Kavli Affiliate: Robert Simcoe | Summary:The population of the Little Red Dots (LRDs) may represent a key phase of supermassive black hole (SMBH) growth. A cocoon of dense excited gas is emerging as key component to explain the most striking properties of LRDs, such as strong Balmer breaks and Balmer absorption, as well as the […]
Kavli Affiliate: Robert A. Simcoe | First 5 Authors: Alberto Torralba, Alberto Torralba, , , | Summary: The population of the Little Red Dots (LRDs) may represent a key phase of supermassive black hole (SMBH) growth. A cocoon of dense excited gas is emerging as key component to explain the most striking properties of LRDs, […]
Kavli Affiliate: Claude Canizares | Summary:We present a multiwavelength analysis of the central stellar pair of $ρ$ Oph, components A and B. Using recent high-resolution textitChandra X-ray Observatory observations, we demonstrate with high confidence that the dominant X-ray source is $ρ$ Oph B, while $ρ$ Oph A is comparatively X-ray faint. This result contrasts with […]
Kavli Affiliate: David P. Huenemoerder | First 5 Authors: Sean J. Gunderson, Sean J. Gunderson, , , | Summary: We present a multiwavelength analysis of the central stellar pair of $rho$ Oph, components A and B. Using recent high-resolution textitChandra X-ray Observatory observations, we demonstrate with high confidence that the dominant X-ray source is $rho$ […]
Kavli Affiliate: Eliska Greplova | Summary:Much attention has been devoted to the use of machine learning to approximate physical concepts. Yet, due to challenges in interpretability of machine learning techniques, the question of what physics machine learning models are able to learn remains open. Here we bridge the concept a physical quantity and its machine […]
Kavli Affiliate: Eliska Greplova| First 5 Authors: D. O. Oriekhov, D. O. Oriekhov, , , | Summary:Much attention has been devoted to the use of machine learning to approximate physical concepts. Yet, due to challenges in interpretability of machine learning techniques, the question of what physics machine learning models are able to learn remains open. […]
Kavli Affiliate: Ting Xu | First 5 Authors: Chuanyang Zheng, Chuanyang Zheng, , , | Summary: Mixture-of-Experts (MoE) has become a cornerstone in recent state-of-the-art large language models (LLMs). Traditionally, MoE relies on $mathrmSoftmax$ as the router score function to aggregate expert output, a designed choice that has persisted from the earliest MoE models to […]
Kavli Affiliate: Ting Xu | First 5 Authors: Chuanyang Zheng, Chuanyang Zheng, , , | Summary: Mixture-of-Experts (MoE) has become a cornerstone in recent state-of-the-art large language models (LLMs). Traditionally, MoE relies on $mathrmSoftmax$ as the router score function to aggregate expert output, a designed choice that has persisted from the earliest MoE models to […]
Kavli Affiliate: Jing Wang | First 5 Authors: Jing Wang, Jing Wang, , , | Summary: Existing imitation learning methods decouple perception and action, which overlooks the causal reciprocity between sensory representations and action execution that humans naturally leverage for adaptive behaviors. To bridge this gap, we introduce Action-Guided Diffusion Policy (DP-AG), a unified representation […]
Kavli Affiliate: Jing Wang | First 5 Authors: Jing Wang, Jing Wang, , , | Summary: Existing imitation learning methods decouple perception and action, which overlooks the causal reciprocity between sensory representations and action execution that humans naturally leverage for adaptive behaviors. To bridge this gap, we introduce Action-Guided Diffusion Policy (DP-AG), a unified representation […]