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pro vyhledávání: '"Wu, John A."'
Medical coding, the translation of unstructured clinical text into standardized medical codes, is a crucial but time-consuming healthcare practice. Though large language models (LLM) could automate the coding process and improve the efficiency of suc
Externí odkaz:
http://arxiv.org/abs/2411.00173
Autor:
Shabram, Megan, McClelland, Ryan, Wu, John, Venkataram, Hamsa Shwetha, Segars, Heidi, Dean, Bruce, Ye, Christine, Moin, Aquib, Ansdell, Megan, Moussa, Mark, Rebbapragada, Umaa, Valizadegan, Hamed, Perini, Dominick, Ko, Glenn, Da Poian, Victoria, Gharib-Nezhad, Sam, Cataldo, Giuseppe
Here we present several use cases for using Generative AI (Gen AI) to improve systems engineering and cognitive knowledge management related to the future of astronomy from a culmination of working meetings and presentations as part of the Gen AI Tas
Externí odkaz:
http://arxiv.org/abs/2410.16609
Predicting high-dimensional or extreme multilabels, such as in medical coding, requires both accuracy and interpretability. Existing works often rely on local interpretability methods, failing to provide comprehensive explanations of the overall mech
Externí odkaz:
http://arxiv.org/abs/2409.10504
Autor:
Iyer, Kartheik G., Yunus, Mikaeel, O'Neill, Charles, Ye, Christine, Hyk, Alina, McCormick, Kiera, Ciuca, Ioana, Wu, John F., Accomazzi, Alberto, Astarita, Simone, Chakrabarty, Rishabh, Cranney, Jesse, Field, Anjalie, Ghosal, Tirthankar, Ginolfi, Michele, Huertas-Company, Marc, Jablonska, Maja, Kruk, Sandor, Liu, Huiling, Marchidan, Gabriel, Mistry, Rohit, Naiman, J. P., Peek, J. E. G., Polimera, Mugdha, Rodriguez, Sergio J., Schawinski, Kevin, Sharma, Sanjib, Smith, Michael J., Ting, Yuan-Sen, Walmsley, Mike
The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable lite
Externí odkaz:
http://arxiv.org/abs/2408.01556
Sparse autoencoders (SAEs) have shown promise in extracting interpretable features from complex neural networks. We present one of the first applications of SAEs to dense text embeddings from large language models, demonstrating their effectiveness i
Externí odkaz:
http://arxiv.org/abs/2408.00657
Galaxies are theorized to form and co-evolve with their dark matter halos, such that their stellar masses and halo masses should be well-correlated. However, it is not known whether other observable galaxy features, such as their morphologies or larg
Externí odkaz:
http://arxiv.org/abs/2407.13735
Autor:
Tsujita, Akiyoshi, Kohno, Kotaro, Huang, Shuo, Oguri, Masamune, Tadaki, Ken-ichi, Smail, Ian, Umehata, Hideki, Gao, Zhen-Kai, Wang, Wei-Hao, Sun, Fengwu, Fujimoto, Seiji, Wang, Tao, Uematsu, Ryosuke, Espada, Daniel, Valentino, Francesco, Ao, Yiping, Bauer, Franz E., Hatsukade, Bunyo, Egusa, Fumi, Nishimura, Yuri, Koekemoer, Anton M., Schaerer, Daniel, Lagos, Claudia, Dessauges-Zavadsky, Miroslava, Brammer, Gabriel, Caputi, Karina, Egami, Eiichi, González-López, Jorge, Jolly, Jean-Baptiste, Knudsen, Kirsten K., Kokorev, Vasily, Magdis, Georgios E., Ouchi, Masami, Toft, Sune, Wu, John F., Zitrin, Adi
We present results from Atacama Large Millimeter/submillimeter Array (ALMA) spectral line-scan observations at 3-mm and 2-mm bands of three near-infrared-dark (NIR-dark) galaxies behind two massive lensing clusters MACS J0417.5-1154 and RXC J0032.1+1
Externí odkaz:
http://arxiv.org/abs/2406.09890
Autor:
Wu, John F., Hyk, Alina, McCormick, Kiera, Ye, Christine, Astarita, Simone, Baral, Elina, Ciuca, Jo, Cranney, Jesse, Field, Anjalie, Iyer, Kartheik, Koehn, Philipp, Kotler, Jenn, Kruk, Sandor, Ntampaka, Michelle, O'Neill, Charles, Peek, Joshua E. G., Sharma, Sanjib, Yunus, Mikaeel
Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communities like astronomy might benefit from them. However, there is currentl
Externí odkaz:
http://arxiv.org/abs/2405.20389
Autor:
Wang, Yunchong, Nadler, Ethan O., Mao, Yao-Yuan, Wechsler, Risa H., Abel, Tom, Behroozi, Peter, Geha, Marla, Asali, Yasmeen, Reyes, Mithi A. C. de los, Kado-Fong, Erin, Kallivayalil, Nitya, Tollerud, Erik J., Weiner, Benjamin, Wu, John F.
Environment plays a critical role in shaping the assembly of low-mass galaxies. Here, we use the UniverseMachine (UM) galaxy-halo connection framework and the Data Release 3 of the Satellites Around Galactic Analogs (SAGA) Survey to place dwarf galax
Externí odkaz:
http://arxiv.org/abs/2404.14500
Autor:
Geha, Marla, Mao, Yao-Yuan, Wechsler, Risa H., Asali, Yasmeen, Kado-Fong, Erin, Kallivayalil, Nitya, Nadler, Ethan O., Tollerud, Erik J., Weiner, Benjamin, Reyes, Mithi A. C. de los, Wang, Yunchong, Wu, John F.
We present the star-forming properties of 378 satellite galaxies around 101 Milky Way analogs in the Satellites Around Galactic Analogs (SAGA) Survey, focusing on the environmental processes that suppress or quench star formation. In the SAGA stellar
Externí odkaz:
http://arxiv.org/abs/2404.14499