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  • Publication . Preprint . Article . 2020 . Embargo End Date: 01 Jan 2020
    Open Access
    Authors: 
    Rouzbeh Allahverdi; Mustafa A. Amin; Asher Berlin; Nicolás Bernal; Christian T. Byrnes; M. Sten Delos; Adrienne L. Erickcek; Miguel Escudero; Daniel G. Figueroa; Katherine Freese; +16 more
    Publisher: arXiv
    Countries: France, United Kingdom, United Kingdom
    Project: EC | DARKHORIZONS (648680), EC | InvisiblesPlus (690575), UKRI | Fundamental Physics from ... (ST/P000258/1), NSF | CAREER: Illuminating the ... (1752752), EC | ELUSIVES (674896), NSF | Kavli Institute for Theor... (1748958), NSF | Beyond the Thermal WIMP P... (1720174), NSF | Collaborative Research: N... (1813834), NSF | Mathematical Sciences Res... (1440140), EC | DARKHORIZONS (648680),...

    It is commonly assumed that the energy density of the Universe was dominated by radiation between reheating after inflation and the onset of matter domination 54,000 years later. While the abundance of light elements indicates that the Universe was radiation dominated during Big Bang Nucleosynthesis (BBN), there is scant evidence that the Universe was radiation dominated prior to BBN. It is therefore possible that the cosmological history was more complicated, with deviations from the standard radiation domination during the earliest epochs. Indeed, several interesting proposals regarding various topics such as the generation of dark matter, matter-antimatter asymmetry, gravitational waves, primordial black holes, or microhalos during a nonstandard expansion phase have been recently made. In this paper, we review various possible causes and consequences of deviations from radiation domination in the early Universe - taking place either before or after BBN - and the constraints on them, as they have been discussed in the literature during the recent years. Comment: 67 pages, 18 figures. v2: Discussion and references added. Accepted for publication in The Open Journal of Astrophysics

  • Open Access English
    Authors: 
    Raivo Kolde; Jaak Vilo;
    Publisher: F1000Research
    Project: EC | ESNATS (201619)

    Functional characterisation of gene lists using Gene Ontology (GO) enrichment analysis is a common approach in computational biology, since many analysis methods end up with a list of genes as a result. Often there can be hundreds of functional terms that are significantly associated with a single list of genes and proper interpretation of such results can be a challenging endeavour. There are methods to visualise and aid the interpretation of these results, but most of them are limited to the results associated with one list of genes. However, in practice the number of gene lists can be considerably higher and common tools are not effective in such situations.We introduce a novel R package, 'GOsummaries' that visualises the GO enrichment results as concise word clouds that can be combined together if the number of gene lists is larger. By also adding the graphs of corresponding raw experimental data, GOsummaries can create informative summary plots for various analyses such as differential expression or clustering. The case studies show that the GOsummaries plots allow rapid functional characterisation of complex sets of gene lists. The GOsummaries approach is particularly effective for Principal Component Analysis (PCA).By adding functional annotation to the principal components, GOsummaries improves significantly the interpretability of PCA results. The GOsummaries layout for PCA can be effective even in situations where we cannot directly apply the GO analysis. For example, in case of metabolomics or metagenomics data it is possible to show the features with significant associations to the components instead of GO terms. The GOsummaries package is available under GPL-2 licence at Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/GOsummaries.html).

  • Open Access English
    Authors: 
    Daniel Moody; Patrick Heymans; Raimundas Matulevičius;
    Publisher: Springer Nature
    Country: Netherlands

    Goal-oriented modelling is one of the most important research developments in the requirements engineering (RE) field. This paper conducts a systematic analysis of the visual syntax of i*, one of the leading goal-oriented languages. Like most RE notations, i* is highly visual. Yet surprisingly, there has been little debate about or modification to its graphical conventions since it was proposed more than a decade ago. We evaluate the i* visual notation using a set of principles for designing cognitively effective visual notations (the Physics of Notations). The analysis reveals some serious flaws in the notation together with some practical recommendations for improvement. The results can be used to improve its effectiveness in practice, particularly for communicating with end users. A broader goal of the paper is to raise awareness about the importance of visual representation in RE research, which has historically received little attention.

  • Open Access English
    Authors: 
    Jezia Zakraoui; Samir Elloumi; Jihad Mohamad Alja'am; Sadok Ben Yahia;
    Publisher: IEEE

    In this paper, we introduce an approach to automatically convert simple modern standard Arabic children’s stories to the best representative images that can efficiently illustrate the meaning of words. It is a kind of imitating the imaginative process when children read a story, yet a great challenge for a machine to achieve it. For simplification issues, we apply several techniques to find the images and we associate them with related words dynamically. First, we apply natural language processing techniques to analyze the text in stories and we extract keywords of all characters and events in each sentence. Second, we apply an image captioning process through a pre-trained deep learning model for all retrieved images from our multimedia database as well as the Google search engine. Third, using sentence similarities, most significant images are retrieved back by selecting top- $k$ highest similarity values. It is worth mentioning that using the captioning process, to rank top- $k$ images, has shown reasonable precision values as per our preliminary results. The option to refine or validate the ranked images to compose the final visualization for each story is also provided to ensure a flexible and safe learning environment.