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  1. Visualization design studies bring together visualization researchers and domain experts to address yet unsolved data analysis challenges stemming from the needs of the domain experts. Typically, the visualization researchers lead the design study process and implementation of any visualization solutions. This setup leverages the visualization researchers’ knowledge of methodology, design, and programming, but the availability to synchronize with the domain experts can hamper the design process. We consider an alternative setup where the domain experts take the lead in the design study, supported by the visualization experts. In this study, the domain experts are computer architecture experts who simulate and analyze novel computer chip designs. These chips rely on a Network-on-Chip (NOC) to connect components. The experts want to understand how the chip designs perform and what in the design led to their performance. To aid this analysis, we develop Vis4Mesh, a visualization system that provides spatial, temporal, and architectural context to simulated NOC behavior. Integration with an existing computer architecture visualization tool enables architects to perform deep-dives into specific architecture component behavior. We validate Vis4Mesh through a case study and a user study with computer architecture researchers. We reflect on our design and process, discussing advantages, disadvantages, and guidance for engaging in a domain expert-led design studies. 
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    Free, publicly-accessible full text available April 1, 2025
  2. Free, publicly-accessible full text available July 1, 2024

    We investigate archival Hubble Space Telescope ACS/SBC F140LP observations of NGC 1399 to search for evidence of multiple stellar populations in extragalactic globular clusters. Enhanced far-ultraviolet (FUV) populations are thought to be indicators of He-enhanced second generation populations in globular clusters, specifically extreme/blue horizontal branch stars. Out of 149 globular clusters in the field of view, 58 have FUV counterparts with magnitudes brighter than 28.5. Six of these FUV-detected globular clusters are also detected in X-rays, including one ultraluminous X-ray source (LX > 1039 erg/s). While optically bright clusters corresponded to brighter FUV counterparts, we observe FUV emission from both metal-rich and metal-poor clusters, which implies that the FUV excess is not dependent on optical colour. We also find no evidence that the cluster size influences the FUV emission. The clusters with X-ray emission are not unusually FUV bright, which suggests that even the ultraluminous X-ray source does not provide significant FUV contributions. NGC 1399 is only the fourth galaxy to have its globular cluster system probed for evidence of FUV-enhanced populations, and we compare these clusters to previous studies of the Milky Way, M31, M87, and the brightest cluster in M81. These sources indicate that many globular clusters likely host extreme HB stars and/or second generation stars, and highlight the need for more complete FUV observations of extragalactic globular cluster systems.

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    Ultraluminous X-ray sources (ULXs) in globular clusters (GCs) are low-mass X-ray binaries that achieve high X-ray luminosities through a currently uncertain accretion mechanism. Using archival Chandra and Hubble Space Telescope observations, we perform a volume-limited search (≲70 Mpc) of 21 of the most massive ($\gt 10^{11.5} \, \mathrm{M}_\odot$) early-type galaxies to identify ULXs hosted by GC candidates. We find a total of 34 ULX candidates above the expected background within five times the effective radius of each galaxy, with 10 of these ($\sim 29.4{{\ \rm per\ cent}}$) potentially hosted by a GC. A comparison of the spatial and luminosity distributions of these new candidate GC ULXs with previously identified GC ULXs shows that they are similar: both samples peak at LX ∼ a few × 1039 erg s−1 and are typically located within a few effective radii of their host galaxies.

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  5. null (Ed.)
    In response to COVID-19, a vast number of visualizations have been created to communicate information to the public. Information exposure in a public health crisis can impact people’s attitudes towards and responses to the crisis and risks, and ultimately the trajectory of a pandemic. As such, there is a need for work that documents, organizes, and investigates what COVID-19 visualizations have been presented to the public. We address this gap through an analysis of 668 COVID-19 visualizations. We present our findings through a conceptual framework derived from our analysis, that examines who, (uses) what data, (to communicate) what messages, in what form, under what circumstances in the context of COVID-19 crisis visualizations. We provide a set of factors to be considered within each component of the framework. We conclude with directions for future crisis visualization research. 
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    In the study of gene expression data, network analysis has played a uniquely important role. To accommodate the high dimensionality and low sample size and generate interpretable results, regularized estimation is usually conducted in the construction of gene expression Gaussian Graphical Models (GGM). Here we use GeO‐GGM to represent gene‐expression‐only GGM. Gene expressions are regulated by regulators. gene‐expression‐regulator GGMs (GeR‐GGMs), which accommodate gene expressions as well as their regulators, have been constructed accordingly. In practical data analysis, with a “lack of information” caused by the large number of model parameters, limited sample size, and weak signals, the construction of both GeO‐GGMs and GeR‐GGMs is often unsatisfactory. In this article, we recognize that with the regulation between gene expressions and regulators, the sparsity structures of a GeO‐GGM and its GeR‐GGM counterpart can satisfy a hierarchy. Accordingly, we propose a joint estimation which reinforces the hierarchical structure and use the construction of a GeO‐GGM to assist that of its GeR‐GGM counterpart and vice versa. Consistency properties are rigorously established, and an effective computational algorithm is developed. In simulation, the assisted construction outperforms the separation construction of GeO‐GGM and GeR‐GGM. Two The Cancer Genome Atlas data sets are analyzed, leading to findings different from the direct competitors. 
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