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Workshop: I’m a scientist, not a writer!

  The measure of science is citations. Whether experimental results get called significant or whether new lines of research get taken up depends on the number of citations an article receives. And it's always articles. The primary outlet of scientific research is the journal, and a journal's Impact Factor (its index for frequency of article […]

Online-Vortrag zum Thema „Asteroseismologie“

online

Wie sehen die Sterne am Nachthimmel eigentlich unter ihrer leuchtenden Oberfläche aus? Lange Zeit konnten wir über das Innere von Sternen nur spekulieren. Heutzutage jedoch kann die Wissenschaft die Struktur eines Sterns messen – mit neuen Methoden wie der Asteroseismologie. Wie genau das funktioniert, erklärt die neue HITS Gruppenleiterin Saskia Hekker bei ihrem Online-Vortrag am […]

Siobhan Roberts: Embracing the Uncertainties

  In her talk, Siobhan Roberts will reflect on her pandemic reporting and the importance of uncertainty in science. She will discuss epistemic uncertainty, a lack of knowledge about facts and numbers pertaining to the past and present, versus aleatory uncertainty, unknowns about the future due to randomness and chance. She will also explore the […]

Biological Diffusion and Brownian Dynamics Brainstorm 5 (BDBDB5)

  Diese Veranstaltung findet auf Englisch statt. Biological Diffusion and Brownian Dynamics Brainstorm 5 (BDBDB5: https://bdbdb.h-its.org/) will take place in virtual format on 24-25 March 2021. We will have four exciting talks by invited speakers - Aleksei Aksimentiev (University of Illinois at Urbana-Champaign, USA), Gerhard Hummer (Max Planck Institute for Biophysics, Frankfurt, Germany), Syma Khalid (University […]

Kolloquium Lillian T. Chong: Weighted ensemble simulations of long-timescale dynamics: From chemical reactions to SARS-CoV-2

By Lillian Chong, Department of Chemistry, University of Pittsburgh, USA   The weighted ensemble (WE) path sampling strategy orchestrates multiple simulations in parallel with rigorous statistical resampling at fixed time intervals to maintain rigorous kinetics. WE simulations can be orders of magnitude more efficient than standard simulations in generating unbiased, atomically detailed pathways for rare […]

Kolloquium Alina Schadwinkel: Science journalism in Corona times

By Alina Schadwinkel, Spektrum der Wissenschaft Verlagsgesellschaft mbH, Heidelberg What the heck is a coronavirus ? How do I protect myself? How safe and effective are the new vaccines - and when will the pandemic finally be over? For more than a year, Sars-CoV-2 has dominated the media. People are demanding information. As quickly as […]

Kolloquium Warner Marzocchi: Model validation in natural hazard forecasting – a scientific perspective

  By Warner Marzocchi, Unversity of Naples, Federico II, Italy Science is rooted in the concept that a model can be tested against independent observations and rejected when necessary. However, the problem of model testing becomes formidable when we consider natural "open" systems. Owing to their scale, complexity, and openness to interactions within a larger […]

Kolloquium Yuji Sugita: Parallel computing algorithms in molecular dynamics simulations for extremely large-scale biological systems

  By Yuji Sugita, RIKEN, Theoretical Molecular Science, Japan Molecular dynamics (MD) simulations of nano- and biomolecules are one of the most important techniques in computational science. Protein conformational changes and protein-ligand bindings are simulated with all-atom MD simulations to elucidate molecular mechanisms underlying these biological processes. Conventionally, we can perform MD simulations of a […]

Symmetric Spaces for Graph Embeddings

Abstract: Learning faithful graph representations as sets of vertex embeddings has become a fundamental intermediary step in a wide range of machine learning applications. We propose the systematic use of symmetric spaces in representation learning, a class encompassing many of the previously used embedding targets. This enables us to introduce new methods for graph embeddings […]

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