Abstract The Internet of Vehicles (IoV) can obtain traffic information through a large number
of data collected by sensors. However, the lack of data, abnormal data, and other low-quality …

Memory and network bandwidth are decisive bottlenecks when handling high-resolution
multidimensional data sets in visualization applications, and they increasingly demand …

To address the problem of ever-growing scientific data sizes making data movement a major
hindrance to analysis, we introduce a novel encoding for scalar fields: a unified tree of …

Abstract Distributed Denial of Service (DDoS) attacks are one of the most important security
threats, since multiple compromised systems perform massive attacks over a victim …

Tensor decompositions, in particular the Tucker model, are a powerful family of techniques
for dimensionality reduction and are being increasingly used for compactly encoding large …

Existing techniques for the simultaneous encryption and compression of three-dimensional
(3D) image sequences (eg, video sequences, medical image sequences) may come with …

Since the early 1990s, methods for the acquisition of three-dimensional (3-D) data and
computer-assisted techniques for the visualization of such data have grown increasingly …

There currently exist two dominant strategies to reduce data sizes in analysis and
visualization: reducing the precision of the data, eg, through quantization, or reducing its …

Learning neural fields has been an active topic in deep learning research, focusing, among
other issues, on finding more compact and easy-to-fit representations. In this paper, we …

We propose an end-to-end trainable framework that processes large-scale visual data
tensors by looking at a fraction of their entries only. Our method combines a neural network …