SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). V. Confusion-limited Submillimeter Galaxy Number Counts at 450 $μ$m and Data Release for the COSMOS Field

Kavli Affiliate: Luis C. Ho | First 5 Authors: Zhen-Kai Gao, Chen-Fatt Lim, Wei-Hao Wang, Chian-Chou Chen, Ian Smail | Summary: We present confusion-limited SCUBA-2 450-$mu$m observations in the COSMOS-CANDELS region as part of the JCMT Large Program, SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). Our maps at 450 and 850 $mu$m cover an area […]


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Predicting Many Properties of Crystals by a Single Deep Learning Model

Kavli Affiliate: Jing Wang | First 5 Authors: Haosheng Xu, Dongheng Qian, Jing Wang, , | Summary: The use of machine learning methods for predicting the properties of crystalline materials encounters significant challenges, primarily related to input encoding, output versatility, and interpretability. Here, we introduce CrystalBERT, an adaptable transformer-based framework with novel structure that integrates […]


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Influence of mid-infrared Galactic bubble on surroundings: A case study on IRAS 16489-4431

Kavli Affiliate: Ke Wang | First 5 Authors: Ariful Hoque, Tapas Baug, Lokesh Dewangan, Ke Wang, Tie Liu | Summary: We studied the influence of a massive star on a mid-infrared bubble and its surrounding gas in the IRAS,16489-4431 star-forming region using multi-wavelength data. The {it Spitzer} mid-infrared band images revealed the shocked nature of […]


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GWnext 2024: Meeting Summary

Kavli Affiliate: Xian Chen | First 5 Authors: Alejandro Torres-Orjuela, Veronica Vazquez-Aceves, Rui Xu, Jin-Hong Chen, Andrea Derdzinski | Summary: GWnext 2024 was a meeting held in the Kavli Institute for Astronomy and Astrophysics at Peking University in March $4^text{th} – 8^text{th}$, 2024. In the meeting researchers at different career stages — with a particular […]


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GWnext 2024: Meeting Summary

Kavli Affiliate: Xian Chen | First 5 Authors: Alejandro Torres-Orjuela, Veronica Vazquez-Aceves, Rui Xu, Jin-Hong Chen, Andrea Derdzinski | Summary: GWnext 2024 was a meeting held in the Kavli Institute for Astronomy and Astrophysics at Peking University in March $4^text{th} – 8^text{th}$, 2024. In the meeting researchers at different career stages — with a particular […]


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Resistance Distribution of Decoherent Quantum Hall-Superconductor Edges

Kavli Affiliate: Jing Wang | First 5 Authors: Yichen Hu, Jing Wang, Biao Lian, , | Summary: We study the probability distribution of the resistance, or equivalently the charge transmission, of a decoherent quantum Hall-superconductor edge, with the decoherence coming from metallic puddles along the edge. Such metallic puddles may originate from magnetic vortex cores […]


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Resistance Distribution of Decoherent Quantum Hall-Superconductor Edges

Kavli Affiliate: Jing Wang | First 5 Authors: Yichen Hu, Jing Wang, Biao Lian, , | Summary: We study the probability distribution of the resistance, or equivalently the charge transmission, of a decoherent quantum Hall-superconductor edge, with the decoherence coming from metallic puddles along the edge. Such metallic puddles may originate from magnetic vortex cores […]


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Inaccurate Label Distribution Learning with Dependency Noise

Kavli Affiliate: Jing Wang | First 5 Authors: Zhiqiang Kou, Jing Wang, Yuheng Jia, Xin Geng, | Summary: In this paper, we introduce the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to tackle the challenges posed by noise in label distribution learning, which arise from dependencies on instances and labels. We start by modeling […]


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M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation

Kavli Affiliate: Zhuo Li | First 5 Authors: Mingshuang Luo, Ruibing Hou, Zhuo Li, Hong Chang, Zimo Liu | Summary: This paper presents M$^3$GPT, an advanced $textbf{M}$ultimodal, $textbf{M}$ultitask framework for $textbf{M}$otion comprehension and generation. M$^3$GPT operates on three fundamental principles. The first focuses on creating a unified representation space for various motion-relevant modalities. We employ […]


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