The AGORA High-resolution Galaxy Simulations Comparison Project. VI. Similarities and Differences in the Circumgalactic Medium

Posted by klaurent Stanford

Kavli Affiliate: Tom Abel | First 5 Authors: Clayton Strawn, Santi Roca-Fàbrega, Joel R. Primack, Ji-hoon Kim, Anna Genina | Summary: We analyze the circumgalactic medium (CGM) for eight commonly-used cosmological codes in the AGORA collaboration. The codes are calibrated to use identical initial conditions, cosmology, heating and cooling, and star formation thresholds, but each […]


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On the evolution of the observed Mass-to-Length relationship for star-forming filaments

Posted by dbos Stanford

Kavli Affiliate: Susan E. Clark | First 5 Authors: Jiancheng Feng, Rowan J. Smith, Alvaro Hacar, Susan E. Clark, Daniel Seifried | Summary: The interstellar medium is threaded by a hierarchy of filaments from large scales (~ 100 pc) to small scales (~ 0.1pc). The masses and lengths of these nested structures may reveal important […]


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Opening the AI black box: program synthesis via mechanistic interpretability

Posted by klaurent MIT

Kavli Affiliate: Max Tegmark | First 5 Authors: Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide | Summary: We present MIPS, a novel method for program synthesis based on automated mechanistic interpretability of neural networks trained to perform the desired task, auto-distilling the learned algorithm into Python code. We test MIPS on […]


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Multiscale Modelling with Physics-informed Neural Network: from Large-scale Dynamics to Small-scale Predictions in Complex Systems

Posted by dbos Peking

Kavli Affiliate: Jing Wang | First 5 Authors: Jing Wang, Zheng Li, Pengyu Lai, Rui Wang, Di Yang | Summary: Multiscale phenomena manifest across various scientific domains, presenting a ubiquitous challenge in accurately and effectively predicting multiscale dynamics in complex systems. In this paper, a novel decoupling solving mode is proposed through modelling large-scale dynamics […]


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Insights into Multiscale Complexity: from Macroscopic Patterns to Microscopic Simulations via Deep Learning

Posted by dbos Peking

Kavli Affiliate: Jing Wang | First 5 Authors: Jing Wang, Zheng Li, Pengyu Lai, Rui Wang, Di Yang | Summary: Multiscale phenomena manifest across various scientific domains, presenting a ubiquitous challenge in accurately and effectively simulating multiscale dynamics in complex systems. In this paper, a novel decoupling solving mode is proposed through modelling large-scale dynamics […]


Continue.. Insights into Multiscale Complexity: from Macroscopic Patterns to Microscopic Simulations via Deep Learning

A Novel Paradigm in Solving Multiscale Problems

Posted by dbos Peking

Kavli Affiliate: Jing Wang | First 5 Authors: Jing Wang, Zheng Li, Pengyu Lai, Rui Wang, Di Yang | Summary: Multiscale phenomena manifest across various scientific domains, presenting a ubiquitous challenge in accurately and effectively simulating multiscale dynamics in complex systems. In this paper, a novel decoupling solving paradigm is proposed through modelling large-scale dynamics […]


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FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation Models

Posted by dbos Peking

Kavli Affiliate: Ke Wang | First 5 Authors: Chuhao Liu, Ke Wang, Jieqi Shi, Zhijian Qiao, Shaojie Shen | Summary: Semantic mapping based on the supervised object detectors is sensitive to image distribution. In real-world environments, the object detection and segmentation performance can lead to a major drop, preventing the use of semantic mapping in […]


Continue.. FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation Models

FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation Models

Posted by dbos Peking

Kavli Affiliate: Ke Wang | First 5 Authors: Chuhao Liu, Ke Wang, Jieqi Shi, Zhijian Qiao, Shaojie Shen | Summary: Semantic mapping based on the supervised object detectors is sensitive to image distribution. In real-world environments, the object detection and segmentation performance can lead to a major drop, preventing the use of semantic mapping in […]


Continue.. FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation Models

Gamma-ray Bursts as Distance Indicators by a Machine Learning Approach

Posted by dbos Stanford

Kavli Affiliate: Vahe Petrosian | First 5 Authors: Maria Giovanna Dainotti, Aditya Narendra, Agnieszka Pollo, Vahe Petrosian, Malgorzata Bogdan | Summary: Gamma-ray bursts (GRBs) can be probes of the early universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory GRBs have known redshifts ($z$) due to observational limitations. To address […]


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