Funding for research with Azure services

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• Marina Dütsch | FLEXWEB

  Organisational unit: Department of Meteorology and Geophysics

  Abstract (in German):

  Flexpart (FLEXible PARTicle dispersion model) ist ein numerisches Modell, das die Ausbreitung von Gasen und Aerosolen in der Atmosphäre simuliert. Das Modell wird am Institut für Meteorologie und Geophysik weiterentwickelt, und kommt in verschiedenen internationalen und nationalen Forschungsprojekten zum Einsatz. Einige Anwendungsfälle sind z.B. die Bestimmung von Treibhausgas-Emissionen oder Transport von Mikroplastik, sowie Ausbreitungsrechnungen bei nuklearen Störfällen (z.B. CTBTO).

  Damit Flexpart verwendet werden kann, muss es auf einem (Super-)computer installiert und ausgeführt werden. Das ist allerdings mit Hürden verbunden, denn einerseits haben nicht alle Wissenschaftler*innen Zugang zu einem Supercomputer, und andererseits gibt es bei der Installation oder Ausführung oft technische Probleme. In diesem Projekt wollen wir deshalb ein Flexpart Web Service (FLEXWEB) entwickeln, bei dem Flexpart über eine Webseite laufen gelassen werden kann.

  Das Projekt soll ein Testprojekt für ein späteres operationelles Service sein. Flexpart soll mit Hilfe eines Kubernetes Clusters in der Cloud Trajektorien berechnen und den Usern diese Ergebnisse leicht zugänglich machen. Sobald die Simulation fertig ist, sollen die Output-Dateien zum Download bereitgestellt und graphisch dargestellt werden. Damit hoffen wir, den Zugang zu Flexpart für Wissenschaftler*innen weltweit zu vereinfachen.

  Flexpart Entwicklung an der Universität Wien

  Category: Geförderte Forschungsprojekte 4.000,00 Euro
• Wolfgang Klas | FactCheck

  Organisational unit: Multimedia Information Systems research group, Faculty of Computer Science

  Abstract:

  FactCheck is an internal research project of the Research Group Multimedia Information Systems, Faculty of Computer Science, that aims to compare and signal conflicts in information available on the Web. This information, which may be available in textual form (e.g., paragraphs in an HTML document) or multimedia form (e.g., news segments in video form), shall be extracted using a combination of approaches from the Semantic Web (e.g., structured data) and state-of-the-art AI technologies and concepts (e.g., named entity recognition or entity linking). The comparison processes for this information will be partially driven by human intelligence and human feedback, which is why approaches for user identities and user management (e.g., Azure Entra ID) will also be investigated. For the deployment of the FactCheck prototype(s), a hybrid approach is considered, which allows for the use of both scalable Azure services (e.g., cognitive services like AI Video Indexer and user management) as well as available on-premises infrastructure (e.g., VMs or databases) at the University of Vienna to achieve suitable tradeoffs in terms of security, privacy, and costs. To keep the deployment highly flexible and modular, parts of this deployment may be containerized, thus simplifying deployment on both Azure and local infrastructure. 

  Category: Geförderte Forschungsprojekte 4.000,00 Euro
• Oliver Wieder | Revolutionizing Olfactory Perception Mapping: A Contrastive Learning Graph Neural Network Approach

  Organisational unit: Department of Pharmaceutical Sciences

  Abstract:

  This project proposes a groundbreaking approach to understanding olfactory perceptions by developing a novel computational model that maps chemical structures to olfactory characteristics. Leveraging the advanced techniques of contrastive learning and graph neural networks (GNNs), the project aims to overcome the limitations of current olfactory perception studies, which predominantly rely on subjective human olfactory tests. The core objective is to create a GNN model that accurately represents the complex geometries and properties of small molecules in an embedding space. This space will then be used to fine-tune an odor classifier, significantly enhancing its predictive accuracy. A key innovation of this project is the integration of attention mechanisms to elucidate the role of functional groups in odor perception, a facet largely unexplored in existing research. A significant outcome of this project will be the development of an interactive online dashboard. This platform will enable industry professionals and researchers to visualize and interact with the olfactory map, inputting their compounds and receiving insights into their olfactory characteristics. This tool is expected to have substantial applications in various industries, particularly in the development of products like mosquito repellants. Backed by promising literature in the fields of contrastive learning of small molecules and deep-learning approaches to odor mapping, this project stands on the cusp of a significant breakthrough in olfactory science. It promises not only to advance our fundamental understanding of how chemical structures translate into olfactory experiences but also to transform industries that rely on these insights.

  Category: Geförderte Forschungsprojekte 4.000,00 Euro

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• Marina Dütsch | FLEXWEB

  Organisational unit: Department of Meteorology and Geophysics

  Abstract (in German):

  Flexpart (FLEXible PARTicle dispersion model) ist ein numerisches Modell, das die Ausbreitung von Gasen und Aerosolen in der Atmosphäre simuliert. Das Modell wird am Institut für Meteorologie und Geophysik weiterentwickelt, und kommt in verschiedenen internationalen und nationalen Forschungsprojekten zum Einsatz. Einige Anwendungsfälle sind z.B. die Bestimmung von Treibhausgas-Emissionen oder Transport von Mikroplastik, sowie Ausbreitungsrechnungen bei nuklearen Störfällen (z.B. CTBTO).

  Damit Flexpart verwendet werden kann, muss es auf einem (Super-)computer installiert und ausgeführt werden. Das ist allerdings mit Hürden verbunden, denn einerseits haben nicht alle Wissenschaftler*innen Zugang zu einem Supercomputer, und andererseits gibt es bei der Installation oder Ausführung oft technische Probleme. In diesem Projekt wollen wir deshalb ein Flexpart Web Service (FLEXWEB) entwickeln, bei dem Flexpart über eine Webseite laufen gelassen werden kann.

  Das Projekt soll ein Testprojekt für ein späteres operationelles Service sein. Flexpart soll mit Hilfe eines Kubernetes Clusters in der Cloud Trajektorien berechnen und den Usern diese Ergebnisse leicht zugänglich machen. Sobald die Simulation fertig ist, sollen die Output-Dateien zum Download bereitgestellt und graphisch dargestellt werden. Damit hoffen wir, den Zugang zu Flexpart für Wissenschaftler*innen weltweit zu vereinfachen.

  Flexpart Entwicklung an der Universität Wien

  Category: Geförderte Forschungsprojekte 4.000,00 Euro
• Wolfgang Klas | FactCheck

  Organisational unit: Multimedia Information Systems research group, Faculty of Computer Science

  Abstract:

  FactCheck is an internal research project of the Research Group Multimedia Information Systems, Faculty of Computer Science, that aims to compare and signal conflicts in information available on the Web. This information, which may be available in textual form (e.g., paragraphs in an HTML document) or multimedia form (e.g., news segments in video form), shall be extracted using a combination of approaches from the Semantic Web (e.g., structured data) and state-of-the-art AI technologies and concepts (e.g., named entity recognition or entity linking). The comparison processes for this information will be partially driven by human intelligence and human feedback, which is why approaches for user identities and user management (e.g., Azure Entra ID) will also be investigated. For the deployment of the FactCheck prototype(s), a hybrid approach is considered, which allows for the use of both scalable Azure services (e.g., cognitive services like AI Video Indexer and user management) as well as available on-premises infrastructure (e.g., VMs or databases) at the University of Vienna to achieve suitable tradeoffs in terms of security, privacy, and costs. To keep the deployment highly flexible and modular, parts of this deployment may be containerized, thus simplifying deployment on both Azure and local infrastructure. 

  Category: Geförderte Forschungsprojekte 4.000,00 Euro
• Oliver Wieder | Revolutionizing Olfactory Perception Mapping: A Contrastive Learning Graph Neural Network Approach

  Organisational unit: Department of Pharmaceutical Sciences

  Abstract:

  This project proposes a groundbreaking approach to understanding olfactory perceptions by developing a novel computational model that maps chemical structures to olfactory characteristics. Leveraging the advanced techniques of contrastive learning and graph neural networks (GNNs), the project aims to overcome the limitations of current olfactory perception studies, which predominantly rely on subjective human olfactory tests. The core objective is to create a GNN model that accurately represents the complex geometries and properties of small molecules in an embedding space. This space will then be used to fine-tune an odor classifier, significantly enhancing its predictive accuracy. A key innovation of this project is the integration of attention mechanisms to elucidate the role of functional groups in odor perception, a facet largely unexplored in existing research. A significant outcome of this project will be the development of an interactive online dashboard. This platform will enable industry professionals and researchers to visualize and interact with the olfactory map, inputting their compounds and receiving insights into their olfactory characteristics. This tool is expected to have substantial applications in various industries, particularly in the development of products like mosquito repellants. Backed by promising literature in the fields of contrastive learning of small molecules and deep-learning approaches to odor mapping, this project stands on the cusp of a significant breakthrough in olfactory science. It promises not only to advance our fundamental understanding of how chemical structures translate into olfactory experiences but also to transform industries that rely on these insights.

  Category: Geförderte Forschungsprojekte 4.000,00 Euro