A5074348487- Astro and Planetary Science
- Planetary Science and Exploration
- Astrophysics and Star Formation Studies
- Adversarial Robustness in Machine Learning
- Stellar, planetary, and galactic studies
- Scientific Research and Discoveries
- 3D Shape Modeling and Analysis
- Blood groups and transfusion
- Ethics and Social Impacts of AI
- Immunodeficiency and Autoimmune Disorders
- Explainable Artificial Intelligence (XAI)
- Calibration and Measurement Techniques
- Astronomical Observations and Instrumentation
- Fluid Dynamics Simulations and Interactions
- Anatomy and Medical Technology
- Geological and Geophysical Studies
- Astronomy and Astrophysical Research
- Medical Image Segmentation Techniques
- Advanced X-ray and CT Imaging
- Machine Learning and Data Classification
- Blood transfusion and management
- Geological and Geochemical Analysis
- Artificial Intelligence in Healthcare and Education
- Mineral Processing and Grinding
- Nuclear reactor physics and engineering
VRVis GmbH (Austria)
2023
Johannes Kepler University of Linz
2019-2021
University of Vienna
1987-2019
Conseil de L'Europe
1988
Artificial Intelligence is one of the fastest growing technologies 21st century and accompanies us in our daily lives when interacting with technical applications. However, reliance on such systems crucial for their widespread applicability acceptance. The societal tools to express are usually formalized by lawful regulations, i.e., standards, norms, accreditations, certificates. Therefore, T\"UV AUSTRIA Group cooperation Institute Machine Learning at Johannes Kepler University Linz,...
Context: The study of stellar structure and evolution depends crucially on accurate parameters. photometry from space telescopes has provided superb data that allowed asteroseismic characterisation thousands stars. However, typical targets are rather faint complementary measurements difficult to obtain. On the other hand, brightest, otherwise well-studied stars, lacking seismic characterization. Aims: Our goal is use granulation and/or oscillation time scales measured photometric series...
Quantitative measurement of crystals in highresolution images allows for important insights into underlying material characteristics.Deep learning has shown great progress vision-based automatic crystal size measurement, but current instance segmentation methods reach their limits with that have large variation or hard to detect boundaries.Even small image errors, such as incorrectly fused separated segments, can significantly lower the accuracy measured results.Instead improving existing...
ABSTRACT The last phase of the formation rocky planets is dominated by collisions among Moon- to Mars-sized planetary embryos. Simulations this need handle difficulty including post-impact material without saturating numerical integrator. A common approach include collision-generated clustering it into few bodies with same mass and uniformly scattering them around collision point. However, oversimplifies properties neglecting features that can play important roles in final structure...
IntroductionThe final phase of planet formation involves the growth planetesimals through pairwise collisions bodies up to size planets in protoplanetary discs. Studying this phase, numerical methods are used investigate and thus planets.N-body simulations can be model dynamical evolution circumstellar disks, including [1]. Gravitational N-body models fail as soon touch, hence for treating must added. A general flexible approach by Burger et al. [1] resolves running dedicated Smoothed...
Efficient and fast reconstruction of anatomical structures plays a crucial role in clinical practice. Minimizing retrieval processing times not only potentially enhances swift response decision-making critical scenarios but also supports interactive surgical planning navigation. Recent methods attempt to solve the medical shape problem by utilizing implicit neural functions. However, their performance suffers terms generalization computation time, metric for real-time applications. To...
In this work we address flexibility in deep learning by means of transductive reasoning. For adaptation to new tasks or data, existing methods typically involve tuning learnable parameters even complete re-training from scratch, rendering such approaches unflexible practice. We argue that the notion separating computation memory transduction can act as a stepping stone for solving these issues. therefore propose PARMESAN (parameter-free search and transduction), scalable method which...
A febrile transfusion reaction caused by strong isoagglutinins in the patients serum is reported. The resulted from a of group platelets 0 patient; recipients’ contained high titered isohemagglutinin (Anti-A 1:8192) capable lysing blood-group A<sub>1</sub> red cells up to titer 1:8. Moreover, showed positive thrombocytotoxic with donor and some other individuals, but remained negative 0, turned samples after neutralization bloodgroup substance. We conclude that pre-transfusion...
In this work we describe a genetic algorithm which is used in order to study orbits of minor bodies the frames close encounters. We find that combination with standard orbital numerical integrators can be as good proxy for finding typical encounters planets and even their moons, saving lot computational time compared t0 long-term integrations. Here, Centaurs Callisto Ganymede particular. also perform n-body simulations comparison. impact velocities between v rel = 20[v esc ] 30[v 25[v 35[v Callisto.
Fast and accurate treatment of collisions in the context modern N-body planet formation simulations remains a challenging task due to inherently complex collision processes. We aim tackle this problem with machine learning (ML), particular via residual neural networks. Our model is motivated by underlying physical processes data-generating process allows for flexible prediction post-collision states. demonstrate that our outperforms commonly used handling methods such as perfect inelastic...