EG2019

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https://diglib.eg.org/handle/10.2312/2632746

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Browsing EG2019 by Subject "CCS Concepts"

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    Cytosplore: Interactive Visual Single-Cell Profiling of the Immune System
    (The Eurographics Association, 2019) Höllt, Thomas; Pezzotti, Nicola; van Unen, Vincent; Li, Na; Koning, Frits; Eisemann, Elmar; Lelieveldt, Boudewijn P. F.; Vilanova, Anna; Bruckner, Stefan and Oeltze-Jafra, Steffen
    Recent advances in single-cell acquisition technology have led to a shift towards single-cell analysis in many fields of biology. In immunology, detailed knowledge of the cellular composition is of interest, as it can be the cause of deregulated immune responses, which cause diseases. Similarly, vaccination is based on triggering proper immune responses; however, many vaccines are ineffective or only work properly in a subset of those who are vaccinated. Identifying differences in the cellular composition of the immune system in such cases can lead to more precise treatment. Cytosplore is an integrated, interactive visual analysis framework for the exploration of large single-cell datasets. We have developed Cytosplore in close collaboration with immunology researchers and several partners use the software in their daily workflow. Cytosplore enables efficient data analysis and has led to several discoveries alongside high-impact publications.
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    Learning Generative Models of 3D Structures
    (The Eurographics Association, 2019) Chaudhuri, Siddhartha; Ritchie, Daniel; Xu, Kai; Zhang, Hao (Richard); Jakob, Wenzel and Puppo, Enrico
    Many important applications demand 3D content, yet 3D modeling is a notoriously difficult and inaccessible activity. This tutorial provides a crash course in one of the most promising approaches for democratizing 3D modeling: learning generative models of 3D structures. Such generative models typically describe a statistical distribution over a space of possible 3D shapes or 3D scenes, as well as a procedure for sampling new shapes or scenes from the distribution. To be useful by non-experts for design purposes, a generative model must represent 3D content at a high level of abstraction in which the user can express their goals-that is, it must be structure-aware. In this tutorial, we will take a deep dive into the most exciting methods for building generative models of both individual shapes as well as composite scenes, highlighting how standard data-driven methods need to be adapted, or new methods developed, to create models that are both generative and structure-aware. The tutorial assumes knowledge of the fundamentals of computer graphics, linear algebra, and probability, though a quick refresher of important algorithmic ideas from geometric analysis and machine learning is included. Attendees should come away from this tutorial with a broad understanding of the historical and current work in generative 3D modeling, as well as familiarity with the mathematical tools needed to start their own research or product development in this area.