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pro vyhledávání: '"Leung Henry"'
Autor:
The Multimodal Universe Collaboration, Audenaert, Jeroen, Bowles, Micah, Boyd, Benjamin M., Chemaly, David, Cherinka, Brian, Ciucă, Ioana, Cranmer, Miles, Do, Aaron, Grayling, Matthew, Hayes, Erin E., Hehir, Tom, Ho, Shirley, Huertas-Company, Marc, Iyer, Kartheik G., Jablonska, Maja, Lanusse, Francois, Leung, Henry W., Mandel, Kaisey, Martínez-Galarza, Juan Rafael, Melchior, Peter, Meyer, Lucas, Parker, Liam H., Qu, Helen, Shen, Jeff, Smith, Michael J., Stone, Connor, Walmsley, Mike, Wu, John F.
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MULTIMODAL UNIVERSE contains hundreds of millions of astronomical observ
Externí odkaz:
http://arxiv.org/abs/2412.02527
Autor:
Leung, Henry
Bulk cone singularities are singularities in boundary two-point functions at points separated by a null geodesic in the bulk, but not in the boundary. In this work, we describe a new type of bulk cone singularities in a family of Vaidya-like spacetim
Externí odkaz:
http://arxiv.org/abs/2411.19924
Autor:
Erdem, H. Emre, Leung, Henry
Static noise maps depicting long-term noise levels over wide areas are valuable urban planning assets for municipalities in decreasing noise exposure of residents. However, non-traffic noise sources with transient behavior, which people complain freq
Externí odkaz:
http://arxiv.org/abs/2407.21204
Transformers are often the go-to architecture to build foundation models that ingest a large amount of training data. But these models do not estimate the probability density distribution when trained on regression problems, yet obtaining full probab
Externí odkaz:
http://arxiv.org/abs/2407.15703
Federated learning offers a compelling solution to the challenges of networking and data privacy within aerial and space networks by utilizing vast private edge data and computing capabilities accessible through drones, balloons, and satellites. Whil
Externí odkaz:
http://arxiv.org/abs/2406.17951
Federated learning (FL) provides a promising collaborative framework to build a model from distributed clients, and this work investigates the carbon emission of the FL process. Cloud and edge servers hosting FL clients may exhibit diverse carbon foo
Externí odkaz:
http://arxiv.org/abs/2404.15503
Publikováno v:
SciPost Phys. 16, 060 (2024)
Matter falling into a Schwarzschild-AdS black hole from the left causes increased focussing of ingoing geodesics from the right, and, as a consequence, they reach the singularity sooner. In a standard Penrose diagram, the singularity "bends down". We
Externí odkaz:
http://arxiv.org/abs/2310.03076
Autor:
Leung, Henry W., Bovy, Jo
Rapid strides are currently being made in the field of artificial intelligence using Transformer-based models like Large Language Models (LLMs). The potential of these methods for creating a single, large, versatile model in astronomy has not yet bee
Externí odkaz:
http://arxiv.org/abs/2308.10944
In the Milky Way, the distribution of stars in the $[\alpha/\mathrm{Fe}]$ vs. $[\mathrm{Fe/H}]$ and $[\mathrm{Fe/H}]$ vs. age planes holds essential information about the history of star formation, accretion, and dynamical evolution of the Galactic d
Externí odkaz:
http://arxiv.org/abs/2306.09319
Federated learning offers a promising approach under the constraints of networking and data privacy constraints in aerial and space networks (ASNs), utilizing large-scale private edge data from drones, balloons, and satellites. Existing research has
Externí odkaz:
http://arxiv.org/abs/2305.16351