Tootfinder

Constraints on the progenitor models of fast radio bursts from population synthesis with the first CHIME/FRB catalog
Min Meng, Can-Min Deng
https://arxiv.org/abs/2506.04986

Constraints on the progenitor models of fast radio bursts from population synthesis with the first CHIME/FRB catalog
Fast radio bursts (FRBs) are enigmatic extragalactic radio transients with unknown origins. We performed comprehensive Monte Carlo simulations based on the first CHIME/FRB catalog to test whether the FRB population tracks the cosmic star formation history directly or requires a delay. By fully considering CHIME's complex selection effects and beam response, we find that the hypothesis that the FRB population tracks the SFH is not ruled out by the current data, although a small delay is preferre…

This https://arxiv.org/abs/2504.14515 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A robust mixed-effects quantile regression model using generalized Laplace mixtures to handle outliers and skewness
Mixed-effects quantile regression models are widely used to capture heterogeneous responses in hierarchically structured data. The asymmetric Laplace (AL) distribution has traditionally served as the basis for quantile regression; however, its fixed skewness limits flexibility and renders it sensitive to outliers. In contrast, the generalized asymmetric Laplace (GAL) distribution enables more flexible modeling of skewness and heavy-tailed behavior, yet it remains vulnerable to extreme observati…

Exploring selection biases in FRB dispersion-galaxy cross-correlations with magnetohydrodynamical simulations
April Qiu Cheng, Shion Elizabeth Andrew, Haochen Wang, Kiyoshi W. Masui
https://arxiv.org/abs/2506.03258

Exploring selection biases in FRB dispersion-galaxy cross-correlations with magnetohydrodynamical simulations
The dispersion of fast radio bursts (FRBs) in conjunction with their redshifts can be used as powerful probes of the distribution of extragalactic plasma, and with a large enough sample, the free-electron--galaxy power spectrum $P_{eg}$ can be measured by cross-correlating FRB dispersions with galaxy positions. However, a precise measurement of $P_{eg}$ requires a careful investigation of the selection effects -- the fact that the probability of both observing the FRB dispersion measure and obt…

fftvis: A Non-Uniform Fast Fourier Transform Based Interferometric Visibility Simulator
Tyler A. Cox, Steven G. Murray, Aaron R. Parsons, Joshua S. Dillon, Kartik Mandar, Zachary E. Martinot, Robert Pascua, Piyanat Kittiwisit, James E. Aguirre
#toXiv_bot_toot

Molecular dynamics simulation of the effects of neutron irradiation on Caesium Lead Bromide
Zhongming Zhang, Samuel Murphy, Michael Aspinall
https://arxiv.org/abs/2506.00675

Molecular dynamics simulation of the effects of neutron irradiation on Caesium Lead Bromide
With the development of fast neutron reactors and nuclear fusion reactors, it is necessary to find new radiation-hardened high-flux core neutron detectors. The use of perovskite Caesium Lead Bromide (CsPbBr$_3$) for neutron radiation detection is a new research direction. However, at high temperatures, the effects of neutron radiation, especially the Primary Knock-out Atom (PKA) and Displacement Per Atom (DPA), and the defect distribution at the molecular level have not been reported. This stud…

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Fangyu Lei, Jinxiang Meng, Yiming Huang, Tinghong Chen, Yun Zhang, Shizhu He, Jun Zhao, Kang Liu
https://arxiv.org/abs/2506.01710

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Table reasoning, encompassing tasks such as table question answering, fact verification, and text-to-SQL, requires precise understanding of structured tabular data, coupled with numerical computation and code manipulation for effective inference. Supervised fine-tuning (SFT) approaches have achieved notable success but often struggle with generalization and robustness due to biases inherent in imitative learning. We introduce Reasoning-Table, the first application of reinforcement learning (RL)…

Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Lingfan Bao, Yan Pan, Tianhu Peng, Chengxu Zhou
https://arxiv.org/abs/2506.01563

Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Effective human-robot interaction requires robots to identify human intentions and generate expressive, socially appropriate motions in real-time. Existing approaches often rely on fixed motion libraries or computationally expensive generative models. We propose a hierarchical framework that combines intention-aware reasoning via in-context learning (ICL) with real-time motion generation using diffusion models. Our system introduces structured prompting with confidence scoring, fallback behavio…

Flexible Mixed Precision Quantization for Learned Image Compression
Md Adnan Faisal Hossain, Zhihao Duan, Fengqing Zhu
https://arxiv.org/abs/2506.01221 htt…

Flexible Mixed Precision Quantization for Learned Image Compression
Despite its improvements in coding performance compared to traditional codecs, Learned Image Compression (LIC) suffers from large computational costs for storage and deployment. Model quantization offers an effective solution to reduce the computational complexity of LIC models. However, most existing works perform fixed-precision quantization which suffers from sub-optimal utilization of resources due to the varying sensitivity to quantization of different layers of a neural network. In this p…

Quanta Magazine authors Janna Levin and Steven Strogatz strike up a conversation with Ellie Pavlick (Research Scientist at Google Deep Mind) about the differences and similarities between the way people understand language, what NLP algorithms do, and the fact that such conversations more often than not shed light into more than Linguistics' computational side.
"Will AI Ever Understand Language Like Humans?"

Will AI Ever Understand Language Like Humans? | Quanta Magazine
AI may sound like a human, but that doesn’t mean that AI learns like a human. In this episode, Ellie Pavlick explains why understanding how LLMs can process language could unlock deeper insights into both AI and the human mind.