Exploring the Dependence of Gas Cooling and Heating Functions on the Incident Radiation Field with Machine Learning

Kavli Affiliate: Nickolay Y. Gnedin | First 5 Authors: David Robinson, Camille Avestruz, Nickolay Y. Gnedin, , | Summary: Gas cooling and heating functions play a crucial role in galaxy formation. But, it is computationally expensive to exactly compute these functions in the presence of an incident radiation field. These computations can be greatly sped […]


Continue.. Exploring the Dependence of Gas Cooling and Heating Functions on the Incident Radiation Field with Machine Learning

Exploring the Dependence of Gas Cooling and Heating Functions on the Incident Radiation Field with Machine Learning

Kavli Affiliate: Nickolay Y. Gnedin | First 5 Authors: David Robinson, Camille Avestruz, Nickolay Y. Gnedin, , | Summary: Gas cooling and heating functions play a crucial role in galaxy formation. But, it is computationally expensive to exactly compute these functions in the presence of an incident radiation field. These computations can be greatly sped […]


Continue.. Exploring the Dependence of Gas Cooling and Heating Functions on the Incident Radiation Field with Machine Learning

Modelling Stochastic Star Formation History of Dwarf Galaxies in GRUMPY

Kavli Affiliate: Andrey Kravtsov | First 5 Authors: Yue Pan, Andrey Kravtsov, , , | Summary: We investigate the impact of bursty star formation on several galaxy scaling relations of dwarf galaxies using the $texttt{GRUMPY}$ galaxy formation model. While this model reproduces the star formation rate (SFR)-stellar mass, stellar mass-gas mass, and stellar mass-metallicity relations, […]


Continue.. Modelling Stochastic Star Formation History of Dwarf Galaxies in GRUMPY

Primordial non-Gaussianities with weak lensing: Information on non-linear scales in the Ulagam full-sky simulations

Kavli Affiliate: Chihway Chang | First 5 Authors: Dhayaa Anbajagane, Chihway Chang, Hayden Lee, Marco Gatti, | Summary: Primordial non-Gaussianities (PNGs) are signatures in the density field that encode particle physics processes from the inflationary epoch. Such signatures have been extensively studied using the Cosmic Microwave Background, through constraining the amplitudes, $f^{X}_{rm NL}$, with future […]


Continue.. Primordial non-Gaussianities with weak lensing: Information on non-linear scales in the Ulagam full-sky simulations

Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method

Kavli Affiliate: Wei Gao | First 5 Authors: Xuan Zhang, Wei Gao, , , | Summary: While large pre-trained language models (LLMs) have shown their impressive capabilities in various NLP tasks, they are still under-explored in the misinformation domain. In this paper, we examine LLMs with in-context learning (ICL) for news claim verification, and find […]


Continue.. Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method

Tackling the Unlimited Staleness in Federated Learning with Intertwined Data and Device Heterogeneities

Kavli Affiliate: Wei Gao | First 5 Authors: Haoming Wang, Wei Gao, , , | Summary: The efficiency of Federated Learning (FL) is often affected by both data and device heterogeneities. Data heterogeneity is defined as the heterogeneity of data distributions on different clients. Device heterogeneity is defined as the clients’ variant latencies in uploading […]


Continue.. Tackling the Unlimited Staleness in Federated Learning with Intertwined Data and Device Heterogeneities

Tackling the Unlimited Staleness in Federated Learning with Intertwined Data and Device Heterogeneities

Kavli Affiliate: Wei Gao | First 5 Authors: Haoming Wang, Wei Gao, , , | Summary: The efficiency of Federated Learning (FL) is often affected by both data and device heterogeneities. Data heterogeneity is defined as the heterogeneity of data distributions on different clients. Device heterogeneity is defined as the clients’ variant latencies in uploading […]


Continue.. Tackling the Unlimited Staleness in Federated Learning with Intertwined Data and Device Heterogeneities

Tackling the Unlimited Staleness in Federated Learning with Intertwined Data and Device Heterogeneities

Kavli Affiliate: Wei Gao | First 5 Authors: Haoming Wang, Wei Gao, , , | Summary: Federated Learning (FL) can be affected by data and device heterogeneities, caused by clients’ different local data distributions and latencies in uploading model updates (i.e., staleness). Traditional schemes consider these heterogeneities as two separate and independent aspects, but this […]


Continue.. Tackling the Unlimited Staleness in Federated Learning with Intertwined Data and Device Heterogeneities

Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness

Kavli Affiliate: Wei Gao | First 5 Authors: Haoming Wang, Wei Gao, , , | Summary: Federated Learning (FL) can be affected by data and device heterogeneities, caused by clients’ different local data distributions and latencies in uploading model updates (i.e., staleness). Traditional schemes consider these heterogeneities as two separate and independent aspects, but this […]


Continue.. Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness