Pathology-Aware Deep Network Visualization and Its Application in Glaucoma Image Synthesis

2019 ◽  
pp. 423-431 ◽  
Author(s):  
Xiaofei Wang ◽  
Mai Xu ◽  
Liu Li ◽  
Zulin Wang ◽  
Zhenyu Guan
Keyword(s):  
Image Synthesis ◽  
Network Visualization ◽  
Deep Network

Document- and Keyword-based Author Co-citation Analysis

2018 ◽  
Vol 2 (2) ◽  
pp. 70-82 ◽  
Author(s):  
Binglu Wang ◽  
Yi Bu ◽  
Win-bin Huang
Keyword(s):  
Citation Analysis ◽  
Semantic Similarity ◽  
Method Validation ◽  
New Method ◽  
Global Network ◽  
Network Visualization ◽  
Knowledge Domain ◽  
Knowledge Domains ◽  
Domain Mapping ◽  
The Relationship

AbstractIn the field of scientometrics, the principal purpose for author co-citation analysis (ACA) is to map knowledge domains by quantifying the relationship between co-cited author pairs. However, traditional ACA has been criticized since its input is insufficiently informative by simply counting authors’ co-citation frequencies. To address this issue, this paper introduces a new method that reconstructs the raw co-citation matrices by regarding document unit counts and keywords of references, named as Document- and Keyword-Based Author Co-Citation Analysis (DKACA). Based on the traditional ACA, DKACA counted co-citation pairs by document units instead of authors from the global network perspective. Moreover, by incorporating the information of keywords from cited papers, DKACA captured their semantic similarity between co-cited papers. In the method validation part, we implemented network visualization and MDS measurement to evaluate the effectiveness of DKACA. Results suggest that the proposed DKACA method not only reveals more insights that are previously unknown but also improves the performance and accuracy of knowledge domain mapping, representing a new basis for further studies.

Image Synthesis by combining the Wavelets and Inverse Approaches

1999 ◽  
Vol 19 (Supplement1) ◽  
pp. 87-90
Author(s):  
D. SEKIJIMA ◽  
S. HAYANO ◽  
Y. SAITO ◽  
T.L. KUNII
Keyword(s):  
Image Synthesis

Mathematics of networks

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Random Walks ◽  
Adjacency Matrix ◽  
Graph Partitioning ◽  
Dynamic Networks ◽  
Graph Laplacian ◽  
Network Visualization ◽  
Final Part ◽  
Bipartite Networks ◽  
Component Structure ◽  
Basic Properties

An introduction to the mathematical tools used in the study of networks. Topics discussed include: the adjacency matrix; weighted, directed, acyclic, and bipartite networks; multilayer and dynamic networks; trees; planar networks. Some basic properties of networks are then discussed, including degrees, density and sparsity, paths on networks, component structure, and connectivity and cut sets. The final part of the chapter focuses on the graph Laplacian and its applications to network visualization, graph partitioning, the theory of random walks, and other problems.

Network Visualization

2020 ◽  
pp. 351-367
Author(s):  
James Moody ◽  
Ryan Light
Keyword(s):  
Best Practices ◽  
Social Relationships ◽  
Visual Display ◽  
Network Visualization ◽  
Making Sense ◽  
Complex Information ◽  
Underlying Network ◽  
History Of ◽  
Effective Network ◽  
Data Visualizations

This chapter provides an overview of social network visualization. Network analysis encourages the visual display of complex information, but effective network diagrams, like other data visualizations, result from several best practices. After a brief history of network visualization, the chapter outlines several of those practices. It highlights the role that network visualizations play as heuristics for making sense of networked data and translating complicated social relationships, such as those that are dynamic, into more comprehensible structures. The goal in this chapter is to help identify the methods underlying network visualization with an eye toward helping users produce more effective figures.

Towards landmarks prediction with Deep Network

2018 ◽  
Author(s):  
Van Linh Le ◽  
M. Beurton-Aimar ◽  
A. Zemmari ◽  
N. Parisey
Keyword(s):  
Deep Network

scholarly journals ccNetViz: a WebGL-based JavaScript library for visualization of large networks

2020 ◽  
Vol 36 (16) ◽  
pp. 4527-4529
Author(s):  
Ales Saska ◽  
David Tichy ◽  
Robert Moore ◽  
Achilles Rasquinha ◽  
Caner Akdas ◽  
...  
Keyword(s):  
Systems Biology ◽  
Complex Networks ◽  
Open Source ◽  
High Speed ◽  
A Priori ◽  
Supplementary Information ◽  
Network Visualization ◽  
Supplementary Data ◽  
Web Based ◽  
Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Resolved spectral variations of the centimetre-wavelength continuum from the ρ Oph W photo-dissociation-region

2021 ◽  
Author(s):  
Simon Casassus ◽  
Matías Vidal ◽  
Carla Arce-Tord ◽  
Clive Dickinson ◽  
Glenn J White ◽  
...  
Keyword(s):  
Molecular Cloud ◽  
Dust Emission ◽  
Image Synthesis ◽  
Microwave Emission ◽  
Radio Continuum ◽  
Continuum Emission ◽  
Parametric Image ◽  
Physical Conditions ◽  
Morphological Differences ◽  
Dust Grain

Abstract Cm-wavelength radio continuum emission in excess of free-free, synchrotron and Rayleigh-Jeans dust emission (excess microwave emission, EME), and often called ‘anomalous microwave emission’, is bright in molecular cloud regions exposed to UV radiation, i.e. in photo-dissociation regions (PDRs). The EME correlates with IR dust emission on degree angular scales. Resolved observations of well-studied PDRs are needed to compare the spectral variations of the cm-continuum with tracers of physical conditions and of the dust grain population. The EME is particularly bright in the regions of the ρ Ophiuchi molecular cloud (ρ Oph) that surround the earliest type star in the complex, HD 147889, where the peak signal stems from the filament known as the ρ Oph-W PDR. Here we report on ATCA observations of ρ Oph-W that resolve the width of the filament. We recover extended emission using a variant of non-parametric image synthesis performed in the sky plane. The multi-frequency 17 GHz to 39 GHz mosaics reveal spectral variations in the cm-wavelength continuum. At ∼30 arcsec resolutions, the 17-20 GHz intensities follow tightly the mid-IR, Icm∝I(8 μm), despite the breakdown of this correlation on larger scales. However, while the 33-39 GHz filament is parallel to IRAC 8 μm, it is offset by 15–20 arcsec towards the UV source. Such morphological differences in frequency reflect spectral variations, which we quantify spectroscopically as a sharp and steepening high-frequency cutoff, interpreted in terms of the spinning dust emission mechanism as a minimum grain size acutoff ∼ 6 ± 1 Å that increases deeper into the PDR.

scholarly journals Visual Thinking of Neural Networks: Interactive Text to Image Synthesis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hyunhee Lee ◽  
Gyeongmin Kim ◽  
Yuna Hur ◽  
Heuiseok Lim
Keyword(s):  
Neural Networks ◽  
Image Synthesis ◽  
Visual Thinking ◽  
Interactive Text
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