Recent interior carving methods for functional design necessitate a cumbersome cut-and-glue process in fabrication. We propose a method to generate interior voids which not only satisfy the functional purposes but are also support-free during the 3D printing process. We introduce a support-free unit structure for voxelization and derive the wall thicknesses parametrization for continuous optimization. We also design a discrete dithering algorithm to ensure the printability of ghost voxels. The interior voids are iteratively carved by alternating the optimization and dithering. We apply our method to optimize the static and rotational stability, and print various results to evaluate the efficacy.

In this paper we describe a study on image grid display with automatic vertical scrolling. While scroll operations are normally carried out manually by the user, in the context of RSVP (Rapid Serial Visual Presentation) techniques this work considers a presentation mode in which the image grid is automatically scrolled. Through experiments carried out with 50 testers, we have investigated user performance while looking for specific target subjects within large collections of images. Different numbers of columns and scrolling speeds have been considered. The search task implied both clicking on the identified target pictures and simply vocally stating their visual recognition. To this purpose, and to identify possible specific gaze behaviours, eye tracking technology has been exploited. The obtained results show that number of columns and scroll speed do affect search performance. Moreover, the user’s gaze tends to focus on different screen areas depending on the values of these two parameters. Although it is not possible to definitely find an optimal columns–speed combination that is valid in all cases, the particular context of use can suggest feasible solutions according to one’s needs. To the best of our knowledge, image grid display with automatic scrolling has never been studied to date.

Effective analysis of large text collections remains a challenging problem given the growing volume of available text data. Recently, text mining techniques have been rapidly developed for automatically extracting key information from massive text data. Topic modeling, as one of the novel techniques that extracts a thematic structure from documents, is widely used to generate text summarization and foster an overall understanding of the corpus content. Although powerful, this technique may not be directly applicable for general analytics scenarios since the topics and topic–document relationship are often presented probabilistically in models. Moreover, information that plays an important role in knowledge discovery, for example, times and authors, is hardly reflected in topic modeling for comprehensive analysis. In this paper, we address this issue by presenting a visual analytics system, VISTopic, to help users make sense of large document collections based on topic modeling. VISTopic first extracts a set of hierarchical topics using a novel hierarchical latent tree model (HLTM) (Liu et al., 2014). In specific, a topic view accounting for the model features is designed for overall understanding and interactive exploration of the topic organization. To leverage multi-perspective information for visual analytics, VISTopic further provides an evolution view to reveal the trend of topics and a document view to show details of topical documents. Three case studies based on the dataset of IEEE VIS conference demonstrate the effectiveness of our system in gaining insights from large document collections.

Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems. Dramatic advances in big data analytics have led to a wide variety of interactive model analysis tasks. In this paper, we present a comprehensive analysis and interpretation of this rapidly developing area. Specifically, we classify the relevant work into three categories: understanding, diagnosis, and refinement. Each category is exemplified by recent influential work. Possible future research opportunities are also explored and discussed.

The advanced telecom technologies and massive volumes of intelligent mobile phone users have yielded a huge amount of real-time data of people’s all-in-one telecommunication records, which we call telco big data. With telco data and the domain knowledge of an urban city, we are now able to analyze the movement and contact patterns of humans in an unprecedented scale. Flow map is widely used to display the movements of humans from one single source to multiple destinations by representing locations as nodes and movements as edges. However, it fails the task of visualizing both movement and contact data. In addition, analysts often need to compare and examine the patterns side by side, and do various quantitative analysis. In this work, we propose a novel spatio-temporal flow map layout to visualize when and where people from different locations move into the same places and make contact. We also propose integrating the spatiotemporal flow maps into existing spatiotemporal visualization techniques to form a suite of techniques for visualizing the movement and contact patterns. We report a potential application the proposed techniques can be applied to. The results show that our design and techniques properly unveil hidden information, while analysis can be achieved efficiently.