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Entropy
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Women in Information Theory 2018
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Women in Information Theory 2018

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (15 December 2018) | Viewed by 37158

Special Issue Editor

Dr. Dawn E. Holmes

E-Mail Website
Guest Editor
Department of Statistics and Applied Probability, University of California, Santa Barbara, CA 93106-3110, USA
Interests: Bayesian networks; machine learning; data mining; knowledge discovery; the foundations of Bayesianism
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims at providing a platform to encourage the participation of women in the field of Information Theory, while being of interest to all researchers.  Original theoretical research papers in this burgeoning interdisciplinary field, as well as applications in all areas of information theory, including, but not limited to, neuroscience, data compression, machine leaning and cryptography, are sought.

Papers exploring the issues faced by women in academia in this area are also welcome. For example, why are there relatively few women working in information theory and what can be done about it? Short personal vignettes are also welcome.

While the focus of this issue is on Women in Information Theory, submissions from all with an interest in this theme are welcome.

Dr. Dawn Holmes
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Entropy
  • Shannon
  • information theory
  • Challenges faced by women in the stem disciplines

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Published Papers (4 papers)

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Research

27 pages, 612 KiB  
Article
Geometric Estimation of Multivariate Dependency
by Salimeh Yasaei Sekeh and Alfred O. Hero
Entropy 2019, 21(8), 787; https://doi.org/10.3390/e21080787 - 12 Aug 2019
Cited by 6 | Viewed by 3774
Abstract
This paper proposes a geometric estimator of dependency between a pair of multivariate random variables. The proposed estimator of dependency is based on a randomly permuted geometric graph (the minimal spanning tree) over the two multivariate samples. This estimator converges to a quantity [...] Read more.
This paper proposes a geometric estimator of dependency between a pair of multivariate random variables. The proposed estimator of dependency is based on a randomly permuted geometric graph (the minimal spanning tree) over the two multivariate samples. This estimator converges to a quantity that we call the geometric mutual information (GMI), which is equivalent to the Henze–Penrose divergence. between the joint distribution of the multivariate samples and the product of the marginals. The GMI has many of the same properties as standard MI but can be estimated from empirical data without density estimation; making it scalable to large datasets. The proposed empirical estimator of GMI is simple to implement, involving the construction of an minimal spanning tree (MST) spanning over both the original data and a randomly permuted version of this data. We establish asymptotic convergence of the estimator and convergence rates of the bias and variance for smooth multivariate density functions belonging to a Hölder class. We demonstrate the advantages of our proposed geometric dependency estimator in a series of experiments. Full article
(This article belongs to the Special Issue Women in Information Theory 2018)
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17 pages, 1195 KiB  
Article
Gender Diversity in STEM Disciplines: A Multiple Factor Problem
by Carmen Botella, Silvia Rueda, Emilia López-Iñesta and Paula Marzal
Entropy 2019, 21(1), 30; https://doi.org/10.3390/e21010030 - 4 Jan 2019
Cited by 137 | Viewed by 22152
Abstract
Lack of diversity, and specifically, gender diversity, is one of the key problems that both technological companies and academia are facing these days. Moreover, recent studies show that the number of female students enrolled in science, technology, engineering and mathematics (STEM) related disciplines [...] Read more.
Lack of diversity, and specifically, gender diversity, is one of the key problems that both technological companies and academia are facing these days. Moreover, recent studies show that the number of female students enrolled in science, technology, engineering and mathematics (STEM) related disciplines have been decreasing in the last twenty years, while the number of women resigning from technological job positions remains unacceptably high. As members of a higher education institution, we foresee that working towards increasing and retaining the number of female students enrolled in STEM disciplines can help to alleviate part of the challenges faced by women in STEM fields. In this paper, we first review the main barriers and challenges that women encounter in their professional STEM careers through different age stages. Next, we focus on the special case of the information theory field, discussing the potential of gendered innovation, and whether it can be applied in the Information Theory case. The working program developed by the School of Engineering at the University of Valencia (ETSE-UV), Spain, which aims at decreasing the gender diversity gap, is then presented and recommendations for practice are given. This program started in 2011 and it encompasses Bachelor, Master and PhD levels. Four main actions are implemented: Providing institutional encouragement and support, increasing the professional support network, promoting and supporting the leadership, and increasing the visibility of female role models. To assess the impact of these actions, a chi-square test of independence is included to evaluate whether there is a significant effect on the percentage of enrolled female students. The percentage of graduated female students in the information and Communications Technology Field is also positioned with respect to other universities and the Spanish reference value. This analysis establishes that, in part, this program has helped to achieve higher female graduation rates, especially among Bachelor students, as well as increasing the number of top-decision positions held by faculty women. Full article
(This article belongs to the Special Issue Women in Information Theory 2018)
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18 pages, 4451 KiB  
Article
A Swarm-Based Approach to Generate Challenging Mazes
by Joanna Kwiecień
Entropy 2018, 20(10), 762; https://doi.org/10.3390/e20100762 - 5 Oct 2018
Cited by 12 | Viewed by 4077
Abstract
Swarm intelligence draws its inspiration from the collective behaviour of many individual agents interacting with both one another and their environment. This paper presents a possibility to apply a swarm-based algorithm, modelled after the behaviour of individuals operating within a group where individuals [...] Read more.
Swarm intelligence draws its inspiration from the collective behaviour of many individual agents interacting with both one another and their environment. This paper presents a possibility to apply a swarm-based algorithm, modelled after the behaviour of individuals operating within a group where individuals move around in the manner intended to avoid mutual collisions, to create the most challenging maze developed on a board with determined dimensions. When solving such a problem, two complexity measures are used. Firstly, the complexity of the path was assumed to be a quality criterion, depending on the number of bends and the length of the path between two set points that was subjected to maximisation. Secondly, we focus on the well-known concept of the maze complexity given as the total complexity of the path and all branches. Owing to the uniqueness of the problem, consisting in the maze modification, a methodology was developed to make it possible for the individuals belonging to their population to make various types of movements, e.g., approach the best individual, within the range of visibility, or relocate randomly. The test results presented here indicate a potential prospect of application of the swarm-based methods to generate more and more challenging two-dimensional mazes. Full article
(This article belongs to the Special Issue Women in Information Theory 2018)
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18 pages, 5390 KiB  
Article
An Image Fusion Method Based on Sparse Representation and Sum Modified-Laplacian in NSCT Domain
by Yuanyuan Li, Yanjing Sun, Xinhua Huang, Guanqiu Qi, Mingyao Zheng and Zhiqin Zhu
Entropy 2018, 20(7), 522; https://doi.org/10.3390/e20070522 - 11 Jul 2018
Cited by 77 | Viewed by 6247
Abstract
Multi-modality image fusion provides more comprehensive and sophisticated information in modern medical diagnosis, remote sensing, video surveillance, etc. Traditional multi-scale transform (MST) based image fusion solutions have difficulties in the selection of decomposition level, and the contrast loss in fused image. At the [...] Read more.
Multi-modality image fusion provides more comprehensive and sophisticated information in modern medical diagnosis, remote sensing, video surveillance, etc. Traditional multi-scale transform (MST) based image fusion solutions have difficulties in the selection of decomposition level, and the contrast loss in fused image. At the same time, traditional sparse-representation based image fusion methods suffer the weak representation ability of fixed dictionary. In order to overcome these deficiencies of MST- and SR-based methods, this paper proposes an image fusion framework which integrates nonsubsampled contour transformation (NSCT) into sparse representation (SR). In this fusion framework, NSCT is applied to source images decomposition for obtaining corresponding low- and high-pass coefficients. It fuses low- and high-pass coefficients by using SR and Sum Modified-laplacian (SML) respectively. NSCT inversely transforms the fused coefficients to obtain the final fused image. In this framework, a principal component analysis (PCA) is implemented in dictionary training to reduce the dimension of learned dictionary and computation costs. A novel high-pass fusion rule based on SML is applied to suppress pseudo-Gibbs phenomena around singularities of fused image. Compared to three mainstream image fusion solutions, the proposed solution achieves better performance on structural similarity and detail preservation in fused images. Full article
(This article belongs to the Special Issue Women in Information Theory 2018)
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