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We introduce CosmoVis, an open source web-based visualization tool for the interactive analysis of massive hydrodynamic cosmological simulation data. CosmoVis was designed in close collaboration with astrophysicists to enable researchers and citizen scientists to share and explore these datasets, and to use them to investigate a range of scientific questions. CosmoVis visualizes many key gas, dark matter, and stellar attributes extracted from the source simulations, which typically consist of complex data structures multiple terabytes in size, often requiring extensive data wrangling. CosmoVis introduces a range of features to facilitate real-time analysis of these simulations, including the use of “virtual skewers,” simulated analogues of absorption line spectroscopy that act as spectral probes piercing the volume of gaseous cosmic medium. We explain how such synthetic spectra can be used to gain insight into the source datasets and to make functional comparisons with observational data. Furthermore, we identify the main analysis tasks that CosmoVis enables and present implementation details of the software interface and the client-server architecture. We conclude by providing details of three contemporary scientific use cases that were conducted by domain experts using the software and by documenting expert feedback from astrophysicists at different career levels. [ABSTRACT FROM AUTHOR]

We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM's self-patterning behavior, showing a wide range of continuous network-like morphologies—called polyphorms—that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept. [ABSTRACT FROM AUTHOR]

We present a state‐of‐the‐art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are classified based on five categories: data wrangling, data exploration, feature identification, object reconstruction, as well as education and outreach. Our unique contribution is to combine the diverse viewpoints from both astronomers and visualization experts to identify challenges and opportunities for visualization in astrophysics. The main goal is to provide a reference point to bring modern data analysis and visualization techniques to the rich datasets in astrophysics. [ABSTRACT FROM AUTHOR]

This paper introduces Polyphorm, an interactive visualization and model fitting tool that provides a novel approach for investigating cosmological datasets. Through a fast computational simulation method inspired by the behavior of Physarum polycephalum, an unicellular slime mold organism that efficiently forages for nutrients, astrophysicists are able to extrapolate from sparse datasets, such as galaxy maps archived in the Sloan Digital Sky Survey, and then use these extrapolations to inform analyses of a wide range of other data, such as spectroscopic observations captured by the Hubble Space Telescope. Researchers can interactively update the simulation by adjusting model parameters, and then investigate the resulting visual output to form hypotheses about the data. We describe details of Polyphorm's simulation model and its interaction and visualization modalities, and we evaluate Polyphorm through three scientific use cases that demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Fast radio burst (FRB) 190608 was detected by the Australian Square Kilometre Array Pathfinder (ASKAP) and localized to a spiral galaxy at in the Sloan Digital Sky Survey (SDSS) footprint. The burst has a large dispersion measure () compared to the expected cosmic average at its redshift. It also has a large rotation measure () and scattering timescale (τ = 3.3 ms at 1.28 GHz). Chittidi et al. perform a detailed analysis of the ultraviolet and optical emission of the host galaxy and estimate the host DM contribution to be. This work complements theirs and reports the analysis of the optical data of galaxies in the foreground of FRB 190608 in order to explore their contributions to the FRB signal. Together, the two studies delineate an observationally driven, end-to-end study of matter distribution along an FRB sightline, the first study of its kind. Combining our Keck Cosmic Web Imager (KCWI) observations and public SDSS data, we estimate the expected cosmic dispersion measure along the sightline to FRB 190608. We first estimate the contribution of hot, ionized gas in intervening virialized halos (). Then, using the Monte Carlo Physarum Machine methodology, we produce a 3D map of ionized gas in cosmic web filaments and compute the DM contribution from matter outside halos (). This implies that a greater fraction of ionized gas along this sightline is extant outside virialized halos. We also investigate whether the intervening halos can account for the large FRB rotation measure and pulse width and conclude that it is implausible. Both the pulse broadening and the large Faraday rotation likely arise from the progenitor environment or the host galaxy. [ABSTRACT FROM AUTHOR]

Neural networks are often quantized to use reduced-precision arithmetic, as it greatly improves their storage and computational costs. This approach is commonly used in image classification and natural language processing applications. However, using a quantized network for the reconstruction of HDR images can lead to a significant loss in image quality. In this paper, we introduce QW-Net, a neural network for image reconstruction, in which close to 95% of the computations can be implemented with 4-bit integers. This is achieved using a combination of two U-shaped networks that are specialized for different tasks, a feature extraction network based on the U-Net architecture, coupled to a filtering network that reconstructs the output image. The feature extraction network has more computational complexity but is more resilient to quantization errors. The filtering network, on the other hand, has significantly fewer computations but requires higher precision. Our network recurrently warps and accumulates previous frames using motion vectors, producing temporally stable results with significantly better quality than TAA, a widely used technique in current games. [ABSTRACT FROM AUTHOR]

The Classification Cube art installation invites participants to become familiar with a machine-learning classification system. Inside a private space within the gallery, participants' bodies are subjected to a classification process that detects their faces and estimates their age, gender, emotion and actions. Participants are also able to see how their own classification compares with how the installation classifies a series of animated figures. Rapidly changing results encourage participants to actively perform their behavior to the system and alter the way it "sees" them. The entanglement with the system raises awareness regarding the effectiveness of machine interpretation. [ABSTRACT FROM AUTHOR]

This article introduces Resonant Waves, a work of interactive new media art that incorporates cymatic patterns into an immersive installation. The authors describe their research and design process in creating Resonant Waves, and they discuss technical details about the installation, highlighting innovative aspects of the project and contextualizing the project in terms of previous cymatics research and related artistic explorations of complex phenomena. Finally, the authors discuss audience reaction to different installations of the project and identify directions for future research in immersive cymatics. [ABSTRACT FROM AUTHOR]

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Motivation: We present an overview of the Kappa platform, an integrated suite of analysis and visualization techniques for building and interactively exploring rule-based models. The main components of the platform are the Kappa Simulator, the Kappa Static Analyzer and the Kappa Story Extractor. In addition to these components, we describe the Kappa User Interface, which includes a range of interactive visualization tools for rule-based models needed to make sense of the complexity of biological systems. We argue that, in this approach, modeling is akin to programming and can likewise benefit from an integrated development environment. Our platform is a step in this direction. Results: We discuss details about the computation and rendering of static, dynamic, and causal views of a model, which include the contact map (CM), snaphots at different resolutions, the dynamic influence network (DIN) and causal compression. We provide use cases illustrating how these concepts generate insight. Specifically, we show how the CM and snapshots provide information about systems capable of polymerization, such as Wnt signaling. A well-understood model of the KaiABC oscillator, translated into Kappa from the literature, is deployed to demonstrate the DIN and its use in understanding systems dynamics. Finally, we discuss how pathways might be discovered or recovered from a rule-based model by means of causal compression, as exemplified for early events in EGF signaling. [ABSTRACT FROM AUTHOR]

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle. [ABSTRACT FROM PUBLISHER]

This paper examines the history of the influential Interactive Image computer graphics showcase, which took place at museum and conference venues from 1987 to 1988. The authors present a preliminary exploration of the historical contexts that led to the creation of this exhibition by the Electronic Visualization Lab (EVL), which included the integrated efforts of both artists and computer scientists. In addition to providing historical details about this event, the authors introduce a media archaeology approach for examining the cultural and technological contexts in which this event is situated. [ABSTRACT FROM AUTHOR]

Background: Understanding complicated networks of interactions and chemical components is essential to solving contemporary problems in modern biology, especially in domains such as cancer and systems research. In these domains, biological pathway data is used to represent chains of interactions that occur within a given biological process. Visual representations can help researchers understand, interact with, and reason about these complex pathways in a number of ways. At the same time, these datasets offer unique challenges for visualization, due to their complexity and heterogeneity. Results: Here, we present taxonomy of tasks that are regularly performed by researchers who work with biological pathway data. The generation of these tasks was done in conjunction with interviews with several domain experts in biology. These tasks require further classification than is provided by existing taxonomies. We also examine existing visualization techniques that support each task, and we discuss gaps in the existing visualization space revealed by our taxonomy. Conclusions: Our taxonomy is designed to support the development and design of future biological pathway visualization applications. We conclude by suggesting future research directions based on our taxonomy and motivated by the comments received by our domain experts. [ABSTRACT FROM AUTHOR]

Electromagnetic fields are formed through complex interactions between outer space, the Sun, our Earth, its atmosphere, and the built environment. Our communications technology makes use of them to enable the transmission of information at local, global, and even extraterrestrial scales. This article introduces a series of artworks that explore new creative opportunities made possible both via low-cost sensors and through the use of state-of-the-art receivers. The projects engage with the electromagnetic spectrum as a medium of creative expression that maps the invisible landscapes of what Anthony Dunne has termed "Hertzian space." [ABSTRACT FROM AUTHOR]

Abstract Study of the behavior of individual members in communities of dynamic networks can help neuroscientists to understand how interactions between neurons in brain networks change over time. Visualization of those temporal features is challenging, especially for networks embedded within spatial structures, such as brain networks. In this article, the authors present the design of SwordPlots, an interactive multi-view visualization system to assist neuroscientists in their exploration of dynamic brain networks from multiple perspectives. Their visualization helps neuroscientists to understand how the functional behavior of the brain changes over time, how such behaviors are related to the spatial structure of the brain, and how communities of neurons with similar functionality evolve over time. To evaluate their application, they asked neuroscientists to use SwordPlots to examine four different mouse brain data sets. Based on feedback, their visualization design can provide neuroscientists with the ability to gain new insights into the properties of dynamic brain networks. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the author highlights issue on topics including artworks and design projects featured in the Institute of Electrical and Electronics Engineers (IEEE) Visualization 2016 and 2017 Arts Program, VISAP'16 and VISAP'17.

We introduce NeuroCave, a novel immersive visualization system that facilitates the visual inspection of structural and functional connectome datasets. The representation of the human connectome as a graph enables neuroscientists to apply network-theoretic approaches in order to explore its complex characteristics. With NeuroCave, brain researchers can interact with the connectome-either in a standard desktop environment or while wearing portable virtual reality headsets (such as Oculus Rift, Samsung Gear, or Google Daydream VR platforms)-in any coordinate system or topological space, as well as cluster brain regions into different modules on-demand. Furthermore, a default side-by-side layout enables simultaneous, synchronized manipulation in 3D, utilizing modern GPU hardware architecture, and facilitates comparison tasks across different subjects or diagnostic groups or longitudinally within the same subject. Visual clutter is mitigated using a state-of-the-art edge bundling technique and through an interactive layout strategy, while modular structure is optimally positioned in 3D exploiting mathematical properties of platonic solids. NeuroCave provides new functionality to support a range of analysis tasks not available in other visualization software platforms., Competing Interests: Competing Interests: The authors have declared that no competing interests exist.