Convolutional neural networks (CNNs) are popularly used in a wide range of applications, such as computer vision, natural language processing, and human-computer interaction. However, testing and understanding a trained ...

The timeslice is the predominant method for drawing and visualizing dynamic graphs. However, when nodes and edges have real coordinates along the time axis, it becomes difficult to organize them into discrete timeslices, ...

General visualization recommendation systems typically make design decisions of the dataset automatically. However, these systems are only able to prune meaningless visualizations but fail to recommend targeted results. ...

Hilbert and Moore treemaps are based on the same named space-filling curves to lay out tree-structured data for visualization. One main component of them is a partitioning subroutine, whose algorithmic complexity poses ...

Anomaly detection has gained increasing attention from researchers in recent times. Owing to a lack of reliable ground-truth labels, many current state-of-art techniques focus on unsupervised learning, which lacks a mechanism ...

This paper presents an in situ visualization algorithm for neural network training. As each training data item leads to multiple hidden variables when being forward-propagated through a neural network, our algorithm first ...

For direct volume rendering of post-classified data, we propose an algorithm that analytically splits a ray through a cubical cell at the control points of a piecewise-polynomial transfer function. This splitting generates ...

Stress and strain tensors, two well-known quantities in mechanical engineering, are linked through a fourth-order stiffness tensor, which is not considered by many visualizations due to its complexity. Considering an ...

Spatiotemporal measurements such as power consumption, temperature, humidity, movement, noise, brightness, etc., will become ubiquitously available in both old and modern homes to capture and analyze behavioral patterns. ...