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Jose Rodrigues

Publications and source records attributed to Jose Rodrigues.

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Optical Discovery of New and Candidate Galactic Supernova Remnants Plus Optical Imaging of the Monogem Supernova Remnant

We report the discovery of new and candidate Galactic supernova remnants made through over 2000 hours of H$\alpha$ and [O III] imaging using amateur class instrumentation. These nebulae range from 0.5 to over 8 degrees in size. Five of these, namely G27.8-17.1, G115.6+9.2, G190.5+25.3, G195.9+2.2 and G239.9+7.0 exhibit low-dispersion optical spectra indicating shock emission by the presence of strong [S II] $\lambda\lambda$6716,6731 and [O I] $\lambda$6300 line emissions relative to H$\alpha$, or exhibit a nonradiative shock generated Balmer dominated spectrum. Their filamentary emission morphology also supports the presence of interstellar shock fronts. Three other optical nebulae, G191.3+11.1, G205.7-1.7, and G305.4-0.7 appear likely to be remnants and are proposed as new SNR candidates. We also present [O III] and H$\alpha$ images of optical filaments associated with the 25 degree diameter Monogem Ring SNR which reveal extensive emission-line filaments around most of the remnant's X-ray boundaries. Deep images of the X-ray suspected remnant G190.4+12.5 and the large and [O III] emission dominated SNR G206.6+6.1 seen toward or near the Monogem SNR are also included. Key findings of this work include: 1) optical surveys can sometimes detect SNRs better than radio and X-ray SNR searches, 2) Balmer nonradiative shocks appear not uncommon in high Galactic latitude SNRs, and 3) faint \O3 emission dominated SNRs may have been missed in prior H$\alpha$ emission only surveys.

astro-ph.HE

Combining Visual Analytics and Content Based Data Retrieval Technology for Efficient Data Analysis

One of the most useful techniques to help visual data analysis systems is interactive filtering (brushing). However, visualization techniques often suffer from overlap of graphical items and multiple attributes complexity, making visual selection inefficient. In these situations, the benefits of data visualization are not fully observable because the graphical items do not pop up as comprehensive patterns. In this work we propose the use of content-based data retrieval technology combined with visual analytics. The idea is to use the similarity query functionalities provided by metric space systems in order to select regions of the data domain according to user-guidance and interests. After that, the data found in such regions feed multiple visualization workspaces so that the user can inspect the correspondent datasets. Our experiments showed that the methodology can break the visual analysis process into smaller problems (views) and that the views hold the expectations of the analyst according to his/her similarity query selection, improving data perception and analytical possibilities. Our contribution introduces a principle that can be used in all sorts of visualization techniques and systems, this principle can be extended with different kinds of integration visualization-metric-space, and with different metrics, expanding the possibilities of visual data analysis in aspects such as semantics and scalability.

cs.GR

A Survey on Distributed Visualization Techniques over Clusters of Personal Computers

In the last years, Distributed Visualization over Personal Computer (PC) clusters has become important for research and industrial communities. They have made large-scale visualizations practical and more accessible. In this work we survey Distributed Visualization techniques aiming at compiling last decade's literature on the use of PC clusters as suitable alternatives to high-end workstations. We review the topic by defining basic concepts, enumerating system requirements and implementation challenges, and presenting up-to-date methodologies. Our work fulfills the needs of newcomers and seasoned professionals as an introductory compilation at the same time that it can help experienced personnel by organizing ideas.

cs.GR

StructMatrix: large-scale visualization of graphs by means of structure detection and dense matrices

Given a large-scale graph with millions of nodes and edges, how to reveal macro patterns of interest, like cliques, bi-partite cores, stars, and chains? Furthermore, how to visualize such patterns altogether getting insights from the graph to support wise decision-making? Although there are many algorithmic and visual techniques to analyze graphs, none of the existing approaches is able to present the structural information of graphs at large-scale. Hence, this paper describes StructMatrix, a methodology aimed at high-scalable visual inspection of graph structures with the goal of revealing macro patterns of interest. StructMatrix combines algorithmic structure detection and adjacency matrix visualization to present cardinality, distribution, and relationship features of the structures found in a given graph. We performed experiments in real, large-scale graphs with up to one million nodes and millions of edges. StructMatrix revealed that graphs of high relevance (e.g., Web, Wikipedia and DBLP) have characterizations that reflect the nature of their corresponding domains; our findings have not been seen in the literature so far. We expect that our technique will bring deeper insights into large graph mining, leveraging their use for decision making.

cs.SI

SuperGraph Visualization

Given a large social or computer network, how can we visualize it, find patterns, outliers, communities? Although several graph visualization tools exist, they cannot handle large graphs with hundred thousand nodes and possibly million edges. Such graphs bring two challenges: interactive visualization demands prohibitive processing power and, even if we could interactively update the visualization, the user would be overwhelmed by the excessive number of graphical items. To cope with this problem, we propose a formal innovation on the use of graph hierarchies that leads to GMine system. GMine promotes scalability using a hierarchy of graph partitions, promotes concomitant presentation for the graph hierarchy and for the original graph, and extends analytical possibilities with the integration of the graph partitions in an interactive environment.

cs.SI

Techniques for effective and efficient fire detection from social media images

Social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. However, much of the data from social media are images, which are uploaded in a rate that makes it impossible for human beings to analyze them. Despite the many works on image analysis, there are no fire detection studies on social media. To fill this gap, we propose the use and evaluation of a broad set of content-based image retrieval and classification techniques for fire detection. Our main contributions are: (i) the development of the Fast-Fire Detection method (FFDnR), which combines feature extractor and evaluation functions to support instance-based learning, (ii) the construction of an annotated set of images with ground-truth depicting fire occurrences -- the FlickrFire dataset, and (iii) the evaluation of 36 efficient image descriptors for fire detection. Using real data from Flickr, our results showed that FFDnR was able to achieve a precision for fire detection comparable to that of human annotators. Therefore, our work shall provide a solid basis for further developments on monitoring images from social media.

cs.CV

GMine: A System for Scalable, Interactive Graph Visualization and Mining

Several graph visualization tools exist. However, they are not able to handle large graphs, and/or they do not allow interaction. We are interested on large graphs, with hundreds of thousands of nodes. Such graphs bring two challenges: the first one is that any straightforward interactive manipulation will be prohibitively slow. The second one is sensory overload: even if we could plot and replot the graph quickly, the user would be overwhelmed with the vast volume of information because the screen would be too cluttered as nodes and edges overlap each other. GMine system addresses both these issues, by using summarization and multi-resolution. GMine offers multi-resolution graph exploration by partitioning a given graph into a hierarchy of com-munities-within-communities and storing it into a novel R-tree-like structure which we name G-Tree. GMine offers summarization by implementing an innovative subgraph extraction algorithm and then visualizing its output.

cs.SI