Multivariate volume visualization through dynamic projections

pre-printWe propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. Using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space

Further dimensions: text, typography and play in the metaverse

In this text I wish to delve into the creation of textual content as well as its visualization through typographic design mechanisms inside three dimensional virtual worlds, which are known as the metaverse. I am particularly focused upon the way in which such virtually three dimensional environments may place the usage of text within a context that stands in contradiction to its traditional one by creating an unexpected novel purpose which takes a marked departure from the intrinsic attribute with which text has inherently been associated – namely the attribute of readability. In such environments readability, or indeed even legibility, may often be displaced through the usage of text and typography as a playful device, as artifacts which may manifest in puzzle-like configurations, or as visual structures the contents of which are meant to be understood through means other than straightforward reading; thus bringing about states of heightened engagement, wonder and ‘play’ through their manipulation or indeed simply by being immersed within the spaces which are brought about through their very agency. I also wish to expand upon this subject by talking about my own experiments with this material and will conclude by positing that further virtual dimensions can be instrumental in eliciting exciting alternative usages of text and typography which bring to the fore the allographic properties of text as an artistic/creative expressive media that may well bear further scrutiny and exploration

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers. Neural architecture search (NAS), as an emerging field, has demonstrated its capabilities in discovering powerful neural network architectures, which motivates us to explore its potential for CTR predictions. Due to 1) diverse unstructured feature interactions, 2) heterogeneous feature space, and 3) high data volume and intrinsic data randomness, it is challenging to construct, search, and compare different architectures effectively for recommendation models. To address these challenges, we propose an automated interaction architecture discovering framework for CTR prediction named AutoCTR. Via modularizing simple yet representative interactions as virtual building blocks and wiring them into a space of direct acyclic graphs, AutoCTR performs evolutionary architecture exploration with learning-to-rank guidance at the architecture level and achieves acceleration using low-fidelity model. Empirical analysis demonstrates the effectiveness of AutoCTR on different datasets comparing to human-crafted architectures. The discovered architecture also enjoys generalizability and transferability among different datasets

Multi-agent evolutionary systems for the generation of complex virtual worlds

Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer's intent through interaction, and encourages playful discovery