We propose a new microscopy simulation system that can depict atomistic models in a micrograph visual style, similar to physical electron microscopy imaging results. This system is scalable, able to represent the simulation of electron microscopy of twenty complex viral particles, and synthesizes the image faster than previous approaches. Additionally, the simulator is differentiable in the deterministic and stochastic stages that form signal and noise representations in the micrograph. This allows for solving inverse problems by means of optimization and thus allows for the generation of microscopy simulations using parameter settings estimated from real data. We demonstrate this capability through two applications: (1) estimating the parameters of the modulation transfer function defining the detector properties of the simulated and real micrographs and (2) denoising the real data based on parameters optimized from the simulated examples using gradient descent. While current simulators do not support any parameter estimation due to their forward-only design, we show that the results obtained using estimated parameters are very similar to real micrographs. Additionally, we evaluate the denoising capabilities of our approach and show that the results are competitive with state-of-the-art methods. Denoised micrographs exhibit less noise in the tomographic reconstruction of tilt-series, facilitating visualization of microscopy tomography using direct volume rendering by reducing the visual dominance of noise.

Cryo-electron tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural details. Existing volume visualization methods, however, are not able to reveal details of interest due to low signal-to-noise ratio. In order to design more powerful transfer functions, we propose leveraging soft segmentation as an explicit component of visualization for noisy volumes. Our technical realization is based on semi-supervised learning, where we combine the advantages of two segmentation algorithms. First, the weak segmentation algorithm provides good results for propagating sparse user-provided labels to other voxels in the same volume and is used to generate dense pseudo-labels. Second, the powerful deep-learning-based segmentation algorithm learns from these pseudo-labels to generalize the segmentation to other unseen volumes, a task that the weak segmentation algorithm fails at completely. The proposed volume visualization uses deep-learning-based segmentation as a component for segmentation-aware transfer function design. Appropriate ramp parameters can be suggested automatically through frequency distribution analysis. Furthermore, our visualization uses gradient-free ambient occlusion shading to further suppress the visual presence of noise, and to give structural detail the desired prominence. The cryo-ET data studied in our technical experiments are based on the highest-quality tilted series of intact SARS-CoV-2 virions. Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques.

Visualization aims to strengthen data exploration and analysis, especially for complex and high-dimensional data. High-performance computing (HPC) systems are typically large and complicated instruments that generate massive performance and operation time series. Monitoring HPC systems’ performance is a daunting task for HPC admins and researchers due to their dynamic natures. This work proposes a visual design using the bipartite graph’s idea to visualize HPC clusters’ structure, metrics, and job scheduling data. We built a web-based prototype, called JobViewer, that integrates advanced methods in visualization and human-computer interaction (HCI) to demonstrate the benefits of visualization in real-time monitoring HPC centers. We also showed real use cases and a user study to validate the efficiency and highlight the current approach’s drawbacks.

Analyzing temporal event sequences play an important role in many application domains, such as workload behavior analysis, hardware fault diagnosis, and natural disaster resilience. As data volume keeps growing, real-world temporal event sequences are often noisy, missing, and complex, thus making it a daunting task to convey much of the information from a comprehensive overview for analysts. This work proposes a visual approach based on clustering and superimposing to construct a high-level overview of sequential event data while balancing the amount of information and its cardinality. We also implement an interactive prototype, called TimeRadar, that allows domain analysts to simultaneously analyze sequence clustering, extract distribution patterns, drill multiple levels of detail to accelerate the analysis. This work aims to provide an abstracted view of temporal event sequences where significant events are highlighted. The TimeRadar is demonstrated through case studies with real-world temporal datasets of various sizes.

We present a new technique for the rapid modeling and construction of scientifically accurate mesoscale biological models. The resulting 3D models are based on a few 2D microscopy scans and the latest knowledge available about the biological entity, represented as a set of geometric relationships. Our new visual-programming technique is based on statistical and rule-based modeling approaches that are rapid to author, fast to construct, and easy to revise. From a few 2D microscopy scans, we determine the statistical properties of various structural aspects, such as the outer membrane shape, the spatial properties, and the distribution characteristics of the macromolecular elements on the membrane. This information is utilized in the construction of the 3D model. Once all the imaging evidence is incorporated into the model, additional information can be incorporated by interactively defining the rules that spatially characterize the rest of the biological entity, such as mutual interactions among macromolecules, and their distances and orientations relative to other structures. These rules are defined through an intuitive 3D interactive visualization as a visual-programming feedback loop. We demonstrate the applicability of our approach on a use case of the modeling procedure of the SARS-CoV-2 virion ultrastructure. This atomistic model, which we present here, can steer biological research to new promising directions in our efforts to fight the spread of the virus.

Understanding the status of high-performance computing platforms and correlating applications to resource usage provide insight into the interactions among platform components. A lot of efforts have been devoted into developing monitoring solutions; however, a large-scale HPC system usually requires a combination of methods/tools to successfully monitor all metrics, which will lead to a huge effort in configuration and monitoring. Besides, monitoring tools are often left behind in the procurement of large-scale HPC systems. These challenges have motivated the development of a next-generation out-of-the-box monitoring tool that can be easily deployed without losing informative metrics. In this work, we introduce MonSTer, an “out-of-the-box” monitoring tool for high-performance computing platforms. MonSTer uses the evolving specification Redfish to retrieve sensor data from Baseboard Management Controller (BMC), and resource management tools such as Univa Grid Engine (UGE) or Slurm to obtain application information and resource usage data. Additionally, it also uses a time-series database (e.g. InfluxDB) for data storage. MonSTer correlates applications to resource usage and reveals insightful knowledge without having additional overhead on the application and computing nodes. This paper presents the design and implementation of MonSTer, as well as experiences gained through real-world deployment on the 467-node Quanah cluster at Texas Tech University's High Performance Computing Center (HPCC) over the past year. In this work, we introduce MonSTer, an “out-of-the-box” monitoring tool for high-performance computing platforms. MonSTer uses the evolving specification Redfish to retrieve sensor data from Baseboard Management Controller (BMC), and resource management tools such as Univa Grid Engine (UGE) or Slurm to obtain application information and resource usage data. Additionally, it also uses a time-series database (e.g. InfluxDB) for data storage. MonSTer correlates applications to resource usage and reveals insightful knowledge without having additional overhead on the application and computing nodes. This paper presents the design and implementation of MonSTer, as well as experiences gained through real-world deployment on the 467-node Quanah cluster at Texas Tech University's High Performance Computing Center (HPCC) over the past year.

Visual analytics studies the integration of human perceptional ability and the data exploration process. Besides, high dimensional time series has become more and more popular data in daily applications and various domains. It is essential to study visual techniques for this type of data set. This paper is an applied work that extends successes of the scatterplot matrix (SPLOM) for cross-sectional data to the high dimensional temporal data sets. We have built a web-based application that contains various visual techniques to display the temporal data sets by SPLOM at each snapshot and animate the SPLOM to illustrate the evolution of the data sets. Two real data sets in different domains are utilized to demonstrate the advances of this approach.

Detecting outliers is one of the fundamental tasks in visual analytics and valuable in many application domains, such as suspicious network cyberattack recognition. This paper introduces an approach to analyzing and visualizing high-dimensional time series, focusing on identifying multivariate observations that are significantly different from the others. We also propose a prototype, called MTSAD, to guide users when interactively exploring abnormalities in large time series. The prototype contains two views: the main window provides an overview of identified outliers overtime, the detail window investigates and explores the ranked temporal data entries based on their outlying contributions to the overall plots. The visual interface supports a full range of interactions, such as lensing, brushing and linking, ranking, and filtering. To validate the benefits and usefulness of our approach, we demonstrate MTSAD on real-world datasets of different numbers of attributes.

In the paper, we focus on the statistical detection and understanding of signaling behavior via various media outlets that are associated with a violent hate crime. Following a body of theory that identifies the links between hate rhetoric with violence, we explore textual patterns of hate speech in traditional and non-traditional media outlets, including television news shows, social media, political blogs, and political forums, to detect an association with hate crime events per time and place. Applying the dynamical system theory, we can characterize the dynamic behaviors of essential keywords. Grouping keyword with similar temporal signatures enables us to highlight important information/topics corresponding to emerging events. We also introduce SpacePhaser, an interactive visual tool for textual pattern analysis. To show the usefulness of SpacePhaser, we demonstrate the results of its applications on various real-world datasets.

Analyzing public user posts and shared information on social media can assist us in measuring various population characteristics, patterns, movements, and as well as the public health conditions. In recent years, researchers have been analyzing social media (such as Facebook or Twitter feeds) to detect and predict various emerging events and market trends. Fewer attentions have been paid to the epidemic of the diseases. In this paper, we present a social media analytics tool, called HealthTvizer, for exploring health awareness using Twitter data through interactive and interconnected multiple views. We use topic modeling to pick the relevant and meaningful terms from more than 57 million tweets. We detect the disease name and related contents which are shared by the users of different geographical locations (mostly in the United States). We believe that the collected geolocations from the users' tweets can reveal the patterns of diseases for a given term which allows a researcher to detect, analyze, and explore information about the diseases and hence take necessary steps to improve public health awareness. We validate the effectiveness of HealthTvizer through an informal user study. The feedback from this study also motivates us on interesting future extensions of the tool.

The explosion of social media has paved a way for big data in which entrepreneurs use this data to find out potential customers, market demands, individual behavior, thereby to improve existing products, to create new products according to users' need, or to analyze and evaluate financial risks. The challenges of the heterogeneity and fragmentation of data make it difficult for analysts to fully exploit the benefit of deluge information. Available statistical software lacks customization and address unknown research questions. This paper proposes FinanViz, a visual analytics tool for analyzing financial news on social media. The principal aim of FinanViz is to observe the dynamic behavior of terms/words over time along with their proximity to other terms/words. The tool provides an intuitive, interactive exploration of the financial topics and what events are emerging in which we would argue that it will give hints for financial marketers in the decision making process.