Revisiting the Design Patterns of Composite Visualizations

Composite visualization is a popular design strategy that represents complex datasets by integrating multiple visualizations in a meaningful and aesthetic layout, such as juxtaposition, overlay, and nesting. With this strategy, numerous novel designs have been proposed in visualization publications to accomplish various visual analytic tasks. These well-crafted composite visualizations have formed a valuable collection for designers and researchers to address real-world problems and inspire new research topics and designs. However, there is a lack of understanding of design patterns of composite visualization, thus failing to provide holistic design space and concrete examples for practical use. In this paper, we opted to revisit the composite visualizations in VIS publications and answered what and how visualizations of different types are composed together. To achieve this, we first constructed a corpus of composite visualizations from IEEE VIS publications and decomposed them into a series of basic visualization types (e.g., bar chart, map, and matrix). With this corpus, we studied the spatial (e.g., separated or overlaying) and semantic relationships (e.g., with same types or shared axis) between visualizations and proposed a taxonomy consisting of eight different design patterns (e.g., repeated, stacked, accompanied, and nested). Furthermore, we analyzed and discussed common practices of composite visualizations, such as the distribution of different patterns and correlations between visualization types. From the analysis and examples, we obtained insights into different design patterns on the utilities, advantages, and disadvantages. Finally, we developed an interactive system to help visualization developers and researchers conveniently explore collected examples and design patterns

Strain fields of Ms >6.0 earthquakes in Menyuan, Qinghai, China

In predicting earthquakes, it is a major challenge to capture the time factor and spatial isoline anomalies, and understand their physical processes, of the seismic strain field before a strong earthquake. In this study, the seismic strain field was used as representative of seismic activity. The natural orthogonal function expansion method was used to calculate the seismic strain field before the Menyuan Ms 6.4 earthquakes in 1986 and 2016, and the Ms 6.9 earthquake in 2022. Time factor and spatial isoline anomaly of the strain field before each earthquake was extracted. We also compared the evolution of the strain field with numerical simulation results under the tectonic stress system at the source. The results showed that the time factor before the earthquakes had high or low value anomalies, exceeding the mean square error of the stable background. The anomalies were concentrated in the first four typical fields of the strain field, which has multiple components. The abnormal contribution rate of the first typical field is the largest (accounting for 42%–49% of the total field). The long- and medium-term anomalies appear 3-4, and 1-2 years before the earthquake, respectively. There were no short or immediate-term anomalies within 3 months of the earthquake. In addition, during the evolution of the strain field, the abnormal area of the spatial isoline changed with the change in time. Usually, the intersection area of the two isoseismic lines of strain accumulation and strain release becomes a potential location for strong earthquakes. Finally, we found that the high strain field values of the 1986 and 2016 Ms 6.4 earthquakes were equivalent to the numerical simulation results, while the high strain field values of the 2022 Menyuan Ms 6.9 earthquakes were slightly different, but within the accepted error range. These results indicate that the two methods are consistent. We have shown that the natural orgthagonal method can be used to obtain the spatiotemporal anomaly information of strain field preceding strong earthquakes

Structural phase transitions in ionic conductor Bi 2 O 3 by temperature dependent XPD and XAS

The superionic behavior of cubic δ-phase Bi2O3, a metastable phase at high temperature, is of great interests from both scientific and technological perspectives. With the highest ionic conductivity among all known compounds, the δ-phase Bi2O3 possesses promising applications in solid-oxide fuel cells. Previous investigations pointed out the α to δ- phase transition occurs during the heating process, as supported by the X-ray and Neutron diffraction experiments. Through in situ measurements of the long-range order structure and the local structure by X-ray powder diffraction and X-ray absorption spectroscopy, we investigated the evolution of the structures under different temperatures. Both techniques provided ample evidence that the existence of meta-stable β-phase are crucial for forming the defective fluorite cubic δ phase. Our finding suggested that the phase transition from tetragonal β-phase to δ-phase is an influencing factor for the generation of the oxygen-ion pathways

Review Zeolite-based Materials for Gas Sensors

Abstract: This review of 53 references deals with the uses of zeolites and zeolite-based materials for developing gas sensors. The potential of these materials is highlighted and avenues for further research are suggested

Multi-Sensor Fusion Self-Supervised Deep Odometry and Depth Estimation

This paper presents a new deep visual-inertial odometry and depth estimation framework for improving the accuracy of depth estimation and ego-motion from image sequences and inertial measurement unit (IMU) raw data. The proposed framework predicts ego-motion and depth with absolute scale in a self-supervised manner. We first capture dense features and solve the pose by deep visual odometry (DVO), and then combine the pose estimation pipeline with deep inertial odometry (DIO) by the extended Kalman filter (EKF) method to produce the sparse depth and pose with absolute scale. We then join deep visual-inertial odometry (DeepVIO) with depth estimation by using sparse depth and the pose from DeepVIO pipeline to align the scale of the depth prediction with the triangulated point cloud and reduce image reconstruction error. Specifically, we use the strengths of learning-based visual-inertial odometry (VIO) and depth estimation to build an end-to-end self-supervised learning architecture. We evaluated the new framework on the KITTI datasets and compared it to the previous techniques. We show that our approach improves results for ego-motion estimation and achieves comparable results for depth estimation, especially in the detail area

Cubic sinusoidal phase mask: Another choice to extend the depth of field of incoherent imaging system

Wave-front coding is a well known technique used to extend the depth of field of incoherent imaging system. The core of this technique lies in the design of suitable phase masks, among which the most important one is the cubic phase mask suggested by Dowski and Cathey (1995) [1]. In this paper, we propose a new type called cubic sinusoidal phase mask which is generated by combing the cubic one and another component having the sinusoidal form. Numerical evaluations and real experimental results demonstrate that the composite phase mask is superior to the original cubic phase mask with parameters optimized and provides another choice to achieve the goal of depth extension. (C) 2009 Elsevier Ltd. All rights reserved

Reverse time migration of multiples for subsalt imaging

Some hydrocarbon reservoirs are trapped beneath salt bodies, where seismic imaging is greatly challenged due to poor illumination. Multiple reflections have different propagation wave paths from primary reflections and thus can be used to complement the illuminations where primary reflections from beneath the salt are not acquired. Consequently, migration of multiples can sometimes provide better subsalt images compared to conventional migration which uses primary reflections only. In this paper, we propose to modify conventional reverse time migration so that multiples can be used as constructive reflection energy for subsalt imaging. This new approach replaces the impulsive source wavelet with the recorded data containing both primaries and multiples and uses predicted multiples as the input data instead of primary reflections. In the reverse time migration process, multiples recorded on the surface are extrapolated backward in time to each depth level, and the observed data with both primaries and multiples are extrapolated forward in time to the same depth levels, followed by a crosscorrelation imaging condition. A numerical test on the Sigsbee2B data set shows that a wider coverage and a more balanced illumination of the subsurface can be achieved by migration of multiples compared with conventional migration of primary reflections. This example demonstrates that reverse time migration of multiples might be a promising method for complex subsalt imaging.National Natural Science Foundation (China) (grant no. 40930421)National Natural Science Foundation (China) (grant no. 40830422)National Natural Science Foundation (China) (grant no. 40874068)National Basic Research Program of China (973 Program, grant No. 2009CB219404