary Predictors

2015 ◽  
Vol 105 (1) ◽  
pp. 9-17 ◽  
Author(s):  
G. Hughes ◽  
N. McRoberts ◽  
F. J. Burnett
Keyword(s):  
Mutual Information ◽  
Crop Protection ◽  
Diagnostic Information ◽  
Specific Information ◽  
Data Set ◽  
Information Theoretic ◽  
Information Updating ◽  
Entropy Information ◽  
Wide Range ◽  
Crop Disease

Binary predictors are used in a wide range of crop protection decision-making applications. Such predictors provide a simple analytical apparatus for the formulation of evidence related to risk factors, for use in the process of Bayesian updating of probabilities of crop disease. For diagrammatic interpretation of diagnostic probabilities, the receiver operating characteristic is available. Here, we view binary predictors from the perspective of diagnostic information. After a brief introduction to the basic information theoretic concepts of entropy and expected mutual information, we use an example data set to provide diagrammatic interpretations of expected mutual information, relative entropy, information inaccuracy, information updating, and specific information. Our information graphs also illustrate correspondences between diagnostic information and diagnostic probabilities.

scholarly journals Do galactic bars depend on environment?: an information theoretic analysis of Galaxy Zoo 2

2020 ◽  
Vol 501 (1) ◽  
pp. 994-1001
Author(s):  
Suman Sarkar ◽  
Biswajit Pandey ◽  
Snehasish Bhattacharjee
Keyword(s):  
Spatial Distribution ◽  
Mutual Information ◽  
Local Density ◽  
Statistical Significance ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Host Galaxy ◽  
Data Sets ◽  
Data Set ◽  
Information Theoretic

ABSTRACT We use an information theoretic framework to analyse data from the Galaxy Zoo 2 project and study if there are any statistically significant correlations between the presence of bars in spiral galaxies and their environment. We measure the mutual information between the barredness of galaxies and their environments in a volume limited sample (Mr ≤ −21) and compare it with the same in data sets where (i) the bar/unbar classifications are randomized and (ii) the spatial distribution of galaxies are shuffled on different length scales. We assess the statistical significance of the differences in the mutual information using a t-test and find that both randomization of morphological classifications and shuffling of spatial distribution do not alter the mutual information in a statistically significant way. The non-zero mutual information between the barredness and environment arises due to the finite and discrete nature of the data set that can be entirely explained by mock Poisson distributions. We also separately compare the cumulative distribution functions of the barred and unbarred galaxies as a function of their local density. Using a Kolmogorov–Smirnov test, we find that the null hypothesis cannot be rejected even at $75{{\ \rm per\ cent}}$ confidence level. Our analysis indicates that environments do not play a significant role in the formation of a bar, which is largely determined by the internal processes of the host galaxy.

scholarly journals Supposed Maximum Mutual Information for Improving Generalization and Interpretation of Multi-Layered Neural Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 123-147 ◽  
Author(s):  
Ryotaro Kamimura
Keyword(s):  
Neural Networks ◽  
Mutual Information ◽  
Data Sets ◽  
Data Set ◽  
Information Theoretic ◽  
Information Maximization ◽  
Maximum Mutual Information ◽  
Information Theoretic Method ◽  
Mutual Information Maximization ◽  
Inputs And Outputs

Abstract The present paper1 aims to propose a new type of information-theoretic method to maximize mutual information between inputs and outputs. The importance of mutual information in neural networks is well known, but the actual implementation of mutual information maximization has been quite difficult to undertake. In addition, mutual information has not extensively been used in neural networks, meaning that its applicability is very limited. To overcome the shortcoming of mutual information maximization, we present it here in a very simplified manner by supposing that mutual information is already maximized before learning, or at least at the beginning of learning. The method was applied to three data sets (crab data set, wholesale data set, and human resources data set) and examined in terms of generalization performance and connection weights. The results showed that by disentangling connection weights, maximizing mutual information made it possible to explicitly interpret the relations between inputs and outputs.

scholarly journals Resolution of Probabilistic Weather Forecasts with Application in Disease Management

2017 ◽  
Vol 107 (2) ◽  
pp. 158-162 ◽  
Author(s):  
G. Hughes ◽  
N. McRoberts ◽  
F. J. Burnett
Keyword(s):  
Disease Management ◽  
Mutual Information ◽  
Disease Risk ◽  
Information Gain ◽  
Weather Forecast ◽  
Weather Data ◽  
Specific Information ◽  
Information Theoretic ◽  
Weather Forecasts ◽  
Measuring Change

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

Quantifying Stimulus Discriminability: A Comparison of Information Theory and Ideal Observer Analysis

2005 ◽  
Vol 17 (4) ◽  
pp. 741-778 ◽  
Author(s):  
Eric E. Thomson ◽  
William B. Kristan
Keyword(s):  
Information Theory ◽  
Mutual Information ◽  
Conditional Entropy ◽  
Ideal Observer ◽  
Upper And Lower Bounds ◽  
Specific Information ◽  
Information Theoretic ◽  
Information Measures ◽  
Improved Performance ◽  
Ideal Observer Analysis

Performance in sensory discrimination tasks is commonly quantified using either information theory or ideal observer analysis. These two quantitative frameworks are often assumed to be equivalent. For example, higher mutual information is said to correspond to improved performance of an ideal observer in a stimulus estimation task. To the contrary, drawing on and extending previous results, we show that five information-theoretic quantities (entropy, response-conditional entropy, specific information, equivocation, and mutual information) violate this assumption. More positively, we show how these information measures can be used to calculate upper and lower bounds on ideal observer performance, and vice versa. The results show that the mathematical resources of ideal observer analysis are preferable to information theory for evaluating performance in a stimulus discrimination task. We also discuss the applicability of information theory to questions that ideal observer analysis cannot address.

Quantifying the dependence of electron fluxes in the Earth’s radiation belts with radial diffusion drivers through the use of information theory.

2021 ◽  
Author(s):  
Adnane Osmane ◽  
Mikko Savola ◽  
Emilia Kilpua ◽  
Hannu Koskinen ◽  
Joe Borovsky ◽  
...  
Keyword(s):  
Solar Wind ◽  
Mutual Information ◽  
Diffusion Processes ◽  
Pearson Correlation ◽  
Energetic Electron ◽  
Radial Diffusion ◽  
Information Theoretic ◽  
Ulf Wave ◽  
Wide Range ◽  
Statistical Dependency

<p>We describe the use of information-theoretic methodologies to characterise statistical dependencies of energetic electron fluxes (130 keV and >1 MeV) with a wide range of solar wind and magnetospheric drivers. We focus specifically on drivers associated with radial diffusion processes and revisit the events studied by Rostoker et al. <em>Geophys. Res. Lett.</em> (1998) in terms of mutual information. The main benefit of mutual information, in comparison to the Pearson correlation and other linear measures, lies in the capacity to distinguish nonlinear statistical dependencies from linear ones.  We find that observed enhancement in relativistic electron fluxes correlate weakly, both linearly and nonlinearly, with the ULF power spectrum, whereas less energetic electron fluxes show stronger statistical dependency with both ground and <em>in situ </em>ULF wave power. Our results are indicative of the need to incorporate data analysis tools that can distinguish between interdependencies of various solar wind drivers.</p>

A Color Coded STEM/SEM Image Storage System

1981 ◽  
Vol 39 ◽  
pp. 272-273
Author(s):  
Y. Kokubo ◽  
W. H. Hardy ◽  
J. Dance ◽  
K. Jones
Keyword(s):  
Image Processing ◽  
Electron Beam ◽  
Storage System ◽  
Particle Analysis ◽  
Specific Information ◽  
X Rays ◽  
Sem Image ◽  
Backscattered Electrons ◽  
Wide Range ◽  
Scanning Electron

A color coded digital image processing is accomplished by using JEM100CX TEM SCAN and ORTEC’s LSI-11 computer based multi-channel analyzer (EEDS-II-System III) for image analysis and display. Color coding of the recorded image enables enhanced visualization of the image using mathematical techniques such as compression, gray scale expansion, gamma-processing, filtering, etc., without subjecting the sample to further electron beam irradiation once images have been stored in the memory.The powerful combination between a scanning electron microscope and computer is starting to be widely used 1) - 4) for the purpose of image processing and particle analysis. Especially, in scanning electron microscopy it is possible to get all information resulting from the interactions between the electron beam and specimen materials, by using different detectors for signals such as secondary electron, backscattered electrons, elastic scattered electrons, inelastic scattered electrons, un-scattered electrons, X-rays, etc., each of which contains specific information arising from their physical origin, study of a wide range of effects becomes possible.

Structure Guided Molecular Docking Assisted Alignment Dependent 3DQSAR Study on Steroidal Aromatase Inhibitors (SAIs) as Anti-breast Cancer Agents

2019 ◽  
Vol 16 (7) ◽  
pp. 808-817 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Breast Cancer ◽  
Molecular Docking ◽  
Aromatase Inhibitors ◽  
Direct Synthesis ◽  
3D Qsar ◽  
Chemotherapeutic Agents ◽  
Modeling Tools ◽  
Data Set ◽  
Wide Range ◽  
Steroidal Aromatase Inhibitors

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

scholarly journals Calling for improved quality in the registration of traditional Chinese medicine during the public health emergency: a survey of trial registries for COVID-19, H1N1, and SARS

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhuoran Kuang ◽  
◽  
Xiaoyan Li ◽  
Jianxiong Cai ◽  
Yaolong Chen ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Secondary Outcome ◽  
Specific Information ◽  
Clinical Trial Registry ◽  
Data Set ◽  
Diagnosis Criteria

Abstract Objective To assess the registration quality of traditional Chinese medicine (TCM) clinical trials for COVID-19, H1N1, and SARS. Method We searched for clinical trial registrations of TCM in the WHO International Clinical Trials Registry Platform (ICTRP) and Chinese Clinical Trial Registry (ChiCTR) on April 30, 2020. The registration quality assessment is based on the WHO Trial Registration Data Set (Version 1.3.1) and extra items for TCM information, including TCM background, theoretical origin, specific diagnosis criteria, description of intervention, and outcomes. Results A total of 136 records were examined, including 129 severe acute respiratory syndrome coronavirus 2 (COVID-19) and 7 H1N1 influenza (H1N1) patients. The deficiencies in the registration of TCM clinical trials (CTs) mainly focus on a low percentage reporting detailed information about interventions (46.6%), primary outcome(s) (37.7%), and key secondary outcome(s) (18.4%) and a lack of summary result (0%). For the TCM items, none of the clinical trial registrations reported the TCM background and rationale; only 6.6% provided the TCM diagnosis criteria or a description of the TCM intervention; and 27.9% provided TCM outcome(s). Conclusion Overall, although the number of registrations of TCM CTs increased, the registration quality was low. The registration quality of TCM CTs should be improved by more detailed reporting of interventions and outcomes, TCM-specific information, and sharing of the result data.

Sign in / Sign up

Export Citation Format

Share Document