How time weathers galaxies: The temporal impact of the cluster environment on galaxy formation and evolution

Kavli Affiliate: Mark Vogelsberger | First 5 Authors: Stephanie O’Neil, Josh Borrow, Mark Vogelsberger, Hanzhang Zhao, Bing Wang | Summary: We illuminate the altered evolution of galaxies in clusters compared to the field by tracking galaxies in the IllustrisTNG300 simulation as they enter isolated clusters of mass $10^{13} < M_{rm 200, mean} / {rm M}_odot […]


Continue.. How time weathers galaxies: The temporal impact of the cluster environment on galaxy formation and evolution

How time weathers galaxies: The temporal impact of the cluster environment on galaxy formation and evolution

Kavli Affiliate: Mark Vogelsberger | First 5 Authors: Stephanie O’Neil, Josh Borrow, Mark Vogelsberger, Hanzhang Zhao, Bing Wang | Summary: We illuminate the altered evolution of galaxies in clusters compared to the field by tracking galaxies in the IllustrisTNG300 simulation as they enter isolated clusters of mass $10^{13} < M_{rm 200, mean} / {rm M}_odot […]


Continue.. How time weathers galaxies: The temporal impact of the cluster environment on galaxy formation and evolution

How time weathers galaxies: The temporal impact of the cluster environment on galaxy formation and evolution

Kavli Affiliate: Mark Vogelsberger | First 5 Authors: Stephanie O’Neil, Josh Borrow, Mark Vogelsberger, Hanzhang Zhao, Bing Wang | Summary: We illuminate the altered evolution of galaxies in clusters compared to the field by tracking galaxies in the IllustrisTNG300 simulation as they enter isolated clusters of mass $10^{13} < M_{rm 200, mean} / {rm M}_odot […]


Continue.. How time weathers galaxies: The temporal impact of the cluster environment on galaxy formation and evolution

Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks

Kavli Affiliate: Max Tegmark | First 5 Authors: Ziming Liu, Mikail Khona, Ila R. Fiete, Max Tegmark, | Summary: Recurrent neural networks (RNNs) trained on compositional tasks can exhibit functional modularity, in which neurons can be clustered by activity similarity and participation in shared computational subtasks. Unlike brains, these RNNs do not exhibit anatomical modularity, […]


Continue.. Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks

Variability as a predictor for the hard-to-soft state transition in GX 339-4

Kavli Affiliate: Erin Kara | First 5 Authors: Matteo Lucchini, Marina Ten Have, Jingyi Wang, Jeroen Homan, Erin Kara | Summary: During the outbursts of black hole X-ray binaries (BHXRBs), their accretion flows transition through several states. The source luminosity rises in the hard state, dominated by non-thermal emission, before transitioning to the blackbody-dominated soft […]


Continue.. Variability as a predictor for the hard-to-soft state transition in GX 339-4

An end-to-end calibration of the Mini-EUSO detector in space

Kavli Affiliate: Angela Olinto | First 5 Authors: Hiroko Miyamoto, Matteo Battisti, Dario Barghini, Alexander Belov, Mario Bertaina | Summary: Mini-EUSO is a wide Field-of-View (FoV, 44$^{circ}$) telescope currently in operation from a nadia-facing UV-transparent window in the Russian Zvezda module on the International Space Station (ISS). It is the first detector of the JEM-EUSO […]


Continue.. An end-to-end calibration of the Mini-EUSO detector in space

On the Impact of Cross-Domain Data on German Language Models

Kavli Affiliate: Cheng Peng | First 5 Authors: Amin Dada, Aokun Chen, Cheng Peng, Kaleb E Smith, Ahmad Idrissi-Yaghir | Summary: Traditionally, large language models have been either trained on general web crawls or domain-specific data. However, recent successes of generative large language models, have shed light on the benefits of cross-domain datasets. To examine […]


Continue.. On the Impact of Cross-Domain Data on German Language Models

On the Impact of Cross-Domain Data on German Language Models

Kavli Affiliate: Cheng Peng | First 5 Authors: Amin Dada, Aokun Chen, Cheng Peng, Kaleb E Smith, Ahmad Idrissi-Yaghir | Summary: Traditionally, large language models have been either trained on general web crawls or domain-specific data. However, recent successes of generative large language models, have shed light on the benefits of cross-domain datasets. To examine […]


Continue.. On the Impact of Cross-Domain Data on German Language Models