Comparing tensors and identifying their (dis)similar structures is fundamental in understanding the underlying phenomena for complex data. Tensor decomposition methods help analysts extract tensors' essential characteristics and aid in visual analytics for tensors. In contrast to dimensionality reduction (DR) methods designed only for analyzing a matrix (i.e., second-order tensor), existing tensor decomposition methods do not support flexible comparative analysis. To address this analysis limitation, we introduce a new tensor decomposition method, named tensor unified linear comparative analysis (TULCA), by extending its DR counterpart, ULCA, for tensor analysis. TULCA integrates discriminant analysis and contrastive learning schemes for tensor decomposition, enabling flexible comparison of tensors. We also introduce an effective method to visualize a core tensor extracted from TULCA into a set of 2D visualizations. We integrate TULCA's functionalities into a visual analytics interface to support analysts in interpreting and refining the TULCA results. We demonstrate the efficacy of TULCA and the visual analytics interface with computational evaluations and two case studies, including an analysis of log data collected from a supercomputer.

One advancing machine-learning-based analysis approach for multivariate time-series data is representing data as a third-order tensor and then applying dimensionality reduction (DR) methods. In this work, we introduce a visual analytics method that employs multiple interactive DR methods to support both extraction and interpretation of latent patterns of multivariate time-series data. Our method first allows analysts to select an analysis focus from three axes: instance, variable, and time axes. Then, the method applies a multi-step DR method to produce a 2D scatterplot that depicts latent patterns of the selected axis’s elements (e.g., time points). Afterward, the analysts interactively investigate data groups that appeared in the plot with a DR method designed for comparative analysis. The method can be further applied iteratively to perform more precise and detailed analyses. We implement a prototype system and demonstrate the effectiveness of our method by analyzing supercomputer log data.

We present a visual analytic system that can quantitatively can evaluate the tree images from the Baum test, which assesses psychological traits of subjects. The system quantifies the tree features in the images and creates an overview plot of the set of the tree images by using dimensionality reduction techniques. The system then identifies psychological traits by analyzing the formed clusters using numerical data obtained from additional psychological evaluations and statistical measures, and integrating these traits with the tree features. Experimental results demonstrate the effectiveness of the system in several cases. However, the system has some limitations, such as challenges in analyzing scenarios where statistically significant differences are not observed, and more detailed feature analysis of the tree images is required for complex psychological state analysis.

With the recent developments in computational science and HPC technology, large-scale numerical simulations have become common in various scientific and technological fields. The output volume data from these simulations have also become larger and more complex, creating a problem for the time-consuming input/output to/from the HPC storage system. To solve this problem, in-situ visualization has been used. However, the output data for posterior analysis is usually a large set of image data obtained from the visualization, and there is a lack of interactivity compared to the conventional analysis, which loads the volume data from the storage, after the simulation, and executes interactive visual exploration. To compensate for these problems, in-situ visualization often places multiple viewpoints in the simulation space and generates images from all of them. However, this may result in a huge number of images, and as a result, this can require time and effort to locate important visualization images that can provide clues to obtain knowledge during the analysis. To solve this problem, this study estimates the regions where important changes occur in the simulation, based on information entropy calculated from the visualization images, and generates a sequence of animated images focusing on these regions. In-situ visualization has widely been recognized as an effective approach for analyzing large-scale simulation outputs from modern HPC systems by reducing the inherent I/O bottleneck problem. However, batch-based in-situ visualization, such as the image- and video-based approaches, can produce large amounts of rendering results for the subsequent offline visual analysis. Therefore, this can make it difficult to gain rapid insight into the simulation results during post-hoc visual analysis. To minimize this problem, we have worked on a smart visualization approach focusing on extracting a set of images that may facilitate the rapid understanding of the underlying simulated phenomena as an alternative to accelerate the process of obtaining scientific knowledge. In this work, we present a method for automatically adjusting the camera focus point and zoom level during in-situ visualization in an attempt to obtain the most suitable rendering images for facilitating visual analysis. We integrated the proposed method with the existing in-situ smooth camera path estimation framework, for evaluation purposes, and used two CFD simulation codes and two HPC systems (x86-based server system and Arm-based Fugaku supercomputer) for the evaluations. We obtained encouraging results from the preliminary evaluations, and we are planning further improvements by working closely with domain expert collaborators.

Energy efficiency has become a serious concern when running applications on HPC systems. Although these systems were designed to mainly run simulation codes as fast as possible, due to the ever-increasing size of the simulation outputs, the in situ visualization has gained increasing attention. In situ visualization uses the same HPC system to execute a part or even the entire visualization processing, and there are currently a variety of tools and libraries, that facilitate domain scientists to integrate them with their simulation codes. Among different approaches, image- and video-based in situ visualization has been widely adopted as an effective approach for the subsequent offline visual analysis. In this approach, a large number of renderings are required at every visualization time step and can consume a considerable computational resource. Fugaku adopted PowerAPI which enables the users to set the power mode for their jobs. However, simulation and visualization codes may have different processing behaviors requiring different power settings for obtaining the most energy-efficient runnings. In this work, we tried to shed light on the energy efficiency of the visualization portion that was not considered before. We investigated the computational cost and energy consumption of some rendering techniques by using the PowerAPI and KVS (Kyoto Visualization System) on the Fugaku, and hope that the obtained findings will be useful for potential users looking to run in situ visualization on the Fugaku and other PowerAPI-enabled HPC systems.

Many real-world applications involve analyzing time-dependent phenomena, which are intrinsically functional, consisting of curves varying over a continuum (e.g., time). When analyzing continuous data, functional data analysis (FDA) provides substantial benefits, such as the ability to study the derivatives and to restrict the ordering of data. However, continuous data inherently has infinite dimensions, and for a long time series, FDA methods often suffer from high computational costs. The analysis problem becomes even more challenging when updating the FDA results for continuously arriving data. In this paper, we present a visual analytics approach for monitoring and reviewing time series data streamed from a hardware system with a focus on identifying outliers by using FDA. To perform FDA while addressing the computational problem, we introduce new incremental and progressive algorithms that promptly generate the magnitude-shape (MS) plot, which conveys both the functional magnitude and shape outlyingness of time series data. In addition, by using an MS plot in conjunction with an FDA version of principal component analysis, we enhance the analyst's ability to investigate the visually-identified outliers. We illustrate the effectiveness of our approach with two use scenarios using real-world datasets. The resulting tool is evaluated by industry experts using real-world streaming datasets.

Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to uncover the intrinsic structure and features of the data. However, DR is usually applied to a subset of data that is either single-time-point multivariate or univariate time-series, resulting in the need to manually examine and correlate the DR results out of different data subsets. When the number of dimensions is large either in terms of the number of time points or attributes, this manual task becomes too tedious and infeasible. In this paper, we present MulTiDR, a new DR framework that enables processing of time-dependent multivariate data as a whole to provide a comprehensive overview of the data. With the framework, we employ DR in two steps. When treating the instances, time points, and attributes of the data as a 3D array, the first DR step reduces the three axes of the array to two, and the second DR step visualizes the data in a lower-dimensional space. In addition, by coupling with a contrastive learning method and interactive visualizations, our framework enhances analysts' ability to interpret DR results. We demonstrate the effectiveness of our framework with four case studies using real-world datasets.

In this paper, we present a study on the available open-source software (OSS) for large-scale data visualization on the SPARC64fx based HPC systems, such as the K computer and also the Fujitsu PRIMEHPC FX family of supercomputers (FX10 and FX100), which are commonly available throughout Japan. It is widely known that these HPC systems have been generating a vast amount of simulation results in a wide range of science and engineering fields. However, there was no much information regarding the large-scale data visualization software and approaches in such HPC infrastructure. In this work, we focused on the visualization approaches where the HPC hardware resources are directly used for the visualization processing, which can be helpful to minimize the large data transfer issue for the visualization and analysis purposes. This study includes both OpenGL (Open Graphics Library) and non-OpenGL based visualization approaches, and also the availability of the GLSL (OpenGL Shading Language) handling functionalities. Although it is a short survey focusing only on the post-processing issue, we expect that this study can be useful and helpful for the current and future potential users of the SPARC64fx CPU based HPC systems, which are still in active use throughout Japan.

Welcome to the proceedings of the IEEE Pacific Visualization Symposium 2017 (IEEE PacificVis 2017), tenth in a series of successful events that have been sponsored by the IEEE Computer Society Visualization and Graphics Technical Committee (VGTC). Past IEEE PacificVis symposia were held in Kyoto (2008), Beijing (2009), Taipei (2010), Hong Kong (2011), Songdo (2012), Sydney (2013), Yokohama (2014), Hangzhou (2015), and Taipei (2016). This year, PacificVis is held at Seoul, Korea from April 18 to 21, 2017, hosted by Seoul National University.

The primary challenge in the visualization of a large-scale unstructured volume data with mixed cell types is to dissolve a bottleneck caused by visibility sorting of unstructured cells. In this paper, we implement an interactive particle-based rendering method to solve this complex visualization problem. This technique uses opaque particles as the proxy geometry of the mixed unstructured cells so that the visibility sorting process is not needed. This characteristic makes the rendering of mixed-cell unstructured volume efficient. We also construct a resizing function to adjust the particle radius depending on the assigned transfer function so that the transfer function can be adjusted in real time. Furthermore, we develop a time-varying level-of-detail (LOD) rendering to efficiently handle the large-scale time-varying data. This LOD rendering can provide high-speed rendering for animation rendering and high-quality rendering when the animation is stopped at any time step of interest. These features facilitate the detailed analysis of the temporal features of the data.

Edge concentration in dense bipartite graphs is a technique for reducing the numbers of edges and edge crossings in graph drawings. The conventional method proposed by Newbery is designed to reduce the number of edge crossings; however, it does not always reduce the number of edges. Reducing the number of edges is also an important factor for improving the readability of graphs. However, no edge concentration method with the explicit purpose of minimizing the number of edges has previously been studied. In this study, we propose a novel, efficient heuristic method for minimizing the number of edges during edge concentration. We demonstrate the efficiency of the proposed method via a comparison using randomly generated graphs. We find that Newbery's method fails to reduce the number of edges when the number of vertices is large. By contrast, the proposed method achieves an average compression ratio of 47 to 82 percent for all generated graph groups. We also present a real-world application of the proposed method using a causality network of biological data.

In this paper, we propose a technique for rendering multiple irregular volumes using a stochastic approach. In this approach, we extend our stochastic projected tetrahedral (SPT) algorithm to treat irregular volumes. The SPT algorithm is a sorting-free volume rendering algorithm that projects a tetrahedral cell onto an image plane and controls the particle rendering using the opacity as the probability in the rasterization process. In the rasterization process, the particle depth is determined as that of a pre-defined location, which may be the front, back or middle point on a ray segment in the SPT algorithm. This determination causes a prominent artifact when treating multiple volumes, although it does not cause any problem when treating a single volume. The artifact occurs because the SPT algorithm controls the particle stochastically in the image plane but not in the depth direction. In the volume rendering process, it is assumed that the number of particles follows a Poisson distribution along the segment of the viewing ray that intersects the tetrahedral cell. The particle spacing follows the exponential distribution. In this case, we construct a cumulative distribution function of the particle spacing and develop a technique for calculating the nearest particle along the interval. We apply this technique to multiple volumes to confirm its effectiveness.

We propose a new application of stochastic point-based rendering, which was recently proposed for implicit surfaces, to large-scale laser-scanned 3D point data. Specifically, we propose a scheme to apply the rendering to transparent and fused visualization of recent large and complex laser-scanned data from cultural assets. Our scheme uses 3D points that are directly acquired using a laser scanner as the rendering primitives. For laser-scanned data that consist of more than 107 or 108 3D points, the pre-processing stage takes only a few minutes, and the rendering stage is executable at interactive frame rates. We do not encounter rendering artifacts originating from the indefiniteness of depth-sorted orders of rendering primitives. Fused visualization with various visual assistants is also possible. We demonstrate the effectiveness of our scheme by visualizing a campus building and a culturally important festival float.

In this study, we propose a novel integrated visualization system that enables interactive visual development of a Habitat Suitability Index (HSI) model for predicting the locations of fishing grounds. Our system enables the interactive selection of variables that are highly correlated to fish catches recorded by fishermen on their vessels. We illustrate the use of this system using a real-world simulation of the Pacific Ocean. Vortex structures, such as those used for fishing grounds exploration, are crucial for exploring fishing ground because they can drive nutrients from the ocean floor to the sea surface. The ability to locate fishing grounds therefore depends on accurate ocean forecasting systems for calculating ocean model variables such as temperature, salinity, velocity etc. Currently, these forecasts are based on multiple spatio-temporal simulations that produce multidimensional and multivariate results. Critical points in the ocean model are good indicators of the likelihood of finding microscale vortices, and our visualization approach enables the interactive exploration and analysis of these critical points. The proposed system makes it possible to analyze the vortex structure in the spatial domain, and explore fishing grounds on the sea surface in detail.

We report the system that collects the data from the sensor network and visualizes the data on real time by three dimensions on a computer. It becomes possible for this system to make a user make the measurement data on space intuitive.

We consider that eye-to-eye contact during face-to-face communication is important to understand each other in everyday communication. However, 3D display usually requires deflection glasses and a head mount display (HMD), and it is difficult to carry out eye-to-eye contact in such a condition. To support face-to-face communication by group in tele-immersive environment, we constructed 3D display environment using a merged video image obtained from the multi-viewpoint videos merging system set up in the actual space. In this paper, we have conducted experiments on the possibility of eye-to-eye contact by the multi-viewpoint videos merging system with auto-stereoscopic display. From experimental results, we have proved that two estimators could realize eye-to-eye contact at each direction for a gazer on auto-stereoscopic display. In addition, we have showed that it is effective to locate cameras at the center or at the lower half of display in order to feel eye-to-eye contact.

In the present paper, we introduce a novel point-based volume rendering technique based on particle generation from user-specified transfer function. In the proposed technique, a set of tiny particles is generated from a given 3D scalar field. This particle generation process is based on a user-specified transfer function and rejection method. These particles are then projected onto the image plane to generate the final image. The main characteristic of the proposed technique is that the particle projection order is independent and unfixed because the transparency values of the particles are not taken into account. Therefore, only the depth-order comparison between the particles is required during the projection stage, which can greatly facilitate the distributed processing. When the quantity of projected particles is small, for instance, a maximum of one per pixel area, it becomes difficult to achieve semi-transparency, which is the main characteristic of volume rendering. To overcome this problem, sub-pixel processing is applied in order to allow the projection of multiple particles onto each of the pixel areas. The final pixel value is then obtained by averaging the contribution from each of these projected particles. The use of the Metropolis method for particle generation is also investigated as an alternative method for further improving the image quality