Hierarchical Correlation Clustering in Multiple 2D Scalar Fields

Sets of multiple scalar fields can be used to model many types of variation in data, such as uncertainty in measurements and simulations or time‐dependent behavior of scalar quantities. Many structural properties of such fields can be explained by dependencies between different points in the scalar field. Although these dependencies can be of arbitrary complexity, correlation, i.e., the linear dependency, already provides significant structural information. Existing methods for correlation analysis are usually limited to positive correlation, handle only local dependencies, or use combinatorial approximations to this continuous problem. We present a new approach for computing and visualizing correlated regions in sets of 2‐dimensional scalar fields. This paper describes the following three main contributions: (i) An algorithm for hierarchical correlation clustering resulting in a dendrogram, (ii) a generalization of topological landscapes for dendrogram visualization, and (iii) a new method for incorporating negative correlation values in the clustering and visualization. All steps are designed to preserve the special properties of correlation coefficients. The results are visualized in two linked views, one showing the cluster hierarchy as 2D landscape and the other providing a spatial context in the scalar field's domain. Different coloring and texturing schemes coupled with interactive selection support an exploratory data analysis.     

Towards patient‐centered packaging design: An industry perspective on processes,functions, and constraints

Medication packaging is essential to provide patients with guidance and correct use of their medicines for effective treatment. This research aims to increase knowledge about the medication packaging innovation process and its uptake towards patient‐centered packaging design. The study applied a qualitative research approach based on data from 25 in‐depth interviews with stakeholders involved in medication packaging design. The empirical data analysis revealed four themes that can improve and advance user‐centered packaging design: medication packaging innovation process, medication packaging functions and features, medication packaging design constraints, and patient‐centered medication packaging design. The findings suggest that medication packaging design is strongly affected by an emphasis on protective and safety packaging functions rather than on patients' needs. Packaging innovation usually is constrained by rigid incremental development processes, where compliance with regulations, extensive documentation, avoidance of manufacturing complexity, and considerations on cost prevail. These findings are discussed in relation to the three most evident trade‐offs for patient‐centered design: protection versus openability, utility versus cost, and complexity of manufacturability versus complexity of use. This research contributes with valuable input and additional evidence about the necessary shift to a user‐centered approach in a field that has not been design driven. This input complements previous research and provides an opportunity for industry decision makers and policy makers to lead patient‐centered packaging design that can benefit patients and relieve overloaded health care systems.     

Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization

The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep‐neural‐network‐based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back‐end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.     

ChronoCorrelator: Enriching Events with Time Series

Event sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.     

Topic Tomographies (TopTom): a visual approach to distill information from media streams

In this paper we present Top Tom, a digital platform whose goal is to provide analytical and visual solutions for the exploration of a dynamic corpus of user‐generated messages and media articles, with the aim of i) distilling the information from thousands of documents in a low‐dimensional space of explainable topics, ii) cluster them in a hierarchical fashion while allowing to drill down to details and stories as constituents of the topics, iii) spotting trends and anomalies. Top Tom implements a batch processing pipeline able to run both in near‐real time with time stamped data from streaming sources and on historical data with a temporal dimension in a cold start mode. The resulting output unfolds along three main axes: time, volume and semantic similarity (i.e. topic hierarchical aggregation). To allow the browsing of data in a multiscale fashion and the identification of anomalous behaviors, three visual metaphors were adopted from biological and medical fields to design visualizations, i.e. the flowing of particles in a coherent stream, tomographic cross sectioning and contrast‐like analysis of biological tissues. The platform interface is composed by three main visualizations with coherent and smooth navigation interactions: calendar view, flow view, and temporal cut view. The integration of these three visual models with the multiscale analytic pipeline proposes a novel system for the identification and exploration of topics from unstructured texts. We evaluated the system using a collection of documents about the emerging opioid epidemics in the United States.     

A Review of Guidance Approaches in Visual Data Analysis: A Multifocal Perspective

Visual data analysis can be envisioned as a collaboration of the user and the computational system with the aim of completing a given task. Pursuing an effective system‐user integration, in which the system actively helps the user to reach his/her analysis goal has been focus of visualization research for quite some time. However, this problem is still largely unsolved. As a result, users might be overwhelmed by powerful but complex visual analysis systems which also limits their ability to produce insightful results. In this context, guidance is a promising step towards enabling an effective mixed‐initiative collaboration to promote the visual analysis. However, the way how guidance should be put into practice is still to be unravelled. Thus, we conducted a comprehensive literature research and provide an overview of how guidance is tackled by different approaches in visual analysis systems. We distinguish between guidance that is provided by the system to support the user, and guidance that is provided by the user to support the system. By identifying open problems, we highlight promising research directions and point to missing factors that are needed to enable the envisioned human‐computer collaboration, and thus, promote a more effective visual data analysis.     

CV3: Visual Exploration,Assessment, and Comparison of CVs

The Curriculum Vitae (CV, also referred to as “résumé”) is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatio‐temporal, nominal, hierarchical, and ordinal data. The main task of a recruiter is, given a job application with specific requirements, to compare and assess CVs in order to build a short list of promising candidates to interview. Commonly, this is done by viewing CVs in a side‐by‐side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention between them. Furthermore, there is no guarantee that the CVs are structured similarly, thus making the overview cluttered and significantly slowing down the comparison process. In order to address these challenges, in this paper we propose “CV3”, an interactive exploration environment offering users a new way to explore, assess, and compare multiple CVs, to suggest suitable candidates for specific job requirements. We validate our system by means of domain expert feedback whose results highlight both the efficacy of our approach and its limitations. We learned that CV3 eases the overall burden of recruiters thereby assisting them in the selection process.     

A Generalized Cubemap for Encoding 360° VR Videos using Polynomial Approximation

360^° VR videos provide users with an immersive visual experience. To encode 360^° VR videos, spherical pixels must be mapped onto a two‐dimensional domain to take advantage of the existing video encoding and storage standards. In VR industry, standard cubemap projection is the most widely used projection method for encoding 360^° VR videos. However, it exhibits pixel density variation at different regions due to projection distortion. We present a generalized algorithm to improve the efficiency of cubemap projection using polynomial approximation. In our algorithm, standard cubemap projection can be regarded as a special form with 1st‐order polynomial. Our experiments show that the generalized cubemap projection can significantly reduce the projection distortion using higher order polynomials. As a result, pixel distribution can be well balanced in the resulting 360^° VR videos. We use PSNR, S‐PSNR and CPP‐PSNR to evaluate the visual quality and the experimental results demonstrate promising performance improvement against standard cubemap projection and Google's equi‐angular cubemap.     

Robin边界条件下光学CT正向问题的数值模拟

文章研究了Robin边界条件下光学层析成像的正向算法,进行了理论分析,给出了实 验结果,实验结果符合理论分析的结果。并对将Robin边界条件和Dirichlet边界条件下的结果进行比较、分析,得出两者的异同点。     

CYCLIC DEFORMATION OF FACE CENTERED CUBIC CRYSTALS AND ITS DISLOCATION INTERACTION MODEL——Ⅱ.DISLOCATION INTERACTION MODEL OF CYCLIC DEFORMATION

A dislocation interaction model has been proposed for cyclic deformation of fcc crystals.Ac-cording to this model,cyclic stress-strain responses and saturation dislocation structures of acrystal are associated with the modes and intensities of dislocation interactions between slipsystems active in the crystal; and,hence,may be predicted by the location of its tensile axis inthe crystallographic triangle.This model has successfully explained the different behaviours ofdouble-slip crystals and multi-slip behaviours of some crystals with orientations usually con-sidered as single-slip ones.